From 9e757df39f2f77ff8ab46cd7898946df5e0eafa3 Mon Sep 17 00:00:00 2001
From: rmflight
Date: Wed, 30 Oct 2024 15:05:53 -0400
Subject: [PATCH] updated testing and passing checks
---
.Rbuildignore | 6 +-
DESCRIPTION | 4 +-
R/gsea.R | 4 +
docs/404.html | 2 +-
docs/LICENSE-text.html | 2 +-
docs/articles/command_line_interface.html | 6 +-
docs/articles/index.html | 2 +-
docs/articles/v2_guide.html | 4 +-
docs/articles/v2_visnetwork_guide.html | 4 +-
docs/authors.html | 2 +-
docs/index.html | 2 +-
docs/pkgdown.yml | 2 +-
docs/reference/add_data_to_graph.html | 2 +-
docs/reference/add_tooltip.html | 2 +-
docs/reference/annotation.html | 2 +-
docs/reference/annotation_2_json.html | 2 +-
docs/reference/annotation_combinations.html | 2 +-
docs/reference/annotation_gene_table.html | 2 +-
docs/reference/assign_colors.html | 2 +-
docs/reference/assign_communities.html | 2 +-
docs/reference/binomial_basic.html | 2 +-
.../binomial_feature_enrichment.html | 2 +-
docs/reference/binomial_features-class.html | 2 +-
docs/reference/binomial_result-class.html | 2 +-
docs/reference/categoryCompare2.html | 2 +-
docs/reference/cc_graph.html | 2 +-
.../combine_annotation_features.html | 2 +-
docs/reference/combine_annotations.html | 2 +-
docs/reference/combine_enrichments.html | 2 +-
docs/reference/combine_text.html | 2 +-
docs/reference/combined_coefficient.html | 2 +-
docs/reference/combined_enrichment.html | 2 +-
.../reference/combined_significant_calls.html | 2 +-
docs/reference/combined_statistics.html | 2 +-
docs/reference/csv_annotation_table.html | 2 +-
docs/reference/enriched_result.html | 2 +-
docs/reference/enriched_to_fgsea.html | 2 +-
docs/reference/executable_path.html | 2 +-
docs/reference/extract_enrich_stats.html | 2 +-
...statistics-combined_enrichment-method.html | 2 +-
docs/reference/extract_statistics.html | 2 +-
docs/reference/filter_annotation_graph.html | 2 +-
docs/reference/generate_annotation_graph.html | 2 +-
.../generate_annotation_similarity_graph.html | 2 +-
docs/reference/generate_colors.html | 2 +-
docs/reference/generate_legend.html | 2 +-
docs/reference/generate_link.html | 2 +-
docs/reference/generate_piecharts.html | 2 +-
docs/reference/generate_table.html | 2 +-
docs/reference/get_db_annotation.html | 2 +-
.../get_significant_annotations.html | 2 +-
.../get_significant_annotations_calls.html | 2 +-
docs/reference/gocats_to_annotation.html | 2 +-
docs/reference/graph_to_visnetwork.html | 2 +-
docs/reference/gsea_feature_enrichment.html | 6 +-
docs/reference/gsea_features-class.html | 2 +-
docs/reference/hypergeom_features-class.html | 2 +-
docs/reference/hypergeometric_basic.html | 2 +-
.../hypergeometric_feature_enrichment.html | 2 +-
docs/reference/index.html | 2 +-
docs/reference/install_executables.html | 2 +-
docs/reference/jaccard_coefficient.html | 2 +-
docs/reference/json_2_annotation.html | 2 +-
docs/reference/json_annotation_reversal.html | 2 +-
docs/reference/kable_annotation_table.html | 2 +-
docs/reference/label_communities.html | 2 +-
docs/reference/multi_query_list.html | 2 +-
docs/reference/node_assign-class.html | 2 +-
docs/reference/overlap_coefficient.html | 2 +-
docs/reference/remove_edges.html | 2 +-
.../show-binomial_result-method.html | 2 +-
.../show-combined_statistics-method.html | 2 +-
.../show-enriched_result-method.html | 2 +-
docs/reference/show-node_assign-method.html | 2 +-
.../show-significant_annotations-method.html | 2 +-
docs/reference/significant_annotations.html | 2 +-
docs/reference/statistical_results-class.html | 2 +-
docs/reference/table_from_graph.html | 2 +-
docs/reference/vis_in_cytoscape.html | 2 +-
docs/reference/vis_visnetwork.html | 2 +-
docs/search.json | 2 +-
man/gsea_feature_enrichment.Rd | 2 +
test_data_gen.R | 99 -------------------
test_data_gen2.R | 76 --------------
tests.R | 78 ---------------
tests/testthat/test-fgsea.R | 2 +-
86 files changed, 96 insertions(+), 343 deletions(-)
delete mode 100644 test_data_gen.R
delete mode 100644 test_data_gen2.R
delete mode 100644 tests.R
diff --git a/.Rbuildignore b/.Rbuildignore
index e970224..01eb7ca 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -4,11 +4,6 @@
^\.Rproj\.user$
^\.travis\.yml$
^\.push_gh_pages\.sh$
-tests.R
-test_data_gen.R
-test_data.RData
-test_table_data.rds
-test_combine_filter.rda
^\.github\workflows\.yml$
^\.github$
^executable_related$
@@ -27,3 +22,4 @@ test_combine_filter.rda
^doc$
^docs$
^_pkgdown\.yml$
+^examples_test_data$
\ No newline at end of file
diff --git a/DESCRIPTION b/DESCRIPTION
index 430f65c..5c8046f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
Package: categoryCompare2
-Version: 0.100.25
+Version: 0.100.26
Title: Meta-Analysis of High-Throughput Experiments Using Feature
Annotations
Authors@R: c(
@@ -18,7 +18,7 @@ Depends: R (>= 3.5.0)
Suggests: knitr, markdown, estrogen, org.Hs.eg.db, hgu95av2.db, limma,
affy, genefilter, testthat (>= 3.0.0), visNetwork, Cairo,
DiagrammeR, KEGGREST, docopt (>= 0.7.0), RCy3, hgu95av2cdf,
- rmarkdown, fgsea
+ rmarkdown, fgsea, data.table, withr
Imports: Biobase, methods, AnnotationDbi, colorspace, graph, igraph,
jsonlite, base64enc, dplyr, purrr, rlang, GO.db, stats
LazyLoad: yes
diff --git a/R/gsea.R b/R/gsea.R
index 7f437dc..9c7c01b 100644
--- a/R/gsea.R
+++ b/R/gsea.R
@@ -18,6 +18,7 @@ setClass("gsea_features",
#' @param min_features the minimum number of features for an annotation (default = 15)
#' @param max_features the maximum number of features for an annotation (default = 500)
#' @param return_type what type of object should be returned? ("cc2" or "fgsea")
+#' @param ... other `fgsea` options
#'
#' @details The runtime is dependent on the maximum size of the provided annotation,
#' so the authors of `fgsea` recommend a maximum size of 500. In addition, to calculate
@@ -82,6 +83,9 @@ gsea_feature_enrichment = function(gsea_features,
#' @return data.table
enriched_to_fgsea = function(in_enriched)
{
+ if (!requireNamespace("data.table", quietly = TRUE)) {
+ stop("data.table must be installed!")
+ }
ranks = in_enriched@ranks
results = data.table::as.data.table(in_enriched@statistics@statistic_data)
rename_list = c(pval = "p", padj = "padjust", log2err = "log2err", ES = "es", NES = "nes", size = "size", leadingEdge = "leading_edge")
diff --git a/docs/404.html b/docs/404.html
index 140898b..6ab9528 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -20,7 +20,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
Introduction
diff --git a/docs/authors.html b/docs/authors.html
index 7ade5e1..c96a106 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -7,7 +7,7 @@
categoryCompare2
-
0.100.25
+
0.100.26
diff --git a/docs/index.html b/docs/index.html
index bd17b0f..f02c4c3 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -22,7 +22,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 31c6d24..29e0a1f 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
command_line_interface: command_line_interface.html
v2_guide: v2_guide.html
v2_visnetwork_guide: v2_visnetwork_guide.html
-last_built: 2024-10-30T17:56Z
+last_built: 2024-10-30T18:58Z
urls:
reference: https://moseleybioinformaticslab.github.io/categoryCompare2/reference
article: https://moseleybioinformaticslab.github.io/categoryCompare2/articles
diff --git a/docs/reference/add_data_to_graph.html b/docs/reference/add_data_to_graph.html
index f066a27..e032232 100644
--- a/docs/reference/add_data_to_graph.html
+++ b/docs/reference/add_data_to_graph.html
@@ -11,7 +11,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/add_tooltip.html b/docs/reference/add_tooltip.html
index 56fe750..95d1b1b 100644
--- a/docs/reference/add_tooltip.html
+++ b/docs/reference/add_tooltip.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/annotation.html b/docs/reference/annotation.html
index 47f8dff..15aa447 100644
--- a/docs/reference/annotation.html
+++ b/docs/reference/annotation.html
@@ -11,7 +11,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/annotation_2_json.html b/docs/reference/annotation_2_json.html
index 4626a4f..880fe3a 100644
--- a/docs/reference/annotation_2_json.html
+++ b/docs/reference/annotation_2_json.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/annotation_combinations.html b/docs/reference/annotation_combinations.html
index cfc2fb1..a05a197 100644
--- a/docs/reference/annotation_combinations.html
+++ b/docs/reference/annotation_combinations.html
@@ -17,7 +17,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/annotation_gene_table.html b/docs/reference/annotation_gene_table.html
index 003b182..f82b165 100644
--- a/docs/reference/annotation_gene_table.html
+++ b/docs/reference/annotation_gene_table.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/assign_colors.html b/docs/reference/assign_colors.html
index 783e269..cddf5af 100644
--- a/docs/reference/assign_colors.html
+++ b/docs/reference/assign_colors.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/assign_communities.html b/docs/reference/assign_communities.html
index 5ff709b..8a2fbbf 100644
--- a/docs/reference/assign_communities.html
+++ b/docs/reference/assign_communities.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/binomial_basic.html b/docs/reference/binomial_basic.html
index 92f755a..3cf2d6a 100644
--- a/docs/reference/binomial_basic.html
+++ b/docs/reference/binomial_basic.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/binomial_feature_enrichment.html b/docs/reference/binomial_feature_enrichment.html
index 1cc7d9e..d2fe688 100644
--- a/docs/reference/binomial_feature_enrichment.html
+++ b/docs/reference/binomial_feature_enrichment.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/binomial_features-class.html b/docs/reference/binomial_features-class.html
index a1a4059..7cb418b 100644
--- a/docs/reference/binomial_features-class.html
+++ b/docs/reference/binomial_features-class.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/binomial_result-class.html b/docs/reference/binomial_result-class.html
index 757948b..030de1e 100644
--- a/docs/reference/binomial_result-class.html
+++ b/docs/reference/binomial_result-class.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/categoryCompare2.html b/docs/reference/categoryCompare2.html
index 47c3111..4a083e4 100644
--- a/docs/reference/categoryCompare2.html
+++ b/docs/reference/categoryCompare2.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/cc_graph.html b/docs/reference/cc_graph.html
index ea90e40..2ffbb4d 100644
--- a/docs/reference/cc_graph.html
+++ b/docs/reference/cc_graph.html
@@ -17,7 +17,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combine_annotation_features.html b/docs/reference/combine_annotation_features.html
index 5aebb25..c8d5784 100644
--- a/docs/reference/combine_annotation_features.html
+++ b/docs/reference/combine_annotation_features.html
@@ -11,7 +11,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combine_annotations.html b/docs/reference/combine_annotations.html
index 7bce8e7..e0b45ba 100644
--- a/docs/reference/combine_annotations.html
+++ b/docs/reference/combine_annotations.html
@@ -11,7 +11,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combine_enrichments.html b/docs/reference/combine_enrichments.html
index 116316b..667d522 100644
--- a/docs/reference/combine_enrichments.html
+++ b/docs/reference/combine_enrichments.html
@@ -15,7 +15,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combine_text.html b/docs/reference/combine_text.html
index d108ea8..ad3d03e 100644
--- a/docs/reference/combine_text.html
+++ b/docs/reference/combine_text.html
@@ -13,7 +13,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combined_coefficient.html b/docs/reference/combined_coefficient.html
index 102ad9f..47f9397 100644
--- a/docs/reference/combined_coefficient.html
+++ b/docs/reference/combined_coefficient.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combined_enrichment.html b/docs/reference/combined_enrichment.html
index f1225b3..34bf686 100644
--- a/docs/reference/combined_enrichment.html
+++ b/docs/reference/combined_enrichment.html
@@ -13,7 +13,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combined_significant_calls.html b/docs/reference/combined_significant_calls.html
index afe61eb..ccc13ff 100644
--- a/docs/reference/combined_significant_calls.html
+++ b/docs/reference/combined_significant_calls.html
@@ -11,7 +11,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/combined_statistics.html b/docs/reference/combined_statistics.html
index ab38043..58910d5 100644
--- a/docs/reference/combined_statistics.html
+++ b/docs/reference/combined_statistics.html
@@ -17,7 +17,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/csv_annotation_table.html b/docs/reference/csv_annotation_table.html
index 707a7dc..6e355c5 100644
--- a/docs/reference/csv_annotation_table.html
+++ b/docs/reference/csv_annotation_table.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/enriched_result.html b/docs/reference/enriched_result.html
index dad3430..d22c0ce 100644
--- a/docs/reference/enriched_result.html
+++ b/docs/reference/enriched_result.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/enriched_to_fgsea.html b/docs/reference/enriched_to_fgsea.html
index 3019a79..56c650f 100644
--- a/docs/reference/enriched_to_fgsea.html
+++ b/docs/reference/enriched_to_fgsea.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/executable_path.html b/docs/reference/executable_path.html
index 7de38d9..554be79 100644
--- a/docs/reference/executable_path.html
+++ b/docs/reference/executable_path.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/extract_enrich_stats.html b/docs/reference/extract_enrich_stats.html
index 34834bc..73cdb07 100644
--- a/docs/reference/extract_enrich_stats.html
+++ b/docs/reference/extract_enrich_stats.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/extract_statistics-combined_enrichment-method.html b/docs/reference/extract_statistics-combined_enrichment-method.html
index 721e9ec..549cc18 100644
--- a/docs/reference/extract_statistics-combined_enrichment-method.html
+++ b/docs/reference/extract_statistics-combined_enrichment-method.html
@@ -13,7 +13,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/extract_statistics.html b/docs/reference/extract_statistics.html
index 908eb2b..b6bc3c0 100644
--- a/docs/reference/extract_statistics.html
+++ b/docs/reference/extract_statistics.html
@@ -11,7 +11,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/filter_annotation_graph.html b/docs/reference/filter_annotation_graph.html
index 7a1f980..707316a 100644
--- a/docs/reference/filter_annotation_graph.html
+++ b/docs/reference/filter_annotation_graph.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/generate_annotation_graph.html b/docs/reference/generate_annotation_graph.html
index 8664133..e328c32 100644
--- a/docs/reference/generate_annotation_graph.html
+++ b/docs/reference/generate_annotation_graph.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/generate_annotation_similarity_graph.html b/docs/reference/generate_annotation_similarity_graph.html
index 0cbe9ec..4747ade 100644
--- a/docs/reference/generate_annotation_similarity_graph.html
+++ b/docs/reference/generate_annotation_similarity_graph.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/generate_colors.html b/docs/reference/generate_colors.html
index db03195..6855438 100644
--- a/docs/reference/generate_colors.html
+++ b/docs/reference/generate_colors.html
@@ -15,7 +15,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/generate_legend.html b/docs/reference/generate_legend.html
index 53f5cbc..c82b6f8 100644
--- a/docs/reference/generate_legend.html
+++ b/docs/reference/generate_legend.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/generate_link.html b/docs/reference/generate_link.html
index 97b16fa..4876140 100644
--- a/docs/reference/generate_link.html
+++ b/docs/reference/generate_link.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/generate_piecharts.html b/docs/reference/generate_piecharts.html
index 84f90ef..ea3f64f 100644
--- a/docs/reference/generate_piecharts.html
+++ b/docs/reference/generate_piecharts.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/generate_table.html b/docs/reference/generate_table.html
index 56252a0..649ad07 100644
--- a/docs/reference/generate_table.html
+++ b/docs/reference/generate_table.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/get_db_annotation.html b/docs/reference/get_db_annotation.html
index e182c79..65e19ee 100644
--- a/docs/reference/get_db_annotation.html
+++ b/docs/reference/get_db_annotation.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/get_significant_annotations.html b/docs/reference/get_significant_annotations.html
index 9123c88..0ef6dc4 100644
--- a/docs/reference/get_significant_annotations.html
+++ b/docs/reference/get_significant_annotations.html
@@ -15,7 +15,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/get_significant_annotations_calls.html b/docs/reference/get_significant_annotations_calls.html
index 53c2695..6eca5c2 100644
--- a/docs/reference/get_significant_annotations_calls.html
+++ b/docs/reference/get_significant_annotations_calls.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/gocats_to_annotation.html b/docs/reference/gocats_to_annotation.html
index 3c98c81..08f6e8a 100644
--- a/docs/reference/gocats_to_annotation.html
+++ b/docs/reference/gocats_to_annotation.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/graph_to_visnetwork.html b/docs/reference/graph_to_visnetwork.html
index 857cedf..9fd223f 100644
--- a/docs/reference/graph_to_visnetwork.html
+++ b/docs/reference/graph_to_visnetwork.html
@@ -9,7 +9,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/gsea_feature_enrichment.html b/docs/reference/gsea_feature_enrichment.html
index fdf7d11..2a0b257 100644
--- a/docs/reference/gsea_feature_enrichment.html
+++ b/docs/reference/gsea_feature_enrichment.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
@@ -72,6 +72,10 @@ Argumentsreturn_type
what type of object should be returned? ("cc2" or "fgsea")
+
+...
+other `fgsea` options
+
Value
diff --git a/docs/reference/gsea_features-class.html b/docs/reference/gsea_features-class.html
index cd1dc9e..66a86b5 100644
--- a/docs/reference/gsea_features-class.html
+++ b/docs/reference/gsea_features-class.html
@@ -7,7 +7,7 @@
categoryCompare2
-
0.100.25
+
0.100.26
diff --git a/docs/reference/hypergeom_features-class.html b/docs/reference/hypergeom_features-class.html
index f43ac7a..b4cf7f3 100644
--- a/docs/reference/hypergeom_features-class.html
+++ b/docs/reference/hypergeom_features-class.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/hypergeometric_basic.html b/docs/reference/hypergeometric_basic.html
index f17fd19..18d4f90 100644
--- a/docs/reference/hypergeometric_basic.html
+++ b/docs/reference/hypergeometric_basic.html
@@ -7,7 +7,7 @@
categoryCompare2
- 0.100.25
+ 0.100.26
diff --git a/docs/reference/hypergeometric_feature_enrichment.html b/docs/reference/hypergeometric_feature_enrichment.html
index 28136b1..c107bd4 100644
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-[{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"categoryCompare2: Command Line Interface","text":"Assuming linux type system, can add path, set executable. ’m going set R, ’s access . show shell well. Assuming ’ve saved path shell variable, CLI_LOCATION, can add path, change executables executable. make scripts executable, can check can actually use . Now, create directory put input files results.","code":"remotes::install(\"moseleybioinformaticslab/categorycompare2\") cli_location = system.file(\"exec\", package = \"categoryCompare2\") cli_location #> [1] \"/tmp/RtmpGN9CpH/temp_libpath23d143b922fa/categoryCompare2/exec\" dir(cli_location) #> [1] \"categoryCompare2.R\" \"create_annotations.R\" \"feature_files_2_json.R\" #> [4] \"filter_and_group.R\" \"run_enrichment.R\" Sys.setenv(CLI_LOCATION = cli_location) old_path = Sys.getenv(\"PATH\") new_path = paste0(old_path, \":\", cli_location) Sys.setenv(PATH = new_path) export PATH=\"$PATH:$CLI_LOCATION\" chmod 0750 $CLI_LOCATION/*.R categoryCompare2.R --help"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"categoryCompare2: Command Line Interface","text":"categoryCompare2 CLI needs different pieces information work: Annotations features enrichment . example Gene Ontology terms gene products. set features measured. RNA-Seq, genes transcripts genome organism. One sets differentially expressed features (genes transcripts). small example, going use estrogen microarray dataset main vignette. ’ve found differential sets genes timepoint, 10 48 hours. set genes measured array universe_entrez.txt, 10 hour differential genes 10_entrez.txt, 48 hour differential genes 48_entrez.txt.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"running","dir":"Articles","previous_headings":"","what":"Running","title":"categoryCompare2: Command Line Interface","text":"knitr good running sequential bash shell commands, going go whole analysis via CLI , comments, discuss one , just without output.","code":"test_loc = system.file(\"extdata\", \"test_data\", package = \"categoryCompare2\") Sys.setenv(TESTLOC = test_loc) export CURR_DIR=$(pwd) export WORKING=$CURR_DIR/cc2_1234 mkdir -p $WORKING cd $WORKING cp $TESTLOC/10_entrez.txt . cp $TESTLOC/48_entrez.txt . cp $TESTLOC/universe_entrez.txt . # get the annotations from installed organism database create_annotations.R --orgdb=org.Hs.eg.db --feature-type=ENTREZID \\ --annotation-type=GO --json=example_annotations.json # setup the gene lists feature_files_2_json.R --file1=10_entrez.txt --file2=48_entrez.txt \\ --universe=universe_entrez.txt --json=example_features.json # do the enrichments run_enrichment.R --features=example_features.json \\ --annotations=example_annotations.json --output-file=example_enrichment.txt # filter and find communities of related GO terms by shared feature annotations filter_and_group.R --enrichment-results=example_enrichment.txt \\ --p-cutoff=0.01 --count-cutoff=2 --similarity-file=example_similarity.rds --similarity-cutoff=0.8 \\ --table-file=example_grouping.txt # cleanup rm -rf $WORKING #>
[H
[2J
[3JLoading required namespace: GO.db #> #>
[H
[2J
[3J
[H
[2J
[3J[1] \"example_enrichment.txt\" #>
[H
[2J
[3JSignificant Annotations: #> Signficance Cutoffs: #> counts >= 2 #> padjust <= 0.01 #> #> Counts: #> 10_entrez 48_entrez counts #> G1 1 1 130 #> G2 1 0 152 #> G3 0 1 42 #> G4 0 0 20110 #> Saving annotation similarities in: example_similarity.rds #> Removed 17683 edges from graph"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"annotations","dir":"Articles","previous_headings":"","what":"Annotations","title":"categoryCompare2: Command Line Interface","text":"OK, let’s break little . Annotations information features. example, gene products, Gene Ontology terms, describe pathways products involved (Biological Process), chemical transformations binding properties gene products might (Molecular Function), cell biological structure might (Cellular Component). common gene product annotations also biological pathway membership like Kyoto Encyclopedia Genes Genomes (KEGG), pathways Reactome. Similarly, chemical compounds might annotated pathways KEGG Reactome. One common feature annotations Gene Ontology (GO) terms. Bioconductor includes GO terms organism databases (org-db), org.Hs.eg.db, organism database Homo sapiens, indexed Entrez Gene (eg). categoryCompare2 includes CLI utility getting GO terms included org-db form can used rest CLI, create_annotations.R. arguments : –orgdb: org-db use –feature-type: type feature IDs used map GO terms –annotation-type: annotations pull –json: output json stored Alternatively, source annotations, can pass directly using: example, Moseley Bioinformatics Lab python project, gocats, enables fuller consideration term-term relationships GO ontology structure. One gocats sub-commands outputs json structured file gene term mappings can used input create_annotations.R, gocats remap_goterms.","code":"create_annotations.R --orgdb=org.Hs.eg.db --feature-type=ENTREZID \\ --annotation-type=GO --json=example_annotations.json create_annotations.R --input=annotations.json --feature-type=ENTREZID \\ --annotation-type=GO --json=example_annotations.json"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"features","dir":"Articles","previous_headings":"","what":"Features","title":"categoryCompare2: Command Line Interface","text":"Features things ’ve measured. example, genes. can also metabolites, chromosomal regions, etc. annotation / category enrichment, need know features measured (universe), features interested . Part utility categoryCompare2 providing ability compare enrichments multiple lists. case, can combine four feature lists using CLI. need combine four feature lists, might want look R API directly, write code create JSON file directly. name group features taken file name. inputs : –file1: set features (required) –file2: another set features (optional) –file3: features (optional) –file4: features (optional) –universe: features measured –json: output file","code":"feature_files_2_json.R --file1=10_entrez.txt --file2=48_entrez.txt \\ --universe=universe_entrez.txt --json=example_features.json"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"running-enrichments","dir":"Articles","previous_headings":"","what":"Running Enrichments","title":"categoryCompare2: Command Line Interface","text":"runs hypergeometric enrichment feature lists output json file creating features . least, features, annotations, output. full list options includes: –config: YAML configuration file –default-config: display default configuration file –features: JSON file containing features (genes) [default: features.json] –annotations: annotations use, file [default: annotations.json] –enrichment-test: type test [default: hypergeometric] –enrichment-direction: want - -enrichment [default: ] –p-adjustment: kind p-value correction perform [default: BH] –output-file: save results [default: cc2_results.txt] –text-: text file generated? [default: FALSE]","code":"run_enrichment.R --features=example_features.json \\ --annotations=example_annotations.json --output-file=example_enrichment.txt"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"text-only-false","dir":"Articles","previous_headings":"Running Enrichments","what":"Text Only FALSE","title":"categoryCompare2: Command Line Interface","text":"’s important, want anything CLI results, keep --text-=FALSE. next step CLI uses rds file generated default enrichment results. use --text-=TRUE, Filter Group Annotations. Depending analysis want , needs, may fine. Just aware.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"filter-and-group-annotations","dir":"Articles","previous_headings":"","what":"Filter and Group Annotations","title":"categoryCompare2: Command Line Interface","text":"Finally, running enrichments, filter think significant, alternatively group GO terms similarity look communities GO terms. Although lists *.txt file input, actually looking matching rds file use, information necessary filtering grouping (see ). Let’s go options: –enrichment-results: enrichment results saved [default: cc2_results.txt] –p-cutoff: maximum p-value consider significant [default: 0.01] –adjusted-p-values: adjusted p-values used exist? [default: TRUE] –count-cutoff: minimum number significant features annotated annotation considered [default: 2] –similarity-file: grouping annotations attempted saved [default: annotation_similarity.rds] –similarity-cutoff: minimum similarity measure consider annotations linked [default: 0] –grouping-algorithm: algorithm used find groups [default: walktrap] –table-file: results file save results [default: cc2_results_grouped.txt] –network-file: desired, save network well [default: NULL] (currently implemented) grouping, definitely want adjust similarity-cutoff something higher, generally, make smaller groups terms. experience, ’ve found 0.8 good value use Gene Ontology terms. types annotations, may want use higher lower similarity cutoff. Unfortunately, outputs get depend values p-cutoff, count-cutoff, similarity-cutoff used. However, can iterate fairly rapidly enrichment already done. addition, annotation similarity network actually saved R rds file, long filename used, instead recalculating annotation similarities, loaded --similarity-file. also speeds computations considerably.","code":"filter_and_group.R --enrichment-results=example_enrichment.txt \\ --p-cutoff=0.01 --count-cutoff=2 --similarity-file=example_similarity.rds \\ --similarity-cutoff=0.8 --table-file=example_grouping.txt"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"Current high-throughput molecular biology experiments generating larger larger amounts data. Although many different methods analyze individual experiments, methods allow comparison different data sets sorely lacking. important due number experiments carried biological systems may amenable either fusion comparison. current tools available focus finding genes experiments listed , can shown statistically significant gene listed results experiments. However, many tools consider similarities (just importantly, differences) experimental results categorical level. Categorical data includes gene annotation, Gene Ontologies, KEGG pathways, chromosome location, etc. categoryCompare developed allow comparison high-throughput experiments categorical level, explore results intuitive fashion.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"sample-data","dir":"Articles","previous_headings":"","what":"Sample Data","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"make concept concrete, examine data microarray data set estrogen available Bioconductor. data set contains 8 samples, 2 levels estrogen therapy (present vs absent), two time points (10 48 hours). pre-processed version data available package, commands used generate . Note: preprocessed one keeps top 100 genes, use results slightly different shown vignette. can see descriptions arrays. First, read cel files, normalize data using RMA. make easier conceptualize, split data two eSet objects time, perform manipulations calculating significantly differentially expressed genes eSet object. 10 hour samples: 48 hour samples thing: grab genes array background set. time points generated list genes differentially expressed present vs absent samples. compare time-points, find common discordant genes experiments, try interpret lists. commonly done many meta-analysis studies attempt combine results many different experiments. alternative approach, used categoryCompare, compare significantly enriched categories two gene lists. Currently package supports two category classes, Gene Ontology, KEGG pathways. used . Note 1: proposing best way analyse particular data, sample data set merely serves illustrate functionality package. However, many different experiments type approach definitely appropriate, user determine data fits analytical paradigm advocated .","code":"library(\"affy\") library(\"hgu95av2.db\") library(\"genefilter\") library(\"estrogen\") library(\"limma\") datadir <- system.file(\"extdata\", package = \"estrogen\") pd <- read.AnnotatedDataFrame(file.path(datadir,\"estrogen.txt\"), header = TRUE, sep = \"\", row.names = 1) pData(pd) ## estrogen time.h ## low10-1.cel absent 10 ## low10-2.cel absent 10 ## high10-1.cel present 10 ## high10-2.cel present 10 ## low48-1.cel absent 48 ## low48-2.cel absent 48 ## high48-1.cel present 48 ## high48-2.cel present 48 currDir <- getwd() setwd(datadir) a <- ReadAffy(filenames=rownames(pData(pd)), phenoData = pd, verbose = TRUE) ## 1 reading low10-1.cel ...instantiating an AffyBatch (intensity a 409600x8 matrix)...done. ## Reading in : low10-1.cel ## Reading in : low10-2.cel ## Reading in : high10-1.cel ## Reading in : high10-2.cel ## Reading in : low48-1.cel ## Reading in : low48-2.cel ## Reading in : high48-1.cel ## Reading in : high48-2.cel setwd(currDir) eData <- affy::rma(a) ## Warning: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when ## loading 'hgu95av2cdf' ## Warning: replacing previous import 'AnnotationDbi::head' by 'utils::head' when ## loading 'hgu95av2cdf' ## Background correcting ## Normalizing ## Calculating Expression e10 <- eData[, eData$time.h == 10] e10 <- nsFilter(e10, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\")$eset e10$estrogen <- factor(e10$estrogen) d10 <- model.matrix(~0 + e10$estrogen) colnames(d10) <- unique(e10$estrogen) fit10 <- lmFit(e10, d10) c10 <- makeContrasts(present - absent, levels=d10) fit10_2 <- contrasts.fit(fit10, c10) eB10 <- eBayes(fit10_2) table10 <- topTable(eB10, number=nrow(e10), p.value=1, adjust.method=\"BH\") table10$Entrez <- unlist(mget(rownames(table10), hgu95av2ENTREZID, ifnotfound=NA)) e48 <- eData[, eData$time.h == 48] e48 <- nsFilter(e48, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\" )$eset e48$estrogen <- factor(e48$estrogen) d48 <- model.matrix(~0 + e48$estrogen) colnames(d48) <- unique(e48$estrogen) fit48 <- lmFit(e48, d48) c48 <- makeContrasts(present - absent, levels=d48) fit48_2 <- contrasts.fit(fit48, c48) eB48 <- eBayes(fit48_2) table48 <- topTable(eB48, number=nrow(e48), p.value=1, adjust.method=\"BH\") table48$Entrez <- unlist(mget(rownames(table48), hgu95av2ENTREZID, ifnotfound=NA)) gUniverse <- unique(union(table10$Entrez, table48$Entrez))"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"create-gene-list","dir":"Articles","previous_headings":"","what":"Create Gene List","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"","code":"library(\"categoryCompare2\") library(\"GO.db\") library(\"org.Hs.eg.db\") g10 <- unique(table10$Entrez[table10$adj.P.Val < 0.05]) g48 <- unique(table48$Entrez[table48$adj.P.Val < 0.05])"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"create-go-annotation-object","dir":"Articles","previous_headings":"","what":"Create GO Annotation Object","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"can analysis, need define annotation object, maps annotations features (genes case). Gene Ontology (GO) based analysis, genes annotated particular GO term based inheritance GO DAG. can generate list using GOALL column org.Hs.eg.db, filter terms interest, use .","code":"go_all_gene <- AnnotationDbi::select(org.Hs.eg.db, keys = gUniverse, columns = c(\"GOALL\", \"ONTOLOGYALL\")) ## 'select()' returned 1:many mapping between keys and columns go_all_gene <- go_all_gene[go_all_gene$ONTOLOGYALL == \"BP\", ] bp_2_gene <- split(go_all_gene$ENTREZID, go_all_gene$GOALL) bp_2_gene <- lapply(bp_2_gene, unique) bp_desc <- AnnotationDbi::select(GO.db, keys = names(bp_2_gene), columns = \"TERM\", keytype = \"GOID\")$TERM ## 'select()' returned 1:1 mapping between keys and columns names(bp_desc) <- names(bp_2_gene) bp_annotation <- categoryCompare2::annotation(annotation_features = bp_2_gene, description = bp_desc, annotation_type = \"GO.BP\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"do-enrichment","dir":"Articles","previous_headings":"","what":"Do Enrichment","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"Now can hypergeometric enrichment gene lists.","code":"g10_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g10, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" ) g48_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g48, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"combine-and-find-significant","dir":"Articles","previous_headings":"","what":"Combine and Find Significant","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"","code":"bp_combined <- combine_enrichments(g10 = g10_enrich, g48 = g48_enrich) bp_sig <- get_significant_annotations(bp_combined, padjust <= 0.001, counts >= 2) bp_sig@statistics@significant ## Signficance Cutoffs: ## padjust <= 0.001 ## counts >= 2 ## ## Counts: ## g10 g48 counts ## G1 1 1 72 ## G2 1 0 53 ## G3 0 1 48 ## G4 0 0 14118"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"generate-graph","dir":"Articles","previous_headings":"","what":"Generate Graph","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"","code":"bp_graph <- generate_annotation_graph(bp_sig) bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 7740 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_graph <- remove_edges(bp_graph, 0.8) ## Removed 7530 edges from graph bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 210 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_assign <- annotation_combinations(bp_graph) bp_assign <- assign_colors(bp_assign)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"find-communities","dir":"Articles","previous_headings":"Generate Graph","what":"Find Communities","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"useful define annotations terms communities. run methods find label communities, generating visualization table.","code":"bp_communities <- assign_communities(bp_graph) bp_comm_labels <- label_communities(bp_communities, bp_annotation)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"cytoscape-visualization","dir":"Articles","previous_headings":"","what":"Cytoscape Visualization","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"can generate legend know colors correspond group.","code":"bp_vis <- vis_in_cytoscape(bp_graph, bp_assign, \"BP\") generate_legend(bp_assign)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"categorycompare2-alternative-visualization","dir":"Articles","previous_headings":"","what":"categoryCompare2: Alternative Visualization","title":"categoryCompare2: visNetwork","text":"Authored : Robert M Flight 2024-10-30 14:00:30.917508","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"introduction","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Introduction","title":"categoryCompare2: visNetwork","text":"Current high-throughput molecular biology experiments generating larger larger amounts data. Although many different methods analyze individual experiments, methods allow comparison different data sets sorely lacking. important due number experiments carried biological systems may amenable either fusion comparison. current tools available focus finding genes experiments listed , can shown statistically significant gene listed results experiments. However, many tools consider similarities (just importantly, differences) experimental results categorical level. Categoical data includes gene annotation, Gene Ontologies, KEGG pathways, chromosome location, etc. categoryCompare developed allow comparison high-throughput experiments categorical level, explore results intuitive fashion.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"sample-data","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Sample Data","title":"categoryCompare2: visNetwork","text":"make concept concrete, examine data microarray data set estrogen available Bioconductor. data set contains 8 samples, 2 levels estrogen therapy (present vs absent), two time points (10 48 hours). pre-processed version data available package, commands used generate . Note: preprocessed one keeps top 100 genes, use results slightly different shown vignette. can see descriptions arrays. First, read cel files, normalize data using RMA. make easier conceptualize, split data two eSet objects time, perform manipulations calculating significantly differentially expressed genes eSet object. 10 hour samples: 48 hour samples thing: grab genes array background set. time points generated list genes differentially expressed present vs absent samples. compare time-points, find common discordant genes experiments, try interpret lists. commonly done many meta-analysis studies attempt combine results many different experiments. alternative approach, used categoryCompare, compare significantly enriched categories two gene lists. Currently package supports two category classes, Gene Ontology, KEGG pathways. used . Note 1: proposing best way analyse particular data, sample data set merely serves illustrate functionality package. However, many different experiments type approach definitely appropriate, user determine data fits analytical paradigm advocated .","code":"library(\"affy\") library(\"hgu95av2.db\") library(\"genefilter\") library(\"estrogen\") library(\"limma\") datadir <- system.file(\"extdata\", package = \"estrogen\") pd <- read.AnnotatedDataFrame(file.path(datadir,\"estrogen.txt\"), header = TRUE, sep = \"\", row.names = 1) pData(pd) ## estrogen time.h ## low10-1.cel absent 10 ## low10-2.cel absent 10 ## high10-1.cel present 10 ## high10-2.cel present 10 ## low48-1.cel absent 48 ## low48-2.cel absent 48 ## high48-1.cel present 48 ## high48-2.cel present 48 currDir <- getwd() setwd(datadir) a <- ReadAffy(filenames=rownames(pData(pd)), phenoData = pd, verbose = TRUE) ## 1 reading low10-1.cel ...instantiating an AffyBatch (intensity a 409600x8 matrix)...done. ## Reading in : low10-1.cel ## Reading in : low10-2.cel ## Reading in : high10-1.cel ## Reading in : high10-2.cel ## Reading in : low48-1.cel ## Reading in : low48-2.cel ## Reading in : high48-1.cel ## Reading in : high48-2.cel setwd(currDir) eData <- rma(a) ## Warning: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when ## loading 'hgu95av2cdf' ## Warning: replacing previous import 'AnnotationDbi::head' by 'utils::head' when ## loading 'hgu95av2cdf' ## Background correcting ## Normalizing ## Calculating Expression e10 <- eData[, eData$time.h == 10] e10 <- nsFilter(e10, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\")$eset e10$estrogen <- factor(e10$estrogen) d10 <- model.matrix(~0 + e10$estrogen) colnames(d10) <- unique(e10$estrogen) fit10 <- lmFit(e10, d10) c10 <- makeContrasts(present - absent, levels=d10) fit10_2 <- contrasts.fit(fit10, c10) eB10 <- eBayes(fit10_2) table10 <- topTable(eB10, number=nrow(e10), p.value=1, adjust.method=\"BH\") table10$Entrez <- unlist(mget(rownames(table10), hgu95av2ENTREZID, ifnotfound=NA)) e48 <- eData[, eData$time.h == 48] e48 <- nsFilter(e48, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\" )$eset e48$estrogen <- factor(e48$estrogen) d48 <- model.matrix(~0 + e48$estrogen) colnames(d48) <- unique(e48$estrogen) fit48 <- lmFit(e48, d48) c48 <- makeContrasts(present - absent, levels=d48) fit48_2 <- contrasts.fit(fit48, c48) eB48 <- eBayes(fit48_2) table48 <- topTable(eB48, number=nrow(e48), p.value=1, adjust.method=\"BH\") table48$Entrez <- unlist(mget(rownames(table48), hgu95av2ENTREZID, ifnotfound=NA)) gUniverse <- unique(union(table10$Entrez, table48$Entrez))"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"create-gene-list","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Create Gene List","title":"categoryCompare2: visNetwork","text":"","code":"library(\"categoryCompare2\") library(\"GO.db\") library(\"org.Hs.eg.db\") g10 <- unique(table10$Entrez[table10$adj.P.Val < 0.05]) g48 <- unique(table48$Entrez[table48$adj.P.Val < 0.05])"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"create-go-annotation-object","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Create GO Annotation Object","title":"categoryCompare2: visNetwork","text":"can analysis, need define annotation object, maps annotations features (genes case). Gene Ontology (GO) based analysis, genes annotated particular GO term based inheritance GO DAG. can generate list using GOALL column org.Hs.eg.db, filter terms interest, use .","code":"go_all_gene <- AnnotationDbi::select(org.Hs.eg.db, keys = gUniverse, columns = c(\"GOALL\", \"ONTOLOGYALL\")) ## 'select()' returned 1:many mapping between keys and columns go_all_gene <- go_all_gene[go_all_gene$ONTOLOGYALL == \"BP\", ] bp_2_gene <- split(go_all_gene$ENTREZID, go_all_gene$GOALL) bp_2_gene <- lapply(bp_2_gene, unique) bp_desc <- AnnotationDbi::select(GO.db, keys = names(bp_2_gene), columns = \"TERM\", keytype = \"GOID\")$TERM ## 'select()' returned 1:1 mapping between keys and columns names(bp_desc) <- names(bp_2_gene) bp_annotation <- categoryCompare2::annotation(annotation_features = bp_2_gene, description = bp_desc, annotation_type = \"GO.BP\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"do-enrichment","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Do Enrichment","title":"categoryCompare2: visNetwork","text":"Now can hypergeometric enrichment gene lists.","code":"g10_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g10, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" ) g48_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g48, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"combine-and-find-significant","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Combine and Find Significant","title":"categoryCompare2: visNetwork","text":"","code":"bp_combined <- combine_enrichments(g10 = g10_enrich, g48 = g48_enrich) bp_sig <- get_significant_annotations(bp_combined, padjust <= 0.001, counts >= 2) bp_sig@statistics@significant ## Signficance Cutoffs: ## padjust <= 0.001 ## counts >= 2 ## ## Counts: ## g10 g48 counts ## G1 1 1 72 ## G2 1 0 53 ## G3 0 1 48 ## G4 0 0 14118"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"generate-graph","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Generate Graph","title":"categoryCompare2: visNetwork","text":"","code":"bp_graph <- generate_annotation_graph(bp_sig) bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 7740 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_graph <- remove_edges(bp_graph, 0.8) ## Removed 7530 edges from graph bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 210 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_assign <- annotation_combinations(bp_graph) bp_assign <- assign_colors(bp_assign)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"visnetwork-visualization","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"visNetwork Visualization","title":"categoryCompare2: visNetwork","text":"can use DiagrammeR visNetwork html widgets create interactive visualizations either RStudio viewer, panes html report.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"find-communities","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization > visNetwork Visualization","what":"Find Communities","title":"categoryCompare2: visNetwork","text":"useful define annotations terms communities. run methods find label communities, generating visualization table.","code":"bp_communities <- assign_communities(bp_graph) bp_comm_labels <- label_communities(bp_communities, bp_annotation)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"create-stats-table","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization > visNetwork Visualization","what":"Create Stats Table","title":"categoryCompare2: visNetwork","text":"provide list GO terms communities found, lets generate table, community labels makes easier find graph desired.","code":"bp_table <- table_from_graph(bp_graph, bp_assign, bp_comm_labels) knitr::kable(bp_table)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"actually-visualize-it","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization > visNetwork Visualization","what":"Actually Visualize It!","title":"categoryCompare2: visNetwork","text":" show table generated first 3 GO terms. one run, find table.","code":"bp_network <- graph_to_visnetwork(bp_graph, bp_assign, bp_comm_labels) vis_visnetwork(bp_network) annotation_table <- annotation_gene_table(bp_combined, graph::nodes(bp_graph), use_db = org.Hs.eg.db) kable_annotation_table(annotation_table, header = 4) csv_annotation_table(annotation_table, out_file = \"bp_annotations.csv\")"},{"path":[]},{"path":[]},{"path":[]},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robert M Flight. Author, maintainer.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Flight RM, Harrison BJ, Mohammad F, Bunge MB, Moon LDF, Petruska JC, Rouchka EC (2014). “CATEGORYCOMPARE, analytical tool based feature annotations.” Frontiers Genetics. doi:10.3389/fgene.2014.00098, http://dx.doi.org/10.3389/fgene.2014.00098.","code":"@Article{, title = {CATEGORYCOMPARE, an analytical tool based on feature annotations}, author = {Robert M Flight and Benjamin J Harrison and Fahim Mohammad and Mary B Bunge and Lawrence D F Moon and Jeffrey C Petruska and Eric C Rouchka}, year = {2014}, url = {http://dx.doi.org/10.3389/fgene.2014.00098}, doi = {10.3389/fgene.2014.00098}, journal = {Frontiers in Genetics}, }"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"categorycompare2","dir":"","previous_headings":"","what":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Bioconductor package meta analysis high-throughput datasets using enriched feature annotations instead just features . Note rewrite categoryCompare package. information things changed , please see poster.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"api","dir":"","previous_headings":"","what":"API","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"version mostly complete works many full analyses. user facing API expected close fixed, especially core functionality. methods need actual S4 R6 based objects methods, expect function calls remain .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"to-do","dir":"","previous_headings":"","what":"To Do","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Things still needed include: Wrapper initial analysis given feature data annotations Example importing users annotations Integration GOCats Python library summarizing ontologies Better exploration features linked specific annotations, including original data associated features","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Vignette provides description thinking behind package well toy example demonstration purposes.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Installation package Github requires remotes package.","code":"install.packages(\"remotes\") library(remotes) install_github(\"MoseleyBioinformaticsLab/categoryCompare2\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"mac-installation","dir":"","previous_headings":"Installation","what":"Mac Installation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"one issue installation MacOS, {Cairo} package. actually due xquartz installed. easiest way can find install using homebrew. terminal, can : Hopefully worked fine, now able use functionality {categoryCompare2}.","code":"/bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\" brew install --cask xquartz R install.packages(\"Cairo\") # skip if already installed library(Cairo)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Flight RM, Harrison BJ, Mohammad F, Bunge MB, Moon LDF, Petruska JC Rouchka EC (2014). .CATEGORYCOMPARE, analytical tool based feature annotations. Frontiers Genetics. link","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"add table data to graph — add_data_to_graph","title":"add table data to graph — add_data_to_graph","text":"given annotation_graph data.frame, add data data.frame graph available elsewhere. Note NA integer numerics, value modified -100, infinite values, modified 1e100.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"add table data to graph — add_data_to_graph","text":"","code":"add_data_to_graph(graph, data)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"add table data to graph — add_data_to_graph","text":"graph graph work data data add ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"add table data to graph — add_data_to_graph","text":"graphNEL","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":null,"dir":"Reference","previous_headings":"","what":"add tooltip — add_tooltip","title":"add tooltip — add_tooltip","text":"passing Cytoscape, add tooltip attribute graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"add tooltip — add_tooltip","text":"","code":"add_tooltip( in_graph, node_data = c(\"name\", \"description\"), description, separator = \"\\n\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"add tooltip — add_tooltip","text":"in_graph graph work node_data pieces node data use description descriptive text use separator separator use tooltip","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"add tooltip — add_tooltip","text":"graph new nodeData member \"tooltip\"","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation class — annotation","title":"annotation class — annotation","text":"class holds annotation object defines annotations relate features, well various pieces annotation sensical checks creating annotation object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation class — annotation","text":"","code":"annotation( annotation_features, annotation_type = NULL, description = character(0), links = character(0), feature_type = NULL ) # S4 method for class 'annotation' show(object) annotation( annotation_features, annotation_type = NULL, description = character(0), links = character(0), feature_type = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation class — annotation","text":"annotation_features list annotation feature relationships annotation_type simple one word description annotations description character vector providing descriptive text annotation links character vector defining html links annotation (may empty) feature_type one word description feature type object annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"annotation class — annotation","text":"objects may created hand, may result specific functions create . notably, package provides functions creating Gene Ontology annotation. See annotation, slot parameter.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"annotation class — annotation","text":"annotation_features list annotation feature relationships description character vector providing descriptive text annotation counts numeric vector many features annotation links character vector defining html links annotation (may empty) annotation_type one word short description \"type\" annotation feature_type one word short description \"type\" features","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation to json — annotation_2_json","title":"annotation to json — annotation_2_json","text":"Given `categoryCompare2` annotation object, generate JSON representation can used command line executable","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation to json — annotation_2_json","text":"","code":"annotation_2_json(annotation_obj, json_file = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation to json — annotation_2_json","text":"annotation_obj annotation object json_file file save ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation to json — annotation_2_json","text":"json string (invisibly)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"unique annotation combinations — annotation_combinations","title":"unique annotation combinations — annotation_combinations","text":"determine unique combinations annotations exist significant matrix cc_graph assign node graph group. determine unique combinations annotations exist significant matrix combined_statistics assign annotation group.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"unique annotation combinations — annotation_combinations","text":"","code":"annotation_combinations(object) # S4 method for class 'cc_graph' annotation_combinations(object) # S4 method for class 'significant_annotations' annotation_combinations(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"unique annotation combinations — annotation_combinations","text":"object combined_statistics work ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"unique annotation combinations — annotation_combinations","text":"node_assignment node_assignment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation to genes — annotation_gene_table","title":"annotation to genes — annotation_gene_table","text":"Creates tabular output annotations genes providing lookup genes contributing particular annotation.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation to genes — annotation_gene_table","text":"","code":"annotation_gene_table( combined_enrichment, annotations = NULL, use_db = NULL, input_type = \"ENTREZID\", gene_info = c(\"SYMBOL\", \"GENENAME\") )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation to genes — annotation_gene_table","text":"combined_enrichment combined enrichment object annotations annotations grab features use_db annotation database input_type type gene id ? gene_info type info return gene","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation to genes — annotation_gene_table","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":null,"dir":"Reference","previous_headings":"","what":"assign colors — assign_colors","title":"assign colors — assign_colors","text":"given node_assign, assign colors either independent groups unique annotations, experiments independently.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"assign colors — assign_colors","text":"","code":"assign_colors(in_assign, type = \"experiment\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"assign colors — assign_colors","text":"in_assign node_assign object generated cc_graph type either \"group\" \"experiment\"","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"assign colors — assign_colors","text":"node_assign colors","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":null,"dir":"Reference","previous_headings":"","what":"assign communities — assign_communities","title":"assign communities — assign_communities","text":"given cc_graph, find communities nodes based connectivity weights.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"assign communities — assign_communities","text":"","code":"assign_communities(in_graph)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"assign communities — assign_communities","text":"in_graph cc_graph object use","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"assign communities — assign_communities","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":null,"dir":"Reference","previous_headings":"","what":"do binomial test — binomial_basic","title":"do binomial test — binomial_basic","text":"binomial test","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do binomial test — binomial_basic","text":"","code":"binomial_basic( positive_cases, total_cases, p_expected = 0.5, direction = \"two.sided\", conf_level = 0.95 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do binomial test — binomial_basic","text":"positive_cases number positive instances total_cases total number cases observed p_expected expected probability direction direction test conf_level confidence level confidence interval","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do binomial test — binomial_basic","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"do binomial testing — binomial_feature_enrichment","title":"do binomial testing — binomial_feature_enrichment","text":"binomial testing","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do binomial testing — binomial_feature_enrichment","text":"","code":"binomial_feature_enrichment( binomial_features, p_expected = 0.5, direction = \"two.sided\", p_adjust = \"BH\", conf_level = 0.95, min_features = 1 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do binomial testing — binomial_feature_enrichment","text":"binomial_features binomial_features object p_expected expected probability (default 0.5) direction direction enrichment (two.sided, less, greater) p_adjust correct p-values (default \"BH\") conf_level confidence level confidence interval (default 0.95) min_features minimum number features annotated annotation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do binomial testing — binomial_feature_enrichment","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_features-class.html","id":null,"dir":"Reference","previous_headings":"","what":"binomial feature class — binomial_features-class","title":"binomial feature class — binomial_features-class","text":"class hold features undergoing binomail statistical testing","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_features-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"binomial feature class — binomial_features-class","text":"positivefc features positive fold-changes negativefc features negative fold-changes annotation annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_result-class.html","id":null,"dir":"Reference","previous_headings":"","what":"the binomial results class — binomial_result-class","title":"the binomial results class — binomial_result-class","text":"binomial results class","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_result-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"the binomial results class — binomial_result-class","text":"positivefc positive log-fold-changed genes, vector class negativefc negative log-fold-changed genes annotation list giving annotation feature relationship statistics statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/categoryCompare2.html","id":null,"dir":"Reference","previous_headings":"","what":"categoryCompare2: A package for comparing enrichment results from multiple experiments — categoryCompare2","title":"categoryCompare2: A package for comparing enrichment results from multiple experiments — categoryCompare2","text":"categoryCompare2 package provides functions simple enrichment comparison enrichment results.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"cc_graph — cc_graph","title":"cc_graph — cc_graph","text":"cc_graph class graphNEL added slot significant, matrix rows (nodes / annotations) whether found significant given enrichment (columns). matrix used classifying annotations different groups, generating either pie-charts coloring nodes visualization. constructs cc_graph given graphNEL significant matrix.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cc_graph — cc_graph","text":"","code":"cc_graph(graph, significant) # S4 method for class 'cc_graph' show(object) cc_graph(graph, significant)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cc_graph — cc_graph","text":"graph graphNEL significant matrix indicating nodes significant experiment object cc_graph show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"cc_graph — cc_graph","text":"significant numeric matrix ones zeros","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":null,"dir":"Reference","previous_headings":"","what":"combine annotation-features — combine_annotation_features","title":"combine annotation-features — combine_annotation_features","text":"generation proper annotation-annotation relationship graph, need combine annotation-feature relationships across multiple annotation objects","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine annotation-features — combine_annotation_features","text":"","code":"combine_annotation_features(annotation_features)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine annotation-features — combine_annotation_features","text":"annotation_features list annotation_features combine","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine annotation-features — combine_annotation_features","text":"list combined annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":null,"dir":"Reference","previous_headings":"","what":"combine annotations — combine_annotations","title":"combine annotations — combine_annotations","text":"Takes multiple annotation objects combines consistent sole set creating cc_graph providing information annotation entry.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine annotations — combine_annotations","text":"","code":"combine_annotations(annotation_list) # S4 method for class 'list' combine_annotations(annotation_list)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine annotations — combine_annotations","text":"annotation_list one annotation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine annotations — combine_annotations","text":"annotation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":null,"dir":"Reference","previous_headings":"","what":"combine enrichments — combine_enrichments","title":"combine enrichments — combine_enrichments","text":"one primary workhorse functions behind categoryCompare2. primary function categoryCompare enable comparisons different enrichment analyses. facilitate , must first combine one (really, can single) enriched_result.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine enrichments — combine_enrichments","text":"","code":"combine_enrichments(...) # S4 method for class 'enriched_result' combine_enrichments(...) # S4 method for class 'list' combine_enrichments(...)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine enrichments — combine_enrichments","text":"... list enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine enrichments — combine_enrichments","text":"combined_enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":null,"dir":"Reference","previous_headings":"","what":"combine text — combine_text","title":"combine text — combine_text","text":"Given lists named character objects, character vector names final object, either get character string list names, check character string across lists.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine text — combine_text","text":"","code":"combine_text(list_characters, names_out, text_id)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine text — combine_text","text":"list_characters list containing named character strings names_out full list names use text_id name thing put ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine text — combine_text","text":"named character vector","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":null,"dir":"Reference","previous_headings":"","what":"combined coefficient — combined_coefficient","title":"combined coefficient — combined_coefficient","text":"takes average overlap jaccard coefficients","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combined coefficient — combined_coefficient","text":"","code":"combined_coefficient(n1, n2)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combined coefficient — combined_coefficient","text":"n1 group 1 n2 group 2","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combined coefficient — combined_coefficient","text":"double","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"combined enrichments — combined_enrichment-class","title":"combined enrichments — combined_enrichment-class","text":"combined_enrichment class holds results combining several enriched_results together, includes original enriched_results, well cc_graph combined annotation objects.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_enrichment.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"combined enrichments — combined_enrichment-class","text":"enriched list enriched objects annotation annotation annotation_features combined across enriched_result statistics combined_statistics ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":null,"dir":"Reference","previous_headings":"","what":"get significant annotations calls — combined_significant_calls","title":"get significant annotations calls — combined_significant_calls","text":"case combined_enrichment want get significant annotations , put together can start real meta-analysis.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get significant annotations calls — combined_significant_calls","text":"","code":"combined_significant_calls(in_results, queries)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get significant annotations calls — combined_significant_calls","text":"in_results combined_enrichment object queries list queries can form call object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get significant annotations calls — combined_significant_calls","text":"combined_enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get significant annotations calls — combined_significant_calls","text":"Note function returns original combined_enrichment object modified combined_statistics slot significant annotations added .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":null,"dir":"Reference","previous_headings":"","what":"combined statistics — combined_statistics","title":"combined statistics — combined_statistics","text":"holds results extracting bunch statistics combined_enrichment one entity. useful want enable multiple data representations simple filtering actual data.frame statistics, provides flexibility enable . constructor function combined_statistics object, makes sure empty things get initialized correctly","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combined statistics — combined_statistics","text":"","code":"combined_statistics( statistic_data, which_enrichment, which_statistic, annotation_id, significant = NULL, measured = NULL, use_names = NULL ) combined_statistics( statistic_data, which_enrichment, which_statistic, annotation_id, significant = NULL, measured = NULL, use_names = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combined statistics — combined_statistics","text":"statistic_data data.frame statistics which_enrichment enrichment gave results which_statistic statistics calculated case annotation_id annotations returning statistics significant significant annotations measured measured annotations use_names order naming","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combined statistics — combined_statistics","text":"combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"combined statistics — combined_statistics","text":"statistic_data data.frame statistics enrichments significant significant_annotations object, may empty which_enrichment vector giving enrichment column statistics came which_statistic vector providing statistic column contains","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/csv_annotation_table.html","id":null,"dir":"Reference","previous_headings":"","what":"print table csv — csv_annotation_table","title":"print table csv — csv_annotation_table","text":"print annotation gene table CSV file","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/csv_annotation_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print table csv — csv_annotation_table","text":"","code":"csv_annotation_table(annotation_gene_table, out_file = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/csv_annotation_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print table csv — csv_annotation_table","text":"annotation_gene_table list tables out_file file write ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":null,"dir":"Reference","previous_headings":"","what":"the enriched results class — enriched_result","title":"the enriched results class — enriched_result","text":"given slots enriched_result, checks data self-consistent, creates enriched_result object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"the enriched results class — enriched_result","text":"","code":"enriched_result(features, universe, annotation, statistics) enriched_result(features, universe, annotation, statistics)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"the enriched results class — enriched_result","text":"features features differentially expressed (see details) universe features measured annotation annotation object statistics statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"the enriched results class — enriched_result","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"the enriched results class — enriched_result","text":"features \"features\" interest, vector class universe \"features\" background annotation list giving annotation feature relationship statistics statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":null,"dir":"Reference","previous_headings":"","what":"convert enriched object — enriched_to_fgsea","title":"convert enriched object — enriched_to_fgsea","text":"Takes `enriched_result`, converts table expected `fgsea`. done `gsea` *Enrichment Method*.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"convert enriched object — enriched_to_fgsea","text":"","code":"enriched_to_fgsea(in_enriched)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"convert enriched object — enriched_to_fgsea","text":"in_enriched enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"convert enriched object — enriched_to_fgsea","text":"data.table","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/executable_path.html","id":null,"dir":"Reference","previous_headings":"","what":"executable path — executable_path","title":"executable path — executable_path","text":"Show path executables, user can add whatever want.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/executable_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"executable path — executable_path","text":"","code":"executable_path()"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"extract enrich stats — extract_enrich_stats","title":"extract enrich stats — extract_enrich_stats","text":"Extract statistical table single enrichment object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract enrich stats — extract_enrich_stats","text":"","code":"extract_enrich_stats(enrichment_result)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract enrich stats — extract_enrich_stats","text":"enrichment_result enrichment result object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract enrich stats — extract_enrich_stats","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":null,"dir":"Reference","previous_headings":"","what":"extract statistics — extract_statistics,combined_enrichment-method","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"extract statistics combined_enrichment object create combined_statistics statistic underlying statistical_results object enrichments named according enrichment statistic .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"","code":"# S4 method for class 'combined_enrichment' extract_statistics(in_results)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"in_results combined_enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":null,"dir":"Reference","previous_headings":"","what":"get statistics — extract_statistics","title":"get statistics — extract_statistics","text":"extract statistics statistical_results object. can combined data.frame can returned used annotate graph annotations.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get statistics — extract_statistics","text":"","code":"extract_statistics(in_results) # S4 method for class 'statistical_results' extract_statistics(in_results)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get statistics — extract_statistics","text":"in_results statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get statistics — extract_statistics","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"filter graph by significant entries — filter_annotation_graph","title":"filter graph by significant entries — filter_annotation_graph","text":"graph already generated, may faster filter previously generated one generate new one significant data.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"filter graph by significant entries — filter_annotation_graph","text":"","code":"filter_annotation_graph(in_graph, comb_enrich)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"filter graph by significant entries — filter_annotation_graph","text":"in_graph cc_graph previously generated comb_enrich combined_enrichment want use filter ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"filter graph by significant entries — filter_annotation_graph","text":"cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"generate annotation graph — generate_annotation_graph","title":"generate annotation graph — generate_annotation_graph","text":"given combined_enrichment, generate annotation similarity graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate annotation graph — generate_annotation_graph","text":"","code":"generate_annotation_graph( comb_enrichment, annotation_similarity = \"combined\", low_cut = 5, hi_cut = 500 ) # S4 method for class 'combined_enrichment' generate_annotation_graph( comb_enrichment, annotation_similarity = \"combined\", low_cut = 5, hi_cut = 500 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate annotation graph — generate_annotation_graph","text":"comb_enrichment combined_enrichment object annotation_similarity similarity measure use low_cut keep annotations graph least many annotated features hi_cut keep annotations less many annotated features","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate annotation graph — generate_annotation_graph","text":"cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation similarity graph — generate_annotation_similarity_graph","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"given annotation-feature list, generate similarity graph annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"","code":"generate_annotation_similarity_graph( annotation_features, similarity_type = \"combined\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"annotation_features list entry set features annotation similarity_type type overlap coefficient report","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_colors.html","id":null,"dir":"Reference","previous_headings":"","what":"generate colors — generate_colors","title":"generate colors — generate_colors","text":"given bunch items, generate set colors either single node colorings pie-chart annotations. Colors generated using hcl colorspace, n_color >= 5, colors re-ordered attempt create largest contrasts colors, result picked circle hcl space.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_colors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate colors — generate_colors","text":"","code":"generate_colors(n_color)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_colors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate colors — generate_colors","text":"n_color many colors generate","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_legend.html","id":null,"dir":"Reference","previous_headings":"","what":"generate a legend — generate_legend","title":"generate a legend — generate_legend","text":"often helps legend displayed reference.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_legend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate a legend — generate_legend","text":"","code":"generate_legend( in_assign, upper_names = TRUE, img = FALSE, width = 800, height = 400, pointsize = 70, ... )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_legend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate a legend — generate_legend","text":"in_assign assign object annotation_combinations upper_names whether make names uppercase easier viewing img base64 encoded data uri returned embedding? width wide image saving image height high pointsize pointsize parameter Cairo, determines textsize image ... parameter pie","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":null,"dir":"Reference","previous_headings":"","what":"generate link text — generate_link","title":"generate link text — generate_link","text":"given named vector links, generate actual html link formatted output html documents","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate link text — generate_link","text":"","code":"generate_link(links)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate link text — generate_link","text":"links vector links","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate link text — generate_link","text":"character","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":null,"dir":"Reference","previous_headings":"","what":"create piecharts for visualization — generate_piecharts","title":"create piecharts for visualization — generate_piecharts","text":"given group matrix colors experiment, generate pie graphs used glyphs Cytoscape","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"create piecharts for visualization — generate_piecharts","text":"","code":"generate_piecharts(grp_matrix, use_color)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"create piecharts for visualization — generate_piecharts","text":"grp_matrix group matrix use_color colors experiment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"create piecharts for visualization — generate_piecharts","text":"list png files pie graphs","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"create piecharts for visualization — generate_piecharts","text":"exported final version","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":null,"dir":"Reference","previous_headings":"","what":"generate statistical table — generate_table","title":"generate statistical table — generate_table","text":"given combined_enrichment object, get data.frame either investigation add data cc_graph.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate statistical table — generate_table","text":"","code":"generate_table(comb_enrichment, link_type = \"explicit\") # S4 method for class 'combined_enrichment' generate_table(comb_enrichment, link_type = \"explicit\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate statistical table — generate_table","text":"comb_enrichment combined_enrichment object link_type \"explicit\" link (see details)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate statistical table — generate_table","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"generate statistical table — generate_table","text":"link_type controls whether create \"explicit\" link actually column data.frame, create \"implicit\" html link part @name column returned data.frame. Useful embedding data.frame html report.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"orgdb annotations — get_db_annotation","title":"orgdb annotations — get_db_annotation","text":"Generate annotation object genes based \"org.*.db\" object, pulling information .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"orgdb annotations — get_db_annotation","text":"","code":"get_db_annotation( orgdb = \"org.Hs.eg.db\", features = NULL, feature_type = \"ENTREZID\", annotation_type = \"GO\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"orgdb annotations — get_db_annotation","text":"orgdb name org.*.db object features features get annotations feature_type type IDs map (see details) annotation_type type annotation grab (see details)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"orgdb annotations — get_db_annotation","text":"annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"orgdb annotations — get_db_annotation","text":"function generates categoryCompare2 annotation object Bioconductor \"org.*.db\" object. Even though different gene identifiers can used, almost mappings via ENTREZID. set feature gene keys can used create annotations include: ENTREZID: ENTREZ gene ids ACCNUM: genbank accession numbers SYMBOL: gene symbols, eg ABCA1 GENENAME: gene names, eg \"ATP binding cassette subfamily member 1\" ENSEMBL: ensembl gene ids (start ENSG...) ENSEMBLPROT: ensembl protein ids (ENSP...) ENSEMBLTRANS: ensemlb transcript ids (ENST...) REFSEQ: reference sequence IDs, NM, NP, NR, XP, etc UNIGENE: gene ids UNIPROT eg Hs.88556 UNIPROT: protein ids UNIPROT eg P80404 set annotations can mapped features include: GO: annotations gene ontology PATH: KEGG Pathway identifiers (updated since 2011!) CHRLOC: location chromosome OMIM: mendelian inheritance man identifiers PMID: pubmed identifiers PROSITE PFAM: protein family identifiers IPI: protein-protein interactions GO annotations, also possible pass GO use 3 sub-ontologies simultaneously, combination BP, MF, CC.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":null,"dir":"Reference","previous_headings":"","what":"get significant annotations — get_significant_annotations","title":"get significant annotations — get_significant_annotations","text":"given statistical_results object conditional expressions, return significant annotations case combined_enrichment want get significant annotations , put together can start real meta-analysis.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get significant annotations — get_significant_annotations","text":"","code":"get_significant_annotations(in_results, ...) # S4 method for class 'statistical_results' get_significant_annotations(in_results, ...) # S4 method for class 'combined_enrichment' get_significant_annotations(in_results, ...)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get significant annotations — get_significant_annotations","text":"in_results combined_enrichment object ... conditional expressions","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get significant annotations — get_significant_annotations","text":"vector significant annotation_id's combined_enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get significant annotations — get_significant_annotations","text":"Note function returns original combined_enrichment object modified combined_statistics slot significant annotations added .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get significant annotations — get_significant_annotations","text":"","code":"test_stat <- new(\"statistical_results\", annotation_id = c(\"a1\", \"a2\", \"a3\"), statistic_data = list(pvalues = c(a1 = 0.01, a2 = 0.5, a3 = 0.0001), counts = c(a1 = 5, a2 = 10, a3 = 1), odds = c(a1 = 20, a2 = 100, a3 = 0))) get_significant_annotations(test_stat, pvalues < 0.05) #> [1] \"a1\" \"a3\" get_significant_annotations(test_stat, odds > 10) #> [1] \"a1\" \"a2\" get_significant_annotations(test_stat, pvalues < 0.05, counts >= 1) #> [1] \"a1\" \"a3\""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":null,"dir":"Reference","previous_headings":"","what":"get significant annotations calls — get_significant_annotations_calls","title":"get significant annotations calls — get_significant_annotations_calls","text":"case statistical_results want get significant annotations ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get significant annotations calls — get_significant_annotations_calls","text":"","code":"get_significant_annotations_calls(in_results, queries)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get significant annotations calls — get_significant_annotations_calls","text":"in_results statistical_results object queries list queries can form call object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get significant annotations calls — get_significant_annotations_calls","text":"vector significant annotation_id's","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"gocats to annnotations — gocats_to_annotation","title":"gocats to annnotations — gocats_to_annotation","text":"Transforms gocats ancestors JSON list GO annotation object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"gocats to annnotations — gocats_to_annotation","text":"","code":"gocats_to_annotation( ancestors_file = \"ancestors.json\", namespace_file = \"namespace.json\", annotation_type = \"gocatsGO\", feature_type = \"Uniprot\", feature_translation = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"gocats to annnotations — gocats_to_annotation","text":"ancestors_file ancestors.json file gocats (required) namespace_file namespace.json file gocats (optional) annotation_type annotations making? (gocatsGO default) feature_type type features using (assume Uniprot) feature_translation data.frame used convert feature IDs","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"gocats to annnotations — gocats_to_annotation","text":"annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"cc_graph to visnetwork — graph_to_visnetwork","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"takes cc_graph object transforms something can visualized using visNetwork","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"","code":"graph_to_visnetwork( in_graph, in_assign, node_communities = NULL, use_nodes = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"in_graph cc_graph object in_assign colors generated assign_colors node_communities communities generated label_communities use_nodes list nodes actually use","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"do GSEA — gsea_feature_enrichment","title":"do GSEA — gsea_feature_enrichment","text":"Performs gene-set enrichment analysis using `fgsea` package.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do GSEA — gsea_feature_enrichment","text":"","code":"gsea_feature_enrichment( gsea_features, min_features = 15, max_features = 500, return_type = \"cc2\", ... )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do GSEA — gsea_feature_enrichment","text":"gsea_features GSEA features object min_features minimum number features annotation (default = 15) max_features maximum number features annotation (default = 500) return_type type object returned? (\"cc2\" \"fgsea\")","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do GSEA — gsea_feature_enrichment","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"do GSEA — gsea_feature_enrichment","text":"runtime dependent maximum size provided annotation, authors `fgsea` recommend maximum size 500. addition, calculate statistics, minimum size annotated features required. Going 15 may advised. want use `fgsea` functions, recommended set `return_type = \"fgsea\"`. Otherwise, keep default \"cc2\".","code":""},{"path":[]},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_features-class.html","id":null,"dir":"Reference","previous_headings":"","what":"GSEA feature class — gsea_features-class","title":"GSEA feature class — gsea_features-class","text":"class hold features undergoing GSEA","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_features-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"GSEA feature class — gsea_features-class","text":"ranks named vector ranks annotation annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeom_features-class.html","id":null,"dir":"Reference","previous_headings":"","what":"hypergeom feature class — hypergeom_features-class","title":"hypergeom feature class — hypergeom_features-class","text":"class hold features undergoing hypergeometric enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeom_features-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"hypergeom feature class — hypergeom_features-class","text":"significant significant features universe features measured annotation annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":null,"dir":"Reference","previous_headings":"","what":"do hypergeometric test — hypergeometric_basic","title":"do hypergeometric test — hypergeometric_basic","text":"hypergeometric enrichment test","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do hypergeometric test — hypergeometric_basic","text":"","code":"hypergeometric_basic( num_white, num_black, num_drawn, num_white_drawn, direction = \"over\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do hypergeometric test — hypergeometric_basic","text":"num_white number white balls urn num_black number black balls urn num_drawn number balls taken urn num_white_drawn number white balls taken urn direction direction test","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do hypergeometric test — hypergeometric_basic","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"do hypergeometric enrichment — hypergeometric_feature_enrichment","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"hypergeometric enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"","code":"hypergeometric_feature_enrichment( hypergeom_features, direction = \"over\", p_adjust = \"BH\", min_features = 1 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"hypergeom_features hypergeometric_features object direction direction enrichment () p_adjust correct p-values (default \"BH\") min_features many features annotated testing ?","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"min_features argument applies minumum number features annotation universe features supplied, minumum number features differential list. p-value adjustment, see stats::p.adjust","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":null,"dir":"Reference","previous_headings":"","what":"install executables — install_executables","title":"install executables — install_executables","text":"move executables user location, default ~/bin changes permissions make executable.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"install executables — install_executables","text":"","code":"install_executables(path = \"~/bin\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"install executables — install_executables","text":"path path put executable scripts","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"install executables — install_executables","text":"listing files.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":null,"dir":"Reference","previous_headings":"","what":"jaccard coefficient — jaccard_coefficient","title":"jaccard coefficient — jaccard_coefficient","text":"calculates similarity two groups objects using \"jaccard\" coefficient, defined :","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"jaccard coefficient — jaccard_coefficient","text":"","code":"jaccard_coefficient(n1, n2)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"jaccard coefficient — jaccard_coefficient","text":"n1 group 1 n2 group 2","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"jaccard coefficient — jaccard_coefficient","text":"double","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"jaccard coefficient — jaccard_coefficient","text":"length(intersect(n1, n2)) / min(c(length(n1), length(n2)))","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"json to annotation — json_2_annotation","title":"json to annotation — json_2_annotation","text":"Given JSON based annotation object, read create `annotation` actually enrichment.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"json to annotation — json_2_annotation","text":"","code":"json_2_annotation(json_file)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"json to annotation — json_2_annotation","text":"json_file json annotation file","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"json to annotation — json_2_annotation","text":"annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation reversal — json_annotation_reversal","title":"annotation reversal — json_annotation_reversal","text":"Given JSON file features annotations, reverse turn annotations features, optionally add meta-information .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation reversal — json_annotation_reversal","text":"","code":"json_annotation_reversal( json_file, out_file = \"annotations.json\", feature_type = NULL, annotation_type = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation reversal — json_annotation_reversal","text":"json_file json file use out_file json file write feature_type type features annotation_type type annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation reversal — json_annotation_reversal","text":"json object, invisibly","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":null,"dir":"Reference","previous_headings":"","what":"print table kable — kable_annotation_table","title":"print table kable — kable_annotation_table","text":"print annotation gene table knitr::kable format","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print table kable — kable_annotation_table","text":"","code":"kable_annotation_table(annotation_gene_table, header_level = 3, cat = TRUE)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print table kable — kable_annotation_table","text":"annotation_gene_table list tables header_level header level labels done ? cat whether write directly, just return table later","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print table kable — kable_annotation_table","text":"character","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":null,"dir":"Reference","previous_headings":"","what":"label communities — label_communities","title":"label communities — label_communities","text":"Determine label community based generic member community, defined one annotations.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"label communities — label_communities","text":"","code":"label_communities(community_defs, annotation)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"label communities — label_communities","text":"community_defs communities assign_communities annotation annotation object used enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"label communities — label_communities","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":null,"dir":"Reference","previous_headings":"","what":"index a list — multi_query_list","title":"index a list — multi_query_list","text":"Provided list, condition, returns logical indices named part list provided. Uses subset like non-standard evaluation can define appropriate expressions.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"index a list — multi_query_list","text":"","code":"multi_query_list(list_to_query, ...)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"index a list — multi_query_list","text":"list_to_query list run query ... expressions queries","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"index a list — multi_query_list","text":"logical \"&\" queries","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/node_assign-class.html","id":null,"dir":"Reference","previous_headings":"","what":"node_assign — node_assign-class","title":"node_assign — node_assign-class","text":"node_assign class holds unique annotation combinations assignment nodes combinations use visualization.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/node_assign-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"node_assign — node_assign-class","text":"groups unique groups, logical matrix assignments named character vector providing association groups description named character vector providing description group colors named character vector hex colors groups experiments color_type whether group experiment based colors pie_locs experiment colors, pie graphs generated ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":null,"dir":"Reference","previous_headings":"","what":"overlap coefficient — overlap_coefficient","title":"overlap coefficient — overlap_coefficient","text":"calculates similarity using \"overlap\" coefficient, ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"overlap coefficient — overlap_coefficient","text":"","code":"overlap_coefficient(n1, n2)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"overlap coefficient — overlap_coefficient","text":"n1 group 1 objects n2 group 2 objects","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"overlap coefficient — overlap_coefficient","text":"double","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"overlap coefficient — overlap_coefficient","text":"length(intersect(n1, n2)) / length(union(n1, n2))","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":null,"dir":"Reference","previous_headings":"","what":"remove edges — remove_edges","title":"remove edges — remove_edges","text":"given RCy3 network connection, remove edges according provided values.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"remove edges — remove_edges","text":"","code":"remove_edges(edge_obj, cutoff, edge_attr = \"weight\", value_direction = \"under\") # S4 method for class 'character,numeric' remove_edges(edge_obj, cutoff, edge_attr = \"weight\", value_direction = \"under\") # S4 method for class 'cc_graph,numeric' remove_edges(edge_obj, cutoff, edge_attr = \"weight\", value_direction = \"under\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"remove edges — remove_edges","text":"edge_obj cc_graph cutoff cutoff use edge_attr attribute use value_direction remove edges value ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"remove edges — remove_edges","text":"nothing cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-binomial_result-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show binomial_result — show,binomial_result-method","title":"show binomial_result — show,binomial_result-method","text":"show binomial_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-binomial_result-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show binomial_result — show,binomial_result-method","text":"","code":"# S4 method for class 'binomial_result' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-binomial_result-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show binomial_result — show,binomial_result-method","text":"object binomial_result object show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-combined_statistics-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show combined_statistics — show,combined_statistics-method","title":"show combined_statistics — show,combined_statistics-method","text":"show combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-combined_statistics-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show combined_statistics — show,combined_statistics-method","text":"","code":"# S4 method for class 'combined_statistics' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-combined_statistics-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show combined_statistics — show,combined_statistics-method","text":"object combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-enriched_result-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show enriched_result — show,enriched_result-method","title":"show enriched_result — show,enriched_result-method","text":"show enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-enriched_result-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show enriched_result — show,enriched_result-method","text":"","code":"# S4 method for class 'enriched_result' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-enriched_result-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show enriched_result — show,enriched_result-method","text":"object enriched_result object show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-node_assign-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show node_assign — show,node_assign-method","title":"show node_assign — show,node_assign-method","text":"show node_assign","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-node_assign-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show node_assign — show,node_assign-method","text":"","code":"# S4 method for class 'node_assign' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-node_assign-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show node_assign — show,node_assign-method","text":"object node_assign see","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-significant_annotations-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show signficant_annotations — show,significant_annotations-method","title":"show signficant_annotations — show,significant_annotations-method","text":"show signficant_annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-significant_annotations-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show signficant_annotations — show,significant_annotations-method","text":"","code":"# S4 method for class 'significant_annotations' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-significant_annotations-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show signficant_annotations — show,significant_annotations-method","text":"object significant annotations object show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":null,"dir":"Reference","previous_headings":"","what":"significant annotations — significant_annotations","title":"significant annotations — significant_annotations","text":"significant_annotations class holds annotations enrichment measured significant. slots logical matrix rows named annotation_id columns named names enriched_result combined. Makes new significant_annotation checking everything valid.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"significant annotations — significant_annotations","text":"","code":"significant_annotations(significant, measured, sig_calls = NULL) significant_annotations(significant, measured, sig_calls = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"significant annotations — significant_annotations","text":"significant logical matrix annotations (rows) experiments (columns) measured logical matrix annotations (rows) experiments (columns) sig_calls character vector deparsed calls resulted signficant measured","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"significant annotations — significant_annotations","text":"significant logical matrix measured logical matrix sig_calls character representations calls used filter data","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/statistical_results-class.html","id":null,"dir":"Reference","previous_headings":"","what":"statistical results class — statistical_results-class","title":"statistical results class — statistical_results-class","text":"class holds part enrichment statistical results. two pieces, list statistics named list actual numerical results applying statistics. piece annotation_id vector defining entry vector statistics .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/statistical_results-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"statistical results class — statistical_results-class","text":"statistic_data list numerical statistics annotation_id vector ids method statistics calculated","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"table from graph — table_from_graph","title":"table from graph — table_from_graph","text":"Creates table annotation graph, provided, adds community information table.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"table from graph — table_from_graph","text":"","code":"table_from_graph(in_graph, in_assign = NULL, community_info = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"table from graph — table_from_graph","text":"in_graph cc_graph object in_assign node_assign object community_info community_info object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"table from graph — table_from_graph","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":null,"dir":"Reference","previous_headings":"","what":"visualize in cytoscape — vis_in_cytoscape","title":"visualize in cytoscape — vis_in_cytoscape","text":"given graph, node assignments, visualize graph cytoscape manipulation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"visualize in cytoscape — vis_in_cytoscape","text":"","code":"vis_in_cytoscape(in_graph, in_assign, description = \"cc2 enrichment\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"visualize in cytoscape — vis_in_cytoscape","text":"in_graph cc_graph visualize in_assign node_assign generated description something descriptive vis (useful lots different visualizations)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"visualize in cytoscape — vis_in_cytoscape","text":"something","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_visnetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"vis in visNetwork — vis_visnetwork","title":"vis in visNetwork — vis_visnetwork","text":"Visualize cc_graph visNetwork, selection communities exists.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_visnetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"vis in visNetwork — vis_visnetwork","text":"","code":"vis_visnetwork(in_graph_info)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_visnetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"vis in visNetwork — vis_visnetwork","text":"in_graph_info graph structure graph_to_visnetwork","code":""},{"path":[]},{"path":[]}]
+[{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"categoryCompare2: Command Line Interface","text":"Assuming linux type system, can add path, set executable. ’m going set R, ’s access . show shell well. Assuming ’ve saved path shell variable, CLI_LOCATION, can add path, change executables executable. make scripts executable, can check can actually use . Now, create directory put input files results.","code":"remotes::install(\"moseleybioinformaticslab/categorycompare2\") cli_location = system.file(\"exec\", package = \"categoryCompare2\") cli_location #> [1] \"/tmp/RtmpGN9CpH/temp_libpath23d14dd35c6c/categoryCompare2/exec\" dir(cli_location) #> [1] \"categoryCompare2.R\" \"create_annotations.R\" \"feature_files_2_json.R\" #> [4] \"filter_and_group.R\" \"run_enrichment.R\" Sys.setenv(CLI_LOCATION = cli_location) old_path = Sys.getenv(\"PATH\") new_path = paste0(old_path, \":\", cli_location) Sys.setenv(PATH = new_path) export PATH=\"$PATH:$CLI_LOCATION\" chmod 0750 $CLI_LOCATION/*.R categoryCompare2.R --help"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"inputs","dir":"Articles","previous_headings":"","what":"Inputs","title":"categoryCompare2: Command Line Interface","text":"categoryCompare2 CLI needs different pieces information work: Annotations features enrichment . example Gene Ontology terms gene products. set features measured. RNA-Seq, genes transcripts genome organism. One sets differentially expressed features (genes transcripts). small example, going use estrogen microarray dataset main vignette. ’ve found differential sets genes timepoint, 10 48 hours. set genes measured array universe_entrez.txt, 10 hour differential genes 10_entrez.txt, 48 hour differential genes 48_entrez.txt.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"running","dir":"Articles","previous_headings":"","what":"Running","title":"categoryCompare2: Command Line Interface","text":"knitr good running sequential bash shell commands, going go whole analysis via CLI , comments, discuss one , just without output.","code":"test_loc = system.file(\"extdata\", \"test_data\", package = \"categoryCompare2\") Sys.setenv(TESTLOC = test_loc) export CURR_DIR=$(pwd) export WORKING=$CURR_DIR/cc2_1234 mkdir -p $WORKING cd $WORKING cp $TESTLOC/10_entrez.txt . cp $TESTLOC/48_entrez.txt . cp $TESTLOC/universe_entrez.txt . # get the annotations from installed organism database create_annotations.R --orgdb=org.Hs.eg.db --feature-type=ENTREZID \\ --annotation-type=GO --json=example_annotations.json # setup the gene lists feature_files_2_json.R --file1=10_entrez.txt --file2=48_entrez.txt \\ --universe=universe_entrez.txt --json=example_features.json # do the enrichments run_enrichment.R --features=example_features.json \\ --annotations=example_annotations.json --output-file=example_enrichment.txt # filter and find communities of related GO terms by shared feature annotations filter_and_group.R --enrichment-results=example_enrichment.txt \\ --p-cutoff=0.01 --count-cutoff=2 --similarity-file=example_similarity.rds --similarity-cutoff=0.8 \\ --table-file=example_grouping.txt # cleanup rm -rf $WORKING #>
[H
[2J
[3JLoading required namespace: GO.db #> #>
[H
[2J
[3J
[H
[2J
[3J[1] \"example_enrichment.txt\" #>
[H
[2J
[3JSignificant Annotations: #> Signficance Cutoffs: #> counts >= 2 #> padjust <= 0.01 #> #> Counts: #> 10_entrez 48_entrez counts #> G1 1 1 130 #> G2 1 0 152 #> G3 0 1 42 #> G4 0 0 20110 #> Saving annotation similarities in: example_similarity.rds #> Removed 17683 edges from graph"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"annotations","dir":"Articles","previous_headings":"","what":"Annotations","title":"categoryCompare2: Command Line Interface","text":"OK, let’s break little . Annotations information features. example, gene products, Gene Ontology terms, describe pathways products involved (Biological Process), chemical transformations binding properties gene products might (Molecular Function), cell biological structure might (Cellular Component). common gene product annotations also biological pathway membership like Kyoto Encyclopedia Genes Genomes (KEGG), pathways Reactome. Similarly, chemical compounds might annotated pathways KEGG Reactome. One common feature annotations Gene Ontology (GO) terms. Bioconductor includes GO terms organism databases (org-db), org.Hs.eg.db, organism database Homo sapiens, indexed Entrez Gene (eg). categoryCompare2 includes CLI utility getting GO terms included org-db form can used rest CLI, create_annotations.R. arguments : –orgdb: org-db use –feature-type: type feature IDs used map GO terms –annotation-type: annotations pull –json: output json stored Alternatively, source annotations, can pass directly using: example, Moseley Bioinformatics Lab python project, gocats, enables fuller consideration term-term relationships GO ontology structure. One gocats sub-commands outputs json structured file gene term mappings can used input create_annotations.R, gocats remap_goterms.","code":"create_annotations.R --orgdb=org.Hs.eg.db --feature-type=ENTREZID \\ --annotation-type=GO --json=example_annotations.json create_annotations.R --input=annotations.json --feature-type=ENTREZID \\ --annotation-type=GO --json=example_annotations.json"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"features","dir":"Articles","previous_headings":"","what":"Features","title":"categoryCompare2: Command Line Interface","text":"Features things ’ve measured. example, genes. can also metabolites, chromosomal regions, etc. annotation / category enrichment, need know features measured (universe), features interested . Part utility categoryCompare2 providing ability compare enrichments multiple lists. case, can combine four feature lists using CLI. need combine four feature lists, might want look R API directly, write code create JSON file directly. name group features taken file name. inputs : –file1: set features (required) –file2: another set features (optional) –file3: features (optional) –file4: features (optional) –universe: features measured –json: output file","code":"feature_files_2_json.R --file1=10_entrez.txt --file2=48_entrez.txt \\ --universe=universe_entrez.txt --json=example_features.json"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"running-enrichments","dir":"Articles","previous_headings":"","what":"Running Enrichments","title":"categoryCompare2: Command Line Interface","text":"runs hypergeometric enrichment feature lists output json file creating features . least, features, annotations, output. full list options includes: –config: YAML configuration file –default-config: display default configuration file –features: JSON file containing features (genes) [default: features.json] –annotations: annotations use, file [default: annotations.json] –enrichment-test: type test [default: hypergeometric] –enrichment-direction: want - -enrichment [default: ] –p-adjustment: kind p-value correction perform [default: BH] –output-file: save results [default: cc2_results.txt] –text-: text file generated? [default: FALSE]","code":"run_enrichment.R --features=example_features.json \\ --annotations=example_annotations.json --output-file=example_enrichment.txt"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"text-only-false","dir":"Articles","previous_headings":"Running Enrichments","what":"Text Only FALSE","title":"categoryCompare2: Command Line Interface","text":"’s important, want anything CLI results, keep --text-=FALSE. next step CLI uses rds file generated default enrichment results. use --text-=TRUE, Filter Group Annotations. Depending analysis want , needs, may fine. Just aware.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/command_line_interface.html","id":"filter-and-group-annotations","dir":"Articles","previous_headings":"","what":"Filter and Group Annotations","title":"categoryCompare2: Command Line Interface","text":"Finally, running enrichments, filter think significant, alternatively group GO terms similarity look communities GO terms. Although lists *.txt file input, actually looking matching rds file use, information necessary filtering grouping (see ). Let’s go options: –enrichment-results: enrichment results saved [default: cc2_results.txt] –p-cutoff: maximum p-value consider significant [default: 0.01] –adjusted-p-values: adjusted p-values used exist? [default: TRUE] –count-cutoff: minimum number significant features annotated annotation considered [default: 2] –similarity-file: grouping annotations attempted saved [default: annotation_similarity.rds] –similarity-cutoff: minimum similarity measure consider annotations linked [default: 0] –grouping-algorithm: algorithm used find groups [default: walktrap] –table-file: results file save results [default: cc2_results_grouped.txt] –network-file: desired, save network well [default: NULL] (currently implemented) grouping, definitely want adjust similarity-cutoff something higher, generally, make smaller groups terms. experience, ’ve found 0.8 good value use Gene Ontology terms. types annotations, may want use higher lower similarity cutoff. Unfortunately, outputs get depend values p-cutoff, count-cutoff, similarity-cutoff used. However, can iterate fairly rapidly enrichment already done. addition, annotation similarity network actually saved R rds file, long filename used, instead recalculating annotation similarities, loaded --similarity-file. also speeds computations considerably.","code":"filter_and_group.R --enrichment-results=example_enrichment.txt \\ --p-cutoff=0.01 --count-cutoff=2 --similarity-file=example_similarity.rds \\ --similarity-cutoff=0.8 --table-file=example_grouping.txt"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"Current high-throughput molecular biology experiments generating larger larger amounts data. Although many different methods analyze individual experiments, methods allow comparison different data sets sorely lacking. important due number experiments carried biological systems may amenable either fusion comparison. current tools available focus finding genes experiments listed , can shown statistically significant gene listed results experiments. However, many tools consider similarities (just importantly, differences) experimental results categorical level. Categorical data includes gene annotation, Gene Ontologies, KEGG pathways, chromosome location, etc. categoryCompare developed allow comparison high-throughput experiments categorical level, explore results intuitive fashion.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"sample-data","dir":"Articles","previous_headings":"","what":"Sample Data","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"make concept concrete, examine data microarray data set estrogen available Bioconductor. data set contains 8 samples, 2 levels estrogen therapy (present vs absent), two time points (10 48 hours). pre-processed version data available package, commands used generate . Note: preprocessed one keeps top 100 genes, use results slightly different shown vignette. can see descriptions arrays. First, read cel files, normalize data using RMA. make easier conceptualize, split data two eSet objects time, perform manipulations calculating significantly differentially expressed genes eSet object. 10 hour samples: 48 hour samples thing: grab genes array background set. time points generated list genes differentially expressed present vs absent samples. compare time-points, find common discordant genes experiments, try interpret lists. commonly done many meta-analysis studies attempt combine results many different experiments. alternative approach, used categoryCompare, compare significantly enriched categories two gene lists. Currently package supports two category classes, Gene Ontology, KEGG pathways. used . Note 1: proposing best way analyse particular data, sample data set merely serves illustrate functionality package. However, many different experiments type approach definitely appropriate, user determine data fits analytical paradigm advocated .","code":"library(\"affy\") library(\"hgu95av2.db\") library(\"genefilter\") library(\"estrogen\") library(\"limma\") datadir <- system.file(\"extdata\", package = \"estrogen\") pd <- read.AnnotatedDataFrame(file.path(datadir,\"estrogen.txt\"), header = TRUE, sep = \"\", row.names = 1) pData(pd) ## estrogen time.h ## low10-1.cel absent 10 ## low10-2.cel absent 10 ## high10-1.cel present 10 ## high10-2.cel present 10 ## low48-1.cel absent 48 ## low48-2.cel absent 48 ## high48-1.cel present 48 ## high48-2.cel present 48 currDir <- getwd() setwd(datadir) a <- ReadAffy(filenames=rownames(pData(pd)), phenoData = pd, verbose = TRUE) ## 1 reading low10-1.cel ...instantiating an AffyBatch (intensity a 409600x8 matrix)...done. ## Reading in : low10-1.cel ## Reading in : low10-2.cel ## Reading in : high10-1.cel ## Reading in : high10-2.cel ## Reading in : low48-1.cel ## Reading in : low48-2.cel ## Reading in : high48-1.cel ## Reading in : high48-2.cel setwd(currDir) eData <- affy::rma(a) ## Warning: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when ## loading 'hgu95av2cdf' ## Warning: replacing previous import 'AnnotationDbi::head' by 'utils::head' when ## loading 'hgu95av2cdf' ## Background correcting ## Normalizing ## Calculating Expression e10 <- eData[, eData$time.h == 10] e10 <- nsFilter(e10, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\")$eset e10$estrogen <- factor(e10$estrogen) d10 <- model.matrix(~0 + e10$estrogen) colnames(d10) <- unique(e10$estrogen) fit10 <- lmFit(e10, d10) c10 <- makeContrasts(present - absent, levels=d10) fit10_2 <- contrasts.fit(fit10, c10) eB10 <- eBayes(fit10_2) table10 <- topTable(eB10, number=nrow(e10), p.value=1, adjust.method=\"BH\") table10$Entrez <- unlist(mget(rownames(table10), hgu95av2ENTREZID, ifnotfound=NA)) e48 <- eData[, eData$time.h == 48] e48 <- nsFilter(e48, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\" )$eset e48$estrogen <- factor(e48$estrogen) d48 <- model.matrix(~0 + e48$estrogen) colnames(d48) <- unique(e48$estrogen) fit48 <- lmFit(e48, d48) c48 <- makeContrasts(present - absent, levels=d48) fit48_2 <- contrasts.fit(fit48, c48) eB48 <- eBayes(fit48_2) table48 <- topTable(eB48, number=nrow(e48), p.value=1, adjust.method=\"BH\") table48$Entrez <- unlist(mget(rownames(table48), hgu95av2ENTREZID, ifnotfound=NA)) gUniverse <- unique(union(table10$Entrez, table48$Entrez))"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"create-gene-list","dir":"Articles","previous_headings":"","what":"Create Gene List","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"","code":"library(\"categoryCompare2\") library(\"GO.db\") library(\"org.Hs.eg.db\") g10 <- unique(table10$Entrez[table10$adj.P.Val < 0.05]) g48 <- unique(table48$Entrez[table48$adj.P.Val < 0.05])"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"create-go-annotation-object","dir":"Articles","previous_headings":"","what":"Create GO Annotation Object","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"can analysis, need define annotation object, maps annotations features (genes case). Gene Ontology (GO) based analysis, genes annotated particular GO term based inheritance GO DAG. can generate list using GOALL column org.Hs.eg.db, filter terms interest, use .","code":"go_all_gene <- AnnotationDbi::select(org.Hs.eg.db, keys = gUniverse, columns = c(\"GOALL\", \"ONTOLOGYALL\")) ## 'select()' returned 1:many mapping between keys and columns go_all_gene <- go_all_gene[go_all_gene$ONTOLOGYALL == \"BP\", ] bp_2_gene <- split(go_all_gene$ENTREZID, go_all_gene$GOALL) bp_2_gene <- lapply(bp_2_gene, unique) bp_desc <- AnnotationDbi::select(GO.db, keys = names(bp_2_gene), columns = \"TERM\", keytype = \"GOID\")$TERM ## 'select()' returned 1:1 mapping between keys and columns names(bp_desc) <- names(bp_2_gene) bp_annotation <- categoryCompare2::annotation(annotation_features = bp_2_gene, description = bp_desc, annotation_type = \"GO.BP\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"do-enrichment","dir":"Articles","previous_headings":"","what":"Do Enrichment","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"Now can hypergeometric enrichment gene lists.","code":"g10_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g10, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" ) g48_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g48, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"combine-and-find-significant","dir":"Articles","previous_headings":"","what":"Combine and Find Significant","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"","code":"bp_combined <- combine_enrichments(g10 = g10_enrich, g48 = g48_enrich) bp_sig <- get_significant_annotations(bp_combined, padjust <= 0.001, counts >= 2) bp_sig@statistics@significant ## Signficance Cutoffs: ## padjust <= 0.001 ## counts >= 2 ## ## Counts: ## g10 g48 counts ## G1 1 1 72 ## G2 1 0 53 ## G3 0 1 48 ## G4 0 0 14118"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"generate-graph","dir":"Articles","previous_headings":"","what":"Generate Graph","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"","code":"bp_graph <- generate_annotation_graph(bp_sig) bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 7740 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_graph <- remove_edges(bp_graph, 0.8) ## Removed 7530 edges from graph bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 210 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_assign <- annotation_combinations(bp_graph) bp_assign <- assign_colors(bp_assign)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"find-communities","dir":"Articles","previous_headings":"Generate Graph","what":"Find Communities","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"useful define annotations terms communities. run methods find label communities, generating visualization table.","code":"bp_communities <- assign_communities(bp_graph) bp_comm_labels <- label_communities(bp_communities, bp_annotation)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_guide.html","id":"cytoscape-visualization","dir":"Articles","previous_headings":"","what":"Cytoscape Visualization","title":"categoryCompare: High-throughput data meta-analysis using gene annotations, V2","text":"can generate legend know colors correspond group.","code":"bp_vis <- vis_in_cytoscape(bp_graph, bp_assign, \"BP\") generate_legend(bp_assign)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"categorycompare2-alternative-visualization","dir":"Articles","previous_headings":"","what":"categoryCompare2: Alternative Visualization","title":"categoryCompare2: visNetwork","text":"Authored : Robert M Flight 2024-10-30 15:01:10.077899","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"introduction","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Introduction","title":"categoryCompare2: visNetwork","text":"Current high-throughput molecular biology experiments generating larger larger amounts data. Although many different methods analyze individual experiments, methods allow comparison different data sets sorely lacking. important due number experiments carried biological systems may amenable either fusion comparison. current tools available focus finding genes experiments listed , can shown statistically significant gene listed results experiments. However, many tools consider similarities (just importantly, differences) experimental results categorical level. Categoical data includes gene annotation, Gene Ontologies, KEGG pathways, chromosome location, etc. categoryCompare developed allow comparison high-throughput experiments categorical level, explore results intuitive fashion.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"sample-data","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Sample Data","title":"categoryCompare2: visNetwork","text":"make concept concrete, examine data microarray data set estrogen available Bioconductor. data set contains 8 samples, 2 levels estrogen therapy (present vs absent), two time points (10 48 hours). pre-processed version data available package, commands used generate . Note: preprocessed one keeps top 100 genes, use results slightly different shown vignette. can see descriptions arrays. First, read cel files, normalize data using RMA. make easier conceptualize, split data two eSet objects time, perform manipulations calculating significantly differentially expressed genes eSet object. 10 hour samples: 48 hour samples thing: grab genes array background set. time points generated list genes differentially expressed present vs absent samples. compare time-points, find common discordant genes experiments, try interpret lists. commonly done many meta-analysis studies attempt combine results many different experiments. alternative approach, used categoryCompare, compare significantly enriched categories two gene lists. Currently package supports two category classes, Gene Ontology, KEGG pathways. used . Note 1: proposing best way analyse particular data, sample data set merely serves illustrate functionality package. However, many different experiments type approach definitely appropriate, user determine data fits analytical paradigm advocated .","code":"library(\"affy\") library(\"hgu95av2.db\") library(\"genefilter\") library(\"estrogen\") library(\"limma\") datadir <- system.file(\"extdata\", package = \"estrogen\") pd <- read.AnnotatedDataFrame(file.path(datadir,\"estrogen.txt\"), header = TRUE, sep = \"\", row.names = 1) pData(pd) ## estrogen time.h ## low10-1.cel absent 10 ## low10-2.cel absent 10 ## high10-1.cel present 10 ## high10-2.cel present 10 ## low48-1.cel absent 48 ## low48-2.cel absent 48 ## high48-1.cel present 48 ## high48-2.cel present 48 currDir <- getwd() setwd(datadir) a <- ReadAffy(filenames=rownames(pData(pd)), phenoData = pd, verbose = TRUE) ## 1 reading low10-1.cel ...instantiating an AffyBatch (intensity a 409600x8 matrix)...done. ## Reading in : low10-1.cel ## Reading in : low10-2.cel ## Reading in : high10-1.cel ## Reading in : high10-2.cel ## Reading in : low48-1.cel ## Reading in : low48-2.cel ## Reading in : high48-1.cel ## Reading in : high48-2.cel setwd(currDir) eData <- rma(a) ## Warning: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when ## loading 'hgu95av2cdf' ## Warning: replacing previous import 'AnnotationDbi::head' by 'utils::head' when ## loading 'hgu95av2cdf' ## Background correcting ## Normalizing ## Calculating Expression e10 <- eData[, eData$time.h == 10] e10 <- nsFilter(e10, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\")$eset e10$estrogen <- factor(e10$estrogen) d10 <- model.matrix(~0 + e10$estrogen) colnames(d10) <- unique(e10$estrogen) fit10 <- lmFit(e10, d10) c10 <- makeContrasts(present - absent, levels=d10) fit10_2 <- contrasts.fit(fit10, c10) eB10 <- eBayes(fit10_2) table10 <- topTable(eB10, number=nrow(e10), p.value=1, adjust.method=\"BH\") table10$Entrez <- unlist(mget(rownames(table10), hgu95av2ENTREZID, ifnotfound=NA)) e48 <- eData[, eData$time.h == 48] e48 <- nsFilter(e48, remove.dupEntrez=TRUE, var.filter=FALSE, feature.exclude=\"^AFFX\" )$eset e48$estrogen <- factor(e48$estrogen) d48 <- model.matrix(~0 + e48$estrogen) colnames(d48) <- unique(e48$estrogen) fit48 <- lmFit(e48, d48) c48 <- makeContrasts(present - absent, levels=d48) fit48_2 <- contrasts.fit(fit48, c48) eB48 <- eBayes(fit48_2) table48 <- topTable(eB48, number=nrow(e48), p.value=1, adjust.method=\"BH\") table48$Entrez <- unlist(mget(rownames(table48), hgu95av2ENTREZID, ifnotfound=NA)) gUniverse <- unique(union(table10$Entrez, table48$Entrez))"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"create-gene-list","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Create Gene List","title":"categoryCompare2: visNetwork","text":"","code":"library(\"categoryCompare2\") library(\"GO.db\") library(\"org.Hs.eg.db\") g10 <- unique(table10$Entrez[table10$adj.P.Val < 0.05]) g48 <- unique(table48$Entrez[table48$adj.P.Val < 0.05])"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"create-go-annotation-object","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Create GO Annotation Object","title":"categoryCompare2: visNetwork","text":"can analysis, need define annotation object, maps annotations features (genes case). Gene Ontology (GO) based analysis, genes annotated particular GO term based inheritance GO DAG. can generate list using GOALL column org.Hs.eg.db, filter terms interest, use .","code":"go_all_gene <- AnnotationDbi::select(org.Hs.eg.db, keys = gUniverse, columns = c(\"GOALL\", \"ONTOLOGYALL\")) ## 'select()' returned 1:many mapping between keys and columns go_all_gene <- go_all_gene[go_all_gene$ONTOLOGYALL == \"BP\", ] bp_2_gene <- split(go_all_gene$ENTREZID, go_all_gene$GOALL) bp_2_gene <- lapply(bp_2_gene, unique) bp_desc <- AnnotationDbi::select(GO.db, keys = names(bp_2_gene), columns = \"TERM\", keytype = \"GOID\")$TERM ## 'select()' returned 1:1 mapping between keys and columns names(bp_desc) <- names(bp_2_gene) bp_annotation <- categoryCompare2::annotation(annotation_features = bp_2_gene, description = bp_desc, annotation_type = \"GO.BP\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"do-enrichment","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Do Enrichment","title":"categoryCompare2: visNetwork","text":"Now can hypergeometric enrichment gene lists.","code":"g10_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g10, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" ) g48_enrich <- hypergeometric_feature_enrichment( new(\"hypergeom_features\", significant = g48, universe = gUniverse, annotation = bp_annotation), p_adjust = \"BH\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"combine-and-find-significant","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Combine and Find Significant","title":"categoryCompare2: visNetwork","text":"","code":"bp_combined <- combine_enrichments(g10 = g10_enrich, g48 = g48_enrich) bp_sig <- get_significant_annotations(bp_combined, padjust <= 0.001, counts >= 2) bp_sig@statistics@significant ## Signficance Cutoffs: ## padjust <= 0.001 ## counts >= 2 ## ## Counts: ## g10 g48 counts ## G1 1 1 72 ## G2 1 0 53 ## G3 0 1 48 ## G4 0 0 14118"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"generate-graph","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"Generate Graph","title":"categoryCompare2: visNetwork","text":"","code":"bp_graph <- generate_annotation_graph(bp_sig) bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 7740 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_graph <- remove_edges(bp_graph, 0.8) ## Removed 7530 edges from graph bp_graph ## A cc_graph with ## Number of Nodes = 135 ## Number of Edges = 210 ## g10 g48 counts ## G1 1 1 64 ## G2 1 0 26 ## G3 0 1 45 bp_assign <- annotation_combinations(bp_graph) bp_assign <- assign_colors(bp_assign)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"visnetwork-visualization","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization","what":"visNetwork Visualization","title":"categoryCompare2: visNetwork","text":"can use DiagrammeR visNetwork html widgets create interactive visualizations either RStudio viewer, panes html report.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"find-communities","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization > visNetwork Visualization","what":"Find Communities","title":"categoryCompare2: visNetwork","text":"useful define annotations terms communities. run methods find label communities, generating visualization table.","code":"bp_communities <- assign_communities(bp_graph) bp_comm_labels <- label_communities(bp_communities, bp_annotation)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"create-stats-table","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization > visNetwork Visualization","what":"Create Stats Table","title":"categoryCompare2: visNetwork","text":"provide list GO terms communities found, lets generate table, community labels makes easier find graph desired.","code":"bp_table <- table_from_graph(bp_graph, bp_assign, bp_comm_labels) knitr::kable(bp_table)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/articles/v2_visnetwork_guide.html","id":"actually-visualize-it","dir":"Articles","previous_headings":"categoryCompare2: Alternative Visualization > visNetwork Visualization","what":"Actually Visualize It!","title":"categoryCompare2: visNetwork","text":" show table generated first 3 GO terms. one run, find table.","code":"bp_network <- graph_to_visnetwork(bp_graph, bp_assign, bp_comm_labels) vis_visnetwork(bp_network) annotation_table <- annotation_gene_table(bp_combined, graph::nodes(bp_graph), use_db = org.Hs.eg.db) kable_annotation_table(annotation_table, header = 4) csv_annotation_table(annotation_table, out_file = \"bp_annotations.csv\")"},{"path":[]},{"path":[]},{"path":[]},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robert M Flight. Author, maintainer.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Flight RM, Harrison BJ, Mohammad F, Bunge MB, Moon LDF, Petruska JC, Rouchka EC (2014). “CATEGORYCOMPARE, analytical tool based feature annotations.” Frontiers Genetics. doi:10.3389/fgene.2014.00098, http://dx.doi.org/10.3389/fgene.2014.00098.","code":"@Article{, title = {CATEGORYCOMPARE, an analytical tool based on feature annotations}, author = {Robert M Flight and Benjamin J Harrison and Fahim Mohammad and Mary B Bunge and Lawrence D F Moon and Jeffrey C Petruska and Eric C Rouchka}, year = {2014}, url = {http://dx.doi.org/10.3389/fgene.2014.00098}, doi = {10.3389/fgene.2014.00098}, journal = {Frontiers in Genetics}, }"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"categorycompare2","dir":"","previous_headings":"","what":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Bioconductor package meta analysis high-throughput datasets using enriched feature annotations instead just features . Note rewrite categoryCompare package. information things changed , please see poster.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"api","dir":"","previous_headings":"","what":"API","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"version mostly complete works many full analyses. user facing API expected close fixed, especially core functionality. methods need actual S4 R6 based objects methods, expect function calls remain .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"to-do","dir":"","previous_headings":"","what":"To Do","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Things still needed include: Wrapper initial analysis given feature data annotations Example importing users annotations Integration GOCats Python library summarizing ontologies Better exploration features linked specific annotations, including original data associated features","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Vignette provides description thinking behind package well toy example demonstration purposes.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Installation package Github requires remotes package.","code":"install.packages(\"remotes\") library(remotes) install_github(\"MoseleyBioinformaticsLab/categoryCompare2\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"mac-installation","dir":"","previous_headings":"Installation","what":"Mac Installation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"one issue installation MacOS, {Cairo} package. actually due xquartz installed. easiest way can find install using homebrew. terminal, can : Hopefully worked fine, now able use functionality {categoryCompare2}.","code":"/bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\" brew install --cask xquartz R install.packages(\"Cairo\") # skip if already installed library(Cairo)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Meta-Analysis of High-Throughput Experiments Using Feature Annotations","text":"Flight RM, Harrison BJ, Mohammad F, Bunge MB, Moon LDF, Petruska JC Rouchka EC (2014). .CATEGORYCOMPARE, analytical tool based feature annotations. Frontiers Genetics. link","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"add table data to graph — add_data_to_graph","title":"add table data to graph — add_data_to_graph","text":"given annotation_graph data.frame, add data data.frame graph available elsewhere. Note NA integer numerics, value modified -100, infinite values, modified 1e100.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"add table data to graph — add_data_to_graph","text":"","code":"add_data_to_graph(graph, data)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"add table data to graph — add_data_to_graph","text":"graph graph work data data add ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_data_to_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"add table data to graph — add_data_to_graph","text":"graphNEL","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":null,"dir":"Reference","previous_headings":"","what":"add tooltip — add_tooltip","title":"add tooltip — add_tooltip","text":"passing Cytoscape, add tooltip attribute graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"add tooltip — add_tooltip","text":"","code":"add_tooltip( in_graph, node_data = c(\"name\", \"description\"), description, separator = \"\\n\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"add tooltip — add_tooltip","text":"in_graph graph work node_data pieces node data use description descriptive text use separator separator use tooltip","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/add_tooltip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"add tooltip — add_tooltip","text":"graph new nodeData member \"tooltip\"","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation class — annotation","title":"annotation class — annotation","text":"class holds annotation object defines annotations relate features, well various pieces annotation sensical checks creating annotation object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation class — annotation","text":"","code":"annotation( annotation_features, annotation_type = NULL, description = character(0), links = character(0), feature_type = NULL ) # S4 method for class 'annotation' show(object) annotation( annotation_features, annotation_type = NULL, description = character(0), links = character(0), feature_type = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation class — annotation","text":"annotation_features list annotation feature relationships annotation_type simple one word description annotations description character vector providing descriptive text annotation links character vector defining html links annotation (may empty) feature_type one word description feature type object annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"annotation class — annotation","text":"objects may created hand, may result specific functions create . notably, package provides functions creating Gene Ontology annotation. See annotation, slot parameter.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"annotation class — annotation","text":"annotation_features list annotation feature relationships description character vector providing descriptive text annotation counts numeric vector many features annotation links character vector defining html links annotation (may empty) annotation_type one word short description \"type\" annotation feature_type one word short description \"type\" features","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation to json — annotation_2_json","title":"annotation to json — annotation_2_json","text":"Given `categoryCompare2` annotation object, generate JSON representation can used command line executable","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation to json — annotation_2_json","text":"","code":"annotation_2_json(annotation_obj, json_file = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation to json — annotation_2_json","text":"annotation_obj annotation object json_file file save ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_2_json.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation to json — annotation_2_json","text":"json string (invisibly)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"unique annotation combinations — annotation_combinations","title":"unique annotation combinations — annotation_combinations","text":"determine unique combinations annotations exist significant matrix cc_graph assign node graph group. determine unique combinations annotations exist significant matrix combined_statistics assign annotation group.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"unique annotation combinations — annotation_combinations","text":"","code":"annotation_combinations(object) # S4 method for class 'cc_graph' annotation_combinations(object) # S4 method for class 'significant_annotations' annotation_combinations(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"unique annotation combinations — annotation_combinations","text":"object combined_statistics work ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"unique annotation combinations — annotation_combinations","text":"node_assignment node_assignment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation to genes — annotation_gene_table","title":"annotation to genes — annotation_gene_table","text":"Creates tabular output annotations genes providing lookup genes contributing particular annotation.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation to genes — annotation_gene_table","text":"","code":"annotation_gene_table( combined_enrichment, annotations = NULL, use_db = NULL, input_type = \"ENTREZID\", gene_info = c(\"SYMBOL\", \"GENENAME\") )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation to genes — annotation_gene_table","text":"combined_enrichment combined enrichment object annotations annotations grab features use_db annotation database input_type type gene id ? gene_info type info return gene","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/annotation_gene_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation to genes — annotation_gene_table","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":null,"dir":"Reference","previous_headings":"","what":"assign colors — assign_colors","title":"assign colors — assign_colors","text":"given node_assign, assign colors either independent groups unique annotations, experiments independently.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"assign colors — assign_colors","text":"","code":"assign_colors(in_assign, type = \"experiment\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"assign colors — assign_colors","text":"in_assign node_assign object generated cc_graph type either \"group\" \"experiment\"","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_colors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"assign colors — assign_colors","text":"node_assign colors","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":null,"dir":"Reference","previous_headings":"","what":"assign communities — assign_communities","title":"assign communities — assign_communities","text":"given cc_graph, find communities nodes based connectivity weights.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"assign communities — assign_communities","text":"","code":"assign_communities(in_graph)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"assign communities — assign_communities","text":"in_graph cc_graph object use","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/assign_communities.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"assign communities — assign_communities","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":null,"dir":"Reference","previous_headings":"","what":"do binomial test — binomial_basic","title":"do binomial test — binomial_basic","text":"binomial test","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do binomial test — binomial_basic","text":"","code":"binomial_basic( positive_cases, total_cases, p_expected = 0.5, direction = \"two.sided\", conf_level = 0.95 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do binomial test — binomial_basic","text":"positive_cases number positive instances total_cases total number cases observed p_expected expected probability direction direction test conf_level confidence level confidence interval","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_basic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do binomial test — binomial_basic","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"do binomial testing — binomial_feature_enrichment","title":"do binomial testing — binomial_feature_enrichment","text":"binomial testing","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do binomial testing — binomial_feature_enrichment","text":"","code":"binomial_feature_enrichment( binomial_features, p_expected = 0.5, direction = \"two.sided\", p_adjust = \"BH\", conf_level = 0.95, min_features = 1 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do binomial testing — binomial_feature_enrichment","text":"binomial_features binomial_features object p_expected expected probability (default 0.5) direction direction enrichment (two.sided, less, greater) p_adjust correct p-values (default \"BH\") conf_level confidence level confidence interval (default 0.95) min_features minimum number features annotated annotation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_feature_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do binomial testing — binomial_feature_enrichment","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_features-class.html","id":null,"dir":"Reference","previous_headings":"","what":"binomial feature class — binomial_features-class","title":"binomial feature class — binomial_features-class","text":"class hold features undergoing binomail statistical testing","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_features-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"binomial feature class — binomial_features-class","text":"positivefc features positive fold-changes negativefc features negative fold-changes annotation annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_result-class.html","id":null,"dir":"Reference","previous_headings":"","what":"the binomial results class — binomial_result-class","title":"the binomial results class — binomial_result-class","text":"binomial results class","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/binomial_result-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"the binomial results class — binomial_result-class","text":"positivefc positive log-fold-changed genes, vector class negativefc negative log-fold-changed genes annotation list giving annotation feature relationship statistics statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/categoryCompare2.html","id":null,"dir":"Reference","previous_headings":"","what":"categoryCompare2: A package for comparing enrichment results from multiple experiments — categoryCompare2","title":"categoryCompare2: A package for comparing enrichment results from multiple experiments — categoryCompare2","text":"categoryCompare2 package provides functions simple enrichment comparison enrichment results.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"cc_graph — cc_graph","title":"cc_graph — cc_graph","text":"cc_graph class graphNEL added slot significant, matrix rows (nodes / annotations) whether found significant given enrichment (columns). matrix used classifying annotations different groups, generating either pie-charts coloring nodes visualization. constructs cc_graph given graphNEL significant matrix.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cc_graph — cc_graph","text":"","code":"cc_graph(graph, significant) # S4 method for class 'cc_graph' show(object) cc_graph(graph, significant)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cc_graph — cc_graph","text":"graph graphNEL significant matrix indicating nodes significant experiment object cc_graph show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/cc_graph.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"cc_graph — cc_graph","text":"significant numeric matrix ones zeros","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":null,"dir":"Reference","previous_headings":"","what":"combine annotation-features — combine_annotation_features","title":"combine annotation-features — combine_annotation_features","text":"generation proper annotation-annotation relationship graph, need combine annotation-feature relationships across multiple annotation objects","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine annotation-features — combine_annotation_features","text":"","code":"combine_annotation_features(annotation_features)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine annotation-features — combine_annotation_features","text":"annotation_features list annotation_features combine","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotation_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine annotation-features — combine_annotation_features","text":"list combined annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":null,"dir":"Reference","previous_headings":"","what":"combine annotations — combine_annotations","title":"combine annotations — combine_annotations","text":"Takes multiple annotation objects combines consistent sole set creating cc_graph providing information annotation entry.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine annotations — combine_annotations","text":"","code":"combine_annotations(annotation_list) # S4 method for class 'list' combine_annotations(annotation_list)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine annotations — combine_annotations","text":"annotation_list one annotation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_annotations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine annotations — combine_annotations","text":"annotation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":null,"dir":"Reference","previous_headings":"","what":"combine enrichments — combine_enrichments","title":"combine enrichments — combine_enrichments","text":"one primary workhorse functions behind categoryCompare2. primary function categoryCompare enable comparisons different enrichment analyses. facilitate , must first combine one (really, can single) enriched_result.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine enrichments — combine_enrichments","text":"","code":"combine_enrichments(...) # S4 method for class 'enriched_result' combine_enrichments(...) # S4 method for class 'list' combine_enrichments(...)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine enrichments — combine_enrichments","text":"... list enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_enrichments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine enrichments — combine_enrichments","text":"combined_enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":null,"dir":"Reference","previous_headings":"","what":"combine text — combine_text","title":"combine text — combine_text","text":"Given lists named character objects, character vector names final object, either get character string list names, check character string across lists.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine text — combine_text","text":"","code":"combine_text(list_characters, names_out, text_id)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine text — combine_text","text":"list_characters list containing named character strings names_out full list names use text_id name thing put ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combine_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine text — combine_text","text":"named character vector","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":null,"dir":"Reference","previous_headings":"","what":"combined coefficient — combined_coefficient","title":"combined coefficient — combined_coefficient","text":"takes average overlap jaccard coefficients","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combined coefficient — combined_coefficient","text":"","code":"combined_coefficient(n1, n2)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combined coefficient — combined_coefficient","text":"n1 group 1 n2 group 2","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_coefficient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combined coefficient — combined_coefficient","text":"double","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"combined enrichments — combined_enrichment-class","title":"combined enrichments — combined_enrichment-class","text":"combined_enrichment class holds results combining several enriched_results together, includes original enriched_results, well cc_graph combined annotation objects.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_enrichment.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"combined enrichments — combined_enrichment-class","text":"enriched list enriched objects annotation annotation annotation_features combined across enriched_result statistics combined_statistics ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":null,"dir":"Reference","previous_headings":"","what":"get significant annotations calls — combined_significant_calls","title":"get significant annotations calls — combined_significant_calls","text":"case combined_enrichment want get significant annotations , put together can start real meta-analysis.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get significant annotations calls — combined_significant_calls","text":"","code":"combined_significant_calls(in_results, queries)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get significant annotations calls — combined_significant_calls","text":"in_results combined_enrichment object queries list queries can form call object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get significant annotations calls — combined_significant_calls","text":"combined_enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_significant_calls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get significant annotations calls — combined_significant_calls","text":"Note function returns original combined_enrichment object modified combined_statistics slot significant annotations added .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":null,"dir":"Reference","previous_headings":"","what":"combined statistics — combined_statistics","title":"combined statistics — combined_statistics","text":"holds results extracting bunch statistics combined_enrichment one entity. useful want enable multiple data representations simple filtering actual data.frame statistics, provides flexibility enable . constructor function combined_statistics object, makes sure empty things get initialized correctly","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combined statistics — combined_statistics","text":"","code":"combined_statistics( statistic_data, which_enrichment, which_statistic, annotation_id, significant = NULL, measured = NULL, use_names = NULL ) combined_statistics( statistic_data, which_enrichment, which_statistic, annotation_id, significant = NULL, measured = NULL, use_names = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combined statistics — combined_statistics","text":"statistic_data data.frame statistics which_enrichment enrichment gave results which_statistic statistics calculated case annotation_id annotations returning statistics significant significant annotations measured measured annotations use_names order naming","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combined statistics — combined_statistics","text":"combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/combined_statistics.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"combined statistics — combined_statistics","text":"statistic_data data.frame statistics enrichments significant significant_annotations object, may empty which_enrichment vector giving enrichment column statistics came which_statistic vector providing statistic column contains","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/csv_annotation_table.html","id":null,"dir":"Reference","previous_headings":"","what":"print table csv — csv_annotation_table","title":"print table csv — csv_annotation_table","text":"print annotation gene table CSV file","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/csv_annotation_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print table csv — csv_annotation_table","text":"","code":"csv_annotation_table(annotation_gene_table, out_file = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/csv_annotation_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print table csv — csv_annotation_table","text":"annotation_gene_table list tables out_file file write ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":null,"dir":"Reference","previous_headings":"","what":"the enriched results class — enriched_result","title":"the enriched results class — enriched_result","text":"given slots enriched_result, checks data self-consistent, creates enriched_result object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"the enriched results class — enriched_result","text":"","code":"enriched_result(features, universe, annotation, statistics) enriched_result(features, universe, annotation, statistics)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"the enriched results class — enriched_result","text":"features features differentially expressed (see details) universe features measured annotation annotation object statistics statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"the enriched results class — enriched_result","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_result.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"the enriched results class — enriched_result","text":"features \"features\" interest, vector class universe \"features\" background annotation list giving annotation feature relationship statistics statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":null,"dir":"Reference","previous_headings":"","what":"convert enriched object — enriched_to_fgsea","title":"convert enriched object — enriched_to_fgsea","text":"Takes `enriched_result`, converts table expected `fgsea`. done `gsea` *Enrichment Method*.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"convert enriched object — enriched_to_fgsea","text":"","code":"enriched_to_fgsea(in_enriched)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"convert enriched object — enriched_to_fgsea","text":"in_enriched enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/enriched_to_fgsea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"convert enriched object — enriched_to_fgsea","text":"data.table","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/executable_path.html","id":null,"dir":"Reference","previous_headings":"","what":"executable path — executable_path","title":"executable path — executable_path","text":"Show path executables, user can add whatever want.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/executable_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"executable path — executable_path","text":"","code":"executable_path()"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"extract enrich stats — extract_enrich_stats","title":"extract enrich stats — extract_enrich_stats","text":"Extract statistical table single enrichment object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract enrich stats — extract_enrich_stats","text":"","code":"extract_enrich_stats(enrichment_result)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract enrich stats — extract_enrich_stats","text":"enrichment_result enrichment result object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_enrich_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract enrich stats — extract_enrich_stats","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":null,"dir":"Reference","previous_headings":"","what":"extract statistics — extract_statistics,combined_enrichment-method","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"extract statistics combined_enrichment object create combined_statistics statistic underlying statistical_results object enrichments named according enrichment statistic .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"","code":"# S4 method for class 'combined_enrichment' extract_statistics(in_results)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"in_results combined_enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics-combined_enrichment-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract statistics — extract_statistics,combined_enrichment-method","text":"combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":null,"dir":"Reference","previous_headings":"","what":"get statistics — extract_statistics","title":"get statistics — extract_statistics","text":"extract statistics statistical_results object. can combined data.frame can returned used annotate graph annotations.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get statistics — extract_statistics","text":"","code":"extract_statistics(in_results) # S4 method for class 'statistical_results' extract_statistics(in_results)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get statistics — extract_statistics","text":"in_results statistical_results object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/extract_statistics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get statistics — extract_statistics","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"filter graph by significant entries — filter_annotation_graph","title":"filter graph by significant entries — filter_annotation_graph","text":"graph already generated, may faster filter previously generated one generate new one significant data.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"filter graph by significant entries — filter_annotation_graph","text":"","code":"filter_annotation_graph(in_graph, comb_enrich)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"filter graph by significant entries — filter_annotation_graph","text":"in_graph cc_graph previously generated comb_enrich combined_enrichment want use filter ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/filter_annotation_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"filter graph by significant entries — filter_annotation_graph","text":"cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"generate annotation graph — generate_annotation_graph","title":"generate annotation graph — generate_annotation_graph","text":"given combined_enrichment, generate annotation similarity graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate annotation graph — generate_annotation_graph","text":"","code":"generate_annotation_graph( comb_enrichment, annotation_similarity = \"combined\", low_cut = 5, hi_cut = 500 ) # S4 method for class 'combined_enrichment' generate_annotation_graph( comb_enrichment, annotation_similarity = \"combined\", low_cut = 5, hi_cut = 500 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate annotation graph — generate_annotation_graph","text":"comb_enrichment combined_enrichment object annotation_similarity similarity measure use low_cut keep annotations graph least many annotated features hi_cut keep annotations less many annotated features","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate annotation graph — generate_annotation_graph","text":"cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation similarity graph — generate_annotation_similarity_graph","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"given annotation-feature list, generate similarity graph annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"","code":"generate_annotation_similarity_graph( annotation_features, similarity_type = \"combined\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"annotation_features list entry set features annotation similarity_type type overlap coefficient report","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_annotation_similarity_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation similarity graph — generate_annotation_similarity_graph","text":"cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_colors.html","id":null,"dir":"Reference","previous_headings":"","what":"generate colors — generate_colors","title":"generate colors — generate_colors","text":"given bunch items, generate set colors either single node colorings pie-chart annotations. Colors generated using hcl colorspace, n_color >= 5, colors re-ordered attempt create largest contrasts colors, result picked circle hcl space.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_colors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate colors — generate_colors","text":"","code":"generate_colors(n_color)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_colors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate colors — generate_colors","text":"n_color many colors generate","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_legend.html","id":null,"dir":"Reference","previous_headings":"","what":"generate a legend — generate_legend","title":"generate a legend — generate_legend","text":"often helps legend displayed reference.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_legend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate a legend — generate_legend","text":"","code":"generate_legend( in_assign, upper_names = TRUE, img = FALSE, width = 800, height = 400, pointsize = 70, ... )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_legend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate a legend — generate_legend","text":"in_assign assign object annotation_combinations upper_names whether make names uppercase easier viewing img base64 encoded data uri returned embedding? width wide image saving image height high pointsize pointsize parameter Cairo, determines textsize image ... parameter pie","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":null,"dir":"Reference","previous_headings":"","what":"generate link text — generate_link","title":"generate link text — generate_link","text":"given named vector links, generate actual html link formatted output html documents","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate link text — generate_link","text":"","code":"generate_link(links)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate link text — generate_link","text":"links vector links","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_link.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate link text — generate_link","text":"character","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":null,"dir":"Reference","previous_headings":"","what":"create piecharts for visualization — generate_piecharts","title":"create piecharts for visualization — generate_piecharts","text":"given group matrix colors experiment, generate pie graphs used glyphs Cytoscape","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"create piecharts for visualization — generate_piecharts","text":"","code":"generate_piecharts(grp_matrix, use_color)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"create piecharts for visualization — generate_piecharts","text":"grp_matrix group matrix use_color colors experiment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"create piecharts for visualization — generate_piecharts","text":"list png files pie graphs","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_piecharts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"create piecharts for visualization — generate_piecharts","text":"exported final version","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":null,"dir":"Reference","previous_headings":"","what":"generate statistical table — generate_table","title":"generate statistical table — generate_table","text":"given combined_enrichment object, get data.frame either investigation add data cc_graph.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate statistical table — generate_table","text":"","code":"generate_table(comb_enrichment, link_type = \"explicit\") # S4 method for class 'combined_enrichment' generate_table(comb_enrichment, link_type = \"explicit\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate statistical table — generate_table","text":"comb_enrichment combined_enrichment object link_type \"explicit\" link (see details)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate statistical table — generate_table","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/generate_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"generate statistical table — generate_table","text":"link_type controls whether create \"explicit\" link actually column data.frame, create \"implicit\" html link part @name column returned data.frame. Useful embedding data.frame html report.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"orgdb annotations — get_db_annotation","title":"orgdb annotations — get_db_annotation","text":"Generate annotation object genes based \"org.*.db\" object, pulling information .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"orgdb annotations — get_db_annotation","text":"","code":"get_db_annotation( orgdb = \"org.Hs.eg.db\", features = NULL, feature_type = \"ENTREZID\", annotation_type = \"GO\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"orgdb annotations — get_db_annotation","text":"orgdb name org.*.db object features features get annotations feature_type type IDs map (see details) annotation_type type annotation grab (see details)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"orgdb annotations — get_db_annotation","text":"annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_db_annotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"orgdb annotations — get_db_annotation","text":"function generates categoryCompare2 annotation object Bioconductor \"org.*.db\" object. Even though different gene identifiers can used, almost mappings via ENTREZID. set feature gene keys can used create annotations include: ENTREZID: ENTREZ gene ids ACCNUM: genbank accession numbers SYMBOL: gene symbols, eg ABCA1 GENENAME: gene names, eg \"ATP binding cassette subfamily member 1\" ENSEMBL: ensembl gene ids (start ENSG...) ENSEMBLPROT: ensembl protein ids (ENSP...) ENSEMBLTRANS: ensemlb transcript ids (ENST...) REFSEQ: reference sequence IDs, NM, NP, NR, XP, etc UNIGENE: gene ids UNIPROT eg Hs.88556 UNIPROT: protein ids UNIPROT eg P80404 set annotations can mapped features include: GO: annotations gene ontology PATH: KEGG Pathway identifiers (updated since 2011!) CHRLOC: location chromosome OMIM: mendelian inheritance man identifiers PMID: pubmed identifiers PROSITE PFAM: protein family identifiers IPI: protein-protein interactions GO annotations, also possible pass GO use 3 sub-ontologies simultaneously, combination BP, MF, CC.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":null,"dir":"Reference","previous_headings":"","what":"get significant annotations — get_significant_annotations","title":"get significant annotations — get_significant_annotations","text":"given statistical_results object conditional expressions, return significant annotations case combined_enrichment want get significant annotations , put together can start real meta-analysis.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get significant annotations — get_significant_annotations","text":"","code":"get_significant_annotations(in_results, ...) # S4 method for class 'statistical_results' get_significant_annotations(in_results, ...) # S4 method for class 'combined_enrichment' get_significant_annotations(in_results, ...)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get significant annotations — get_significant_annotations","text":"in_results combined_enrichment object ... conditional expressions","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get significant annotations — get_significant_annotations","text":"vector significant annotation_id's combined_enrichment object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get significant annotations — get_significant_annotations","text":"Note function returns original combined_enrichment object modified combined_statistics slot significant annotations added .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get significant annotations — get_significant_annotations","text":"","code":"test_stat <- new(\"statistical_results\", annotation_id = c(\"a1\", \"a2\", \"a3\"), statistic_data = list(pvalues = c(a1 = 0.01, a2 = 0.5, a3 = 0.0001), counts = c(a1 = 5, a2 = 10, a3 = 1), odds = c(a1 = 20, a2 = 100, a3 = 0))) get_significant_annotations(test_stat, pvalues < 0.05) #> [1] \"a1\" \"a3\" get_significant_annotations(test_stat, odds > 10) #> [1] \"a1\" \"a2\" get_significant_annotations(test_stat, pvalues < 0.05, counts >= 1) #> [1] \"a1\" \"a3\""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":null,"dir":"Reference","previous_headings":"","what":"get significant annotations calls — get_significant_annotations_calls","title":"get significant annotations calls — get_significant_annotations_calls","text":"case statistical_results want get significant annotations ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get significant annotations calls — get_significant_annotations_calls","text":"","code":"get_significant_annotations_calls(in_results, queries)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get significant annotations calls — get_significant_annotations_calls","text":"in_results statistical_results object queries list queries can form call object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/get_significant_annotations_calls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get significant annotations calls — get_significant_annotations_calls","text":"vector significant annotation_id's","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"gocats to annnotations — gocats_to_annotation","title":"gocats to annnotations — gocats_to_annotation","text":"Transforms gocats ancestors JSON list GO annotation object.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"gocats to annnotations — gocats_to_annotation","text":"","code":"gocats_to_annotation( ancestors_file = \"ancestors.json\", namespace_file = \"namespace.json\", annotation_type = \"gocatsGO\", feature_type = \"Uniprot\", feature_translation = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"gocats to annnotations — gocats_to_annotation","text":"ancestors_file ancestors.json file gocats (required) namespace_file namespace.json file gocats (optional) annotation_type annotations making? (gocatsGO default) feature_type type features using (assume Uniprot) feature_translation data.frame used convert feature IDs","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gocats_to_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"gocats to annnotations — gocats_to_annotation","text":"annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"cc_graph to visnetwork — graph_to_visnetwork","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"takes cc_graph object transforms something can visualized using visNetwork","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"","code":"graph_to_visnetwork( in_graph, in_assign, node_communities = NULL, use_nodes = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"in_graph cc_graph object in_assign colors generated assign_colors node_communities communities generated label_communities use_nodes list nodes actually use","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/graph_to_visnetwork.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cc_graph to visnetwork — graph_to_visnetwork","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"do GSEA — gsea_feature_enrichment","title":"do GSEA — gsea_feature_enrichment","text":"Performs gene-set enrichment analysis using `fgsea` package.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do GSEA — gsea_feature_enrichment","text":"","code":"gsea_feature_enrichment( gsea_features, min_features = 15, max_features = 500, return_type = \"cc2\", ... )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do GSEA — gsea_feature_enrichment","text":"gsea_features GSEA features object min_features minimum number features annotation (default = 15) max_features maximum number features annotation (default = 500) return_type type object returned? (\"cc2\" \"fgsea\") ... `fgsea` options","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do GSEA — gsea_feature_enrichment","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_feature_enrichment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"do GSEA — gsea_feature_enrichment","text":"runtime dependent maximum size provided annotation, authors `fgsea` recommend maximum size 500. addition, calculate statistics, minimum size annotated features required. Going 15 may advised. want use `fgsea` functions, recommended set `return_type = \"fgsea\"`. Otherwise, keep default \"cc2\".","code":""},{"path":[]},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_features-class.html","id":null,"dir":"Reference","previous_headings":"","what":"GSEA feature class — gsea_features-class","title":"GSEA feature class — gsea_features-class","text":"class hold features undergoing GSEA","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/gsea_features-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"GSEA feature class — gsea_features-class","text":"ranks named vector ranks annotation annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeom_features-class.html","id":null,"dir":"Reference","previous_headings":"","what":"hypergeom feature class — hypergeom_features-class","title":"hypergeom feature class — hypergeom_features-class","text":"class hold features undergoing hypergeometric enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeom_features-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"hypergeom feature class — hypergeom_features-class","text":"significant significant features universe features measured annotation annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":null,"dir":"Reference","previous_headings":"","what":"do hypergeometric test — hypergeometric_basic","title":"do hypergeometric test — hypergeometric_basic","text":"hypergeometric enrichment test","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do hypergeometric test — hypergeometric_basic","text":"","code":"hypergeometric_basic( num_white, num_black, num_drawn, num_white_drawn, direction = \"over\" )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do hypergeometric test — hypergeometric_basic","text":"num_white number white balls urn num_black number black balls urn num_drawn number balls taken urn num_white_drawn number white balls taken urn direction direction test","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_basic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do hypergeometric test — hypergeometric_basic","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"do hypergeometric enrichment — hypergeometric_feature_enrichment","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"hypergeometric enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"","code":"hypergeometric_feature_enrichment( hypergeom_features, direction = \"over\", p_adjust = \"BH\", min_features = 1 )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"hypergeom_features hypergeometric_features object direction direction enrichment () p_adjust correct p-values (default \"BH\") min_features many features annotated testing ?","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/hypergeometric_feature_enrichment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"do hypergeometric enrichment — hypergeometric_feature_enrichment","text":"min_features argument applies minumum number features annotation universe features supplied, minumum number features differential list. p-value adjustment, see stats::p.adjust","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":null,"dir":"Reference","previous_headings":"","what":"install executables — install_executables","title":"install executables — install_executables","text":"move executables user location, default ~/bin changes permissions make executable.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"install executables — install_executables","text":"","code":"install_executables(path = \"~/bin\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"install executables — install_executables","text":"path path put executable scripts","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/install_executables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"install executables — install_executables","text":"listing files.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":null,"dir":"Reference","previous_headings":"","what":"jaccard coefficient — jaccard_coefficient","title":"jaccard coefficient — jaccard_coefficient","text":"calculates similarity two groups objects using \"jaccard\" coefficient, defined :","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"jaccard coefficient — jaccard_coefficient","text":"","code":"jaccard_coefficient(n1, n2)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"jaccard coefficient — jaccard_coefficient","text":"n1 group 1 n2 group 2","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"jaccard coefficient — jaccard_coefficient","text":"double","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/jaccard_coefficient.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"jaccard coefficient — jaccard_coefficient","text":"length(intersect(n1, n2)) / min(c(length(n1), length(n2)))","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"json to annotation — json_2_annotation","title":"json to annotation — json_2_annotation","text":"Given JSON based annotation object, read create `annotation` actually enrichment.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"json to annotation — json_2_annotation","text":"","code":"json_2_annotation(json_file)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"json to annotation — json_2_annotation","text":"json_file json annotation file","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_2_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"json to annotation — json_2_annotation","text":"annotation object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":null,"dir":"Reference","previous_headings":"","what":"annotation reversal — json_annotation_reversal","title":"annotation reversal — json_annotation_reversal","text":"Given JSON file features annotations, reverse turn annotations features, optionally add meta-information .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"annotation reversal — json_annotation_reversal","text":"","code":"json_annotation_reversal( json_file, out_file = \"annotations.json\", feature_type = NULL, annotation_type = NULL )"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"annotation reversal — json_annotation_reversal","text":"json_file json file use out_file json file write feature_type type features annotation_type type annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/json_annotation_reversal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"annotation reversal — json_annotation_reversal","text":"json object, invisibly","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":null,"dir":"Reference","previous_headings":"","what":"print table kable — kable_annotation_table","title":"print table kable — kable_annotation_table","text":"print annotation gene table knitr::kable format","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print table kable — kable_annotation_table","text":"","code":"kable_annotation_table(annotation_gene_table, header_level = 3, cat = TRUE)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print table kable — kable_annotation_table","text":"annotation_gene_table list tables header_level header level labels done ? cat whether write directly, just return table later","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/kable_annotation_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print table kable — kable_annotation_table","text":"character","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":null,"dir":"Reference","previous_headings":"","what":"label communities — label_communities","title":"label communities — label_communities","text":"Determine label community based generic member community, defined one annotations.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"label communities — label_communities","text":"","code":"label_communities(community_defs, annotation)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"label communities — label_communities","text":"community_defs communities assign_communities annotation annotation object used enrichment","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/label_communities.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"label communities — label_communities","text":"list","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":null,"dir":"Reference","previous_headings":"","what":"index a list — multi_query_list","title":"index a list — multi_query_list","text":"Provided list, condition, returns logical indices named part list provided. Uses subset like non-standard evaluation can define appropriate expressions.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"index a list — multi_query_list","text":"","code":"multi_query_list(list_to_query, ...)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"index a list — multi_query_list","text":"list_to_query list run query ... expressions queries","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/multi_query_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"index a list — multi_query_list","text":"logical \"&\" queries","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/node_assign-class.html","id":null,"dir":"Reference","previous_headings":"","what":"node_assign — node_assign-class","title":"node_assign — node_assign-class","text":"node_assign class holds unique annotation combinations assignment nodes combinations use visualization.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/node_assign-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"node_assign — node_assign-class","text":"groups unique groups, logical matrix assignments named character vector providing association groups description named character vector providing description group colors named character vector hex colors groups experiments color_type whether group experiment based colors pie_locs experiment colors, pie graphs generated ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":null,"dir":"Reference","previous_headings":"","what":"overlap coefficient — overlap_coefficient","title":"overlap coefficient — overlap_coefficient","text":"calculates similarity using \"overlap\" coefficient, ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"overlap coefficient — overlap_coefficient","text":"","code":"overlap_coefficient(n1, n2)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"overlap coefficient — overlap_coefficient","text":"n1 group 1 objects n2 group 2 objects","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"overlap coefficient — overlap_coefficient","text":"double","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/overlap_coefficient.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"overlap coefficient — overlap_coefficient","text":"length(intersect(n1, n2)) / length(union(n1, n2))","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":null,"dir":"Reference","previous_headings":"","what":"remove edges — remove_edges","title":"remove edges — remove_edges","text":"given RCy3 network connection, remove edges according provided values.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"remove edges — remove_edges","text":"","code":"remove_edges(edge_obj, cutoff, edge_attr = \"weight\", value_direction = \"under\") # S4 method for class 'character,numeric' remove_edges(edge_obj, cutoff, edge_attr = \"weight\", value_direction = \"under\") # S4 method for class 'cc_graph,numeric' remove_edges(edge_obj, cutoff, edge_attr = \"weight\", value_direction = \"under\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"remove edges — remove_edges","text":"edge_obj cc_graph cutoff cutoff use edge_attr attribute use value_direction remove edges value ","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/remove_edges.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"remove edges — remove_edges","text":"nothing cc_graph","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-binomial_result-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show binomial_result — show,binomial_result-method","title":"show binomial_result — show,binomial_result-method","text":"show binomial_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-binomial_result-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show binomial_result — show,binomial_result-method","text":"","code":"# S4 method for class 'binomial_result' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-binomial_result-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show binomial_result — show,binomial_result-method","text":"object binomial_result object show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-combined_statistics-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show combined_statistics — show,combined_statistics-method","title":"show combined_statistics — show,combined_statistics-method","text":"show combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-combined_statistics-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show combined_statistics — show,combined_statistics-method","text":"","code":"# S4 method for class 'combined_statistics' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-combined_statistics-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show combined_statistics — show,combined_statistics-method","text":"object combined_statistics","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-enriched_result-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show enriched_result — show,enriched_result-method","title":"show enriched_result — show,enriched_result-method","text":"show enriched_result","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-enriched_result-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show enriched_result — show,enriched_result-method","text":"","code":"# S4 method for class 'enriched_result' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-enriched_result-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show enriched_result — show,enriched_result-method","text":"object enriched_result object show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-node_assign-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show node_assign — show,node_assign-method","title":"show node_assign — show,node_assign-method","text":"show node_assign","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-node_assign-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show node_assign — show,node_assign-method","text":"","code":"# S4 method for class 'node_assign' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-node_assign-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show node_assign — show,node_assign-method","text":"object node_assign see","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-significant_annotations-method.html","id":null,"dir":"Reference","previous_headings":"","what":"show signficant_annotations — show,significant_annotations-method","title":"show signficant_annotations — show,significant_annotations-method","text":"show signficant_annotations","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-significant_annotations-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show signficant_annotations — show,significant_annotations-method","text":"","code":"# S4 method for class 'significant_annotations' show(object)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/show-significant_annotations-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show signficant_annotations — show,significant_annotations-method","text":"object significant annotations object show","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":null,"dir":"Reference","previous_headings":"","what":"significant annotations — significant_annotations","title":"significant annotations — significant_annotations","text":"significant_annotations class holds annotations enrichment measured significant. slots logical matrix rows named annotation_id columns named names enriched_result combined. Makes new significant_annotation checking everything valid.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"significant annotations — significant_annotations","text":"","code":"significant_annotations(significant, measured, sig_calls = NULL) significant_annotations(significant, measured, sig_calls = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"significant annotations — significant_annotations","text":"significant logical matrix annotations (rows) experiments (columns) measured logical matrix annotations (rows) experiments (columns) sig_calls character vector deparsed calls resulted signficant measured","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/significant_annotations.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"significant annotations — significant_annotations","text":"significant logical matrix measured logical matrix sig_calls character representations calls used filter data","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/statistical_results-class.html","id":null,"dir":"Reference","previous_headings":"","what":"statistical results class — statistical_results-class","title":"statistical results class — statistical_results-class","text":"class holds part enrichment statistical results. two pieces, list statistics named list actual numerical results applying statistics. piece annotation_id vector defining entry vector statistics .","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/statistical_results-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"statistical results class — statistical_results-class","text":"statistic_data list numerical statistics annotation_id vector ids method statistics calculated","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"table from graph — table_from_graph","title":"table from graph — table_from_graph","text":"Creates table annotation graph, provided, adds community information table.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"table from graph — table_from_graph","text":"","code":"table_from_graph(in_graph, in_assign = NULL, community_info = NULL)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"table from graph — table_from_graph","text":"in_graph cc_graph object in_assign node_assign object community_info community_info object","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/table_from_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"table from graph — table_from_graph","text":"data.frame","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":null,"dir":"Reference","previous_headings":"","what":"visualize in cytoscape — vis_in_cytoscape","title":"visualize in cytoscape — vis_in_cytoscape","text":"given graph, node assignments, visualize graph cytoscape manipulation","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"visualize in cytoscape — vis_in_cytoscape","text":"","code":"vis_in_cytoscape(in_graph, in_assign, description = \"cc2 enrichment\")"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"visualize in cytoscape — vis_in_cytoscape","text":"in_graph cc_graph visualize in_assign node_assign generated description something descriptive vis (useful lots different visualizations)","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_in_cytoscape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"visualize in cytoscape — vis_in_cytoscape","text":"something","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_visnetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"vis in visNetwork — vis_visnetwork","title":"vis in visNetwork — vis_visnetwork","text":"Visualize cc_graph visNetwork, selection communities exists.","code":""},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_visnetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"vis in visNetwork — vis_visnetwork","text":"","code":"vis_visnetwork(in_graph_info)"},{"path":"https://moseleybioinformaticslab.github.io/categoryCompare2/reference/vis_visnetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"vis in visNetwork — vis_visnetwork","text":"in_graph_info graph structure graph_to_visnetwork","code":""},{"path":[]},{"path":[]}]
diff --git a/man/gsea_feature_enrichment.Rd b/man/gsea_feature_enrichment.Rd
index c04adcf..089e438 100644
--- a/man/gsea_feature_enrichment.Rd
+++ b/man/gsea_feature_enrichment.Rd
@@ -20,6 +20,8 @@ gsea_feature_enrichment(
\item{max_features}{the maximum number of features for an annotation (default = 500)}
\item{return_type}{what type of object should be returned? ("cc2" or "fgsea")}
+
+\item{...}{other `fgsea` options}
}
\value{
enriched_result
diff --git a/test_data_gen.R b/test_data_gen.R
deleted file mode 100644
index 8703eae..0000000
--- a/test_data_gen.R
+++ /dev/null
@@ -1,99 +0,0 @@
-# test data generation for v2 enrichment objects
-
-# because I am concentrating on combining objects first, I need some actual
-# enrichment data that can be used
-#
-# this script generates test data that we can subsequently use for generating and
-# combining objects
-
-library("affy")
-library("hgu95av2.db")
-library("genefilter")
-library("estrogen")
-library("limma")
-library("GOstats")
-#library("Category")
-
-
-datadir <- system.file("extdata", package = "estrogen")
-pd <- read.AnnotatedDataFrame(file.path(datadir,"estrogen.txt"),
- header = TRUE, sep = "", row.names = 1)
-currDir <- getwd()
-setwd(datadir)
-a <- ReadAffy(filenames=rownames(pData(pd)), phenoData = pd, verbose = TRUE)
-setwd(currDir)
-eData <- rma(a)
-
-e10 <- eData[, eData$time.h == 10]
-e10 <- nsFilter(e10, remove.dupEntrez=TRUE, var.filter=FALSE,
- feature.exclude="^AFFX")$eset
-
-e10$estrogen <- factor(e10$estrogen)
-d10 <- model.matrix(~0 + e10$estrogen)
-colnames(d10) <- unique(e10$estrogen)
-fit10 <- lmFit(e10, d10)
-c10 <- makeContrasts(present - absent, levels=d10)
-fit10_2 <- contrasts.fit(fit10, c10)
-eB10 <- eBayes(fit10_2)
-table10 <- topTable(eB10, number=nrow(e10), p.value=1, adjust.method="BH")
-table10$Entrez <- unlist(mget(rownames(table10), hgu95av2ENTREZID, ifnotfound=NA))
-
-e48 <- eData[, eData$time.h == 48]
-e48 <- nsFilter(e48, remove.dupEntrez=TRUE, var.filter=FALSE,
- feature.exclude="^AFFX" )$eset
-
-e48$estrogen <- factor(e48$estrogen)
-d48 <- model.matrix(~0 + e48$estrogen)
-colnames(d48) <- unique(e48$estrogen)
-fit48 <- lmFit(e48, d48)
-c48 <- makeContrasts(present - absent, levels=d48)
-fit48_2 <- contrasts.fit(fit48, c48)
-eB48 <- eBayes(fit48_2)
-table48 <- topTable(eB48, number=nrow(e48), p.value=1, adjust.method="BH")
-table48$Entrez <- unlist(mget(rownames(table48), hgu95av2ENTREZID, ifnotfound=NA))
-
-gUniverse <- unique(union(table10$Entrez, table48$Entrez))
-
-g10 <- unique(table10$Entrez[table10$adj.P.Val < 0.05])
-g48 <- unique(table48$Entrez[table48$adj.P.Val < 0.05])
-
-g10_hyper <- new("GOHyperGParams", geneIds = g10,
- universeGeneIds = gUniverse,
- annotation = "org.Hs.eg.db",
- ontology = "BP",
- pvalueCutoff = 1.0)
-
-g10_enrich <- hyperGTest(g10_hyper)
-
-g48_hyper <- new("GOHyperGParams", geneIds = g48,
- universeGeneIds = gUniverse,
- annotation = "org.Hs.eg.db",
- ontology = "BP",
- pvalueCutoff = 1.0)
-g48_enrich <- hyperGTest(g48_hyper)
-g48_counts <- geneCounts(g48_enrich)
-g48_pvals <- pvalues(g48_enrich)
-
-bp_annotation <- select(org.Hs.eg.db, keys = gUniverse, keytype = "ENTREZID", columns = "GOALL")
-bp_annotation <- bp_annotation[(bp_annotation$ONTOLOGYALL %in% "BP"),]
-
-bp_annotation <- split(bp_annotation$ENTREZID, bp_annotation$GOALL)
-bp_annotation <- lapply(bp_annotation, unique)
-
-estrogen_10_enrichment <- list(features = g10,
- universe = gUniverse,
- annotation = bp_annotation,
- enrich = list(pvalues = pvalues(g10_enrich),
- odds_ratio = oddsRatios(g10_enrich),
- counts = geneCounts(g10_enrich),
- stat_names = names(pvalues(g10_enrich))))
-
-estrogen_48_enrichment <- list(features = g48,
- universe = gUniverse,
- annotation = bp_annotation,
- enrich = list(pvalues = pvalues(g48_enrich),
- odds_ratio = oddsRatios(g48_enrich),
- counts = geneCounts(g48_enrich),
- stat_names = names(pvalues(g48_enrich))))
-
-save(estrogen_10_enrichment, estrogen_48_enrichment, file = "test_data.RData")
diff --git a/test_data_gen2.R b/test_data_gen2.R
deleted file mode 100644
index 0e1e024..0000000
--- a/test_data_gen2.R
+++ /dev/null
@@ -1,76 +0,0 @@
-# generating much smaller test data
-
-library(GO.db)
-library(GOstats)
-library(org.Hs.eg.db)
-library(magrittr)
-
-set.seed("1234")
-
-# to choose genes, we will actually choose a set of GO terms first
-all_hs_GO <- as.list(org.Hs.egGO2ALLEGS)
-all_hs_GO <- lapply(all_hs_GO, unique)
-
-len_GO <- sapply(all_hs_GO, length)
-
-keep_GO <- (len_GO >= 2) & (len_GO <= 200)
-
-all_hs_GO <- all_hs_GO[keep_GO]
-
-init_GO <- sample(names(all_hs_GO), 200) %>% all_hs_GO[.]
-init_GO_len <- sapply(init_GO, length)
-
-diff_GO <- which((init_GO_len >= 30) & (init_GO_len <= 50))[1]
-keep_genes <- init_GO[[diff_GO]]
-n_keep <- length(keep_genes)
-
-all_genes <- unique(unlist(init_GO, use.names = FALSE))
-
-sample_genes <- all_genes[!(all_genes %in% keep_genes)]
-
-universe_genes <- c(keep_genes, sample(sample_genes, 200 - n_keep))
-
-c1_diff <- sample(keep_genes, 20)
-c2_diff <- sample(keep_genes, 20)
-
-c1_hyper <- new("GOHyperGParams", geneIds = c1_diff,
- universeGeneIds = universe_genes,
- annotation = "org.Hs.eg.db",
- ontology = "BP",
- pvalueCutoff = 1.0)
-c2_hyper <- new("GOHyperGParams", geneIds = c2_diff,
- universeGeneIds = universe_genes,
- annotation = "org.Hs.eg.db",
- ontology = "BP",
- pvalueCutoff = 1.0)
-
-c1_enrich <- hyperGTest(c1_hyper)
-c2_enrich <- hyperGTest(c2_hyper)
-
-
-all_hs_go <- keys(org.Hs.eg.db, keytype = "GOALL")
-all_hs_gotype <- select(GO.db, keys = all_hs_go, columns = "ONTOLOGY")
-all_hs_bp <- all_hs_gotype$GOID[all_hs_gotype$ONTOLOGY == "BP"]
-
-all_hs_bp2gene <- select(org.Hs.eg.db, keys = all_hs_bp, keytype = "GOALL", columns = c("ENTREZID"))
-
-bp_annotation <- split(all_hs_bp2gene$ENTREZID, all_hs_bp2gene$GOALL)
-bp_annotation <- lapply(bp_annotation, unique)
-
-c1_enrichment <- list(features = c1_diff,
- universe = universe_genes,
- annotation = bp_annotation,
- enrich = list(pvalues = pvalues(c1_enrich),
- odds_ratio = oddsRatios(c1_enrich),
- counts = geneCounts(c1_enrich),
- stat_names = names(pvalues(c1_enrich))))
-
-c2_enrichment <- list(features = c2_diff,
- universe = universe_genes,
- annotation = bp_annotation,
- enrich = list(pvalues = pvalues(c2_enrich),
- odds_ratio = oddsRatios(c2_enrich),
- counts = geneCounts(c2_enrich),
- stat_names = names(pvalues(c2_enrich))))
-
-save(c1_enrichment, c2_enrichment, bp_annotation, file = "test_data2.RData")
diff --git a/tests.R b/tests.R
deleted file mode 100644
index 83f85b1..0000000
--- a/tests.R
+++ /dev/null
@@ -1,78 +0,0 @@
-# testing the basic enriched class
-
-library(categoryCompare2)
-library(GO.db)
-library(parallel)
-options(mc.cores = 4)
-load("test_data2.RData")
-
-bp_annotation_obj <- new("annotation",
- annotation_features = bp_annotation,
- description = Term(names(bp_annotation)),
- counts = sapply(bp_annotation, length),
- type = "GO.BP")
-
-c1 <- enriched_result(c1_enrichment$features,
- c1_enrichment$universe,
- bp_annotation_obj,
- new("statistical_results",
- statistic_data = c1_enrichment$enrich[c("pvalues", "odds_ratio", "counts")],
- annotation_id = c1_enrichment$enrich$stat_names))
-
-c2 <- enriched_result(c2_enrichment$features,
- c2_enrichment$universe,
- bp_annotation_obj,
- new("statistical_results",
- statistic_data = c2_enrichment$enrich[c("pvalues", "odds_ratio", "counts")],
- annotation_id = c2_enrichment$enrich$stat_names))
-
-sum(c1@statistics@statistic_data$pvalues <= 0.05)
-sum(c2@statistics@statistic_data$pvalues <= 0.05)
-# get 328 and 307 in C1 and C2, respectively
-
-bp_combined <- combine_enrichments(c1 = c1, c2 = c2)
-
-bp_sig <- get_significant_annotations(bp_combined, pvalues <= 0.05)
-
-bp_graph <- generate_annotation_graph(bp_sig, low_cut = 10) # default graph gen
-
-bp_sig <- get_significant_annotations(bp_combined, pvalues <= 0.05, counts >= 2) # add significant annotations
-
-bp_graph_sig <- generate_annotation_graph(bp_sig) # generate graph from sig annotations
-
-bp_table <- generate_table(bp_combined)
-bp_table_sig <- generate_table(bp_sig)
-
-bp_graph_sig2 <- filter_annotation_graph(bp_graph, bp_sig) # filter a previously generated annotation graph
-
-# check that graphs generated by each method are the same
-library(graph)
-sig1_nodes <- sort(graph::nodes(bp_graph_sig))
-sig2_nodes <- sort(graph::nodes(bp_graph_sig2))
-all.equal(sig1_nodes, sig2_nodes)
-
-sig1_edges <- graph::edges(bp_graph_sig)
-sig2_edges <- graph::edges(bp_graph_sig2)
-all.equal(sig1_edges[sig1_nodes], sig2_edges[sig2_nodes])
-
-sig1_matrix <- bp_graph_sig@significant[sig1_nodes, ]
-sig2_matrix <- bp_graph_sig2@significant[sig2_nodes, ]
-all.equal(sig1_matrix, sig2_matrix)
-
-# visualization
-# determine the unique groups in the significant matrix and generate colors or pie charts
-node_assign <- annotation_combinations(bp_graph_sig)
-node_assign_pie <- assign_colors(node_assign)
-
-node_assign_solid <- assign_colors(node_assign, type = "group")
-
-# pass the graph and the node_assignments off to Cytoscape for visualization
-# test_cy <- vis_in_cytoscape(bp_graph_sig, node_assign_pie)
-# remove_edges(test_cy, 0.9)
-
-# why not remove edges *before* passing to cytoscape to make loading faster
-bp_graph_sig3 <- remove_edges(bp_graph_sig, 0.8)
-node_assign_pie <- annotation_combinations(bp_graph_sig3)
-node_assign_pie <- assign_colors(node_assign_pie)
-test_cy2 <- vis_in_cytoscape(bp_graph_sig3, node_assign_pie, "test2")
-remove_edges(test_cy2, 0.95)
diff --git a/tests/testthat/test-fgsea.R b/tests/testthat/test-fgsea.R
index 39ed2eb..c5089f4 100644
--- a/tests/testthat/test-fgsea.R
+++ b/tests/testthat/test-fgsea.R
@@ -28,7 +28,7 @@ test_that("fgsea works", {
tmp_p = fgsea_cc2@statistics@statistic_data[["p"]]
names(tmp_p) = NULL
- tmp_paths = fgsea_results@statistics@annotation_id
+ tmp_paths = fgsea_cc2@statistics@annotation_id
expect_equal(tmp_paths, fgsea_fgsea[["pathway"]])
expect_equal(tmp_p,