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clusterPathways

Use Jaccard index and constituent genes to identify groups of similar pathways

Installation

remotes::install_github("hancockinformatics/clusterPathways")

Example

> library(clusterPathways)
> library(tidyverse)
# Thanks for using clusterPathways v0.0.45!
# If you encounter any bugs or problems, please submit an issue at the
# Github page: https://github.com/hancockinformatics/clusterPathways
# ── Attaching packages ───────────────────────────────────── tidyverse 1.3.1 ──
# ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
# ✓ tibble  3.1.6     ✓ dplyr   1.0.8
# ✓ tidyr   1.2.0     ✓ stringr 1.4.0
# ✓ readr   2.1.2     ✓ forcats 0.5.1
# ── Conflicts ──────────────────────────────────────── tidyverse_conflicts() ──
# x dplyr::filter() masks stats::filter()
# x dplyr::lag()    masks stats::lag()

> my_pathways <- read_csv("pathway_enrichment_result.csv")
> glimpse(my_pathways)
# Rows: 192
# Columns: 5
# $ id          <chr> "R-HSA-156842", "R-HSA-156902", "R-HSA-192823", "R-HSA-9…
# $ description <chr> "Eukaryotic Translation Elongation", "Peptide chain elon…
# $ direction   <chr> "up", "up", "up", "up", "up", "up", "up", "up", "up", "u…
# $ pvalue      <dbl> 1.400442e-39, 4.774000e-38, 6.119370e-35, 7.946710e-35, …
# $ p_adjust    <dbl> 1.819174e-36, 3.100713e-35, 2.580694e-32, 2.580694e-32, …

> my_genes <- read_csv("de_genes.csv") %>% pull(ensembl_gene_id)
> head(my_genes)
# [1] "ENSG00000100664" "ENSG00000114529" "ENSG00000055147" "ENSG00000186226" 
# [5] "ENSG00000243349" "ENSG00000144136"

> cluster_reactome_pathways(
    input_pathways = my_pathways,
    input_genes    = my_genes,
    species        = "human",
    output_dir     = "clustered_pathways",
    width  = 18,
    height = 30
  )