From 129ce72e94a76f9d5d695272703b5089f602cfbd Mon Sep 17 00:00:00 2001 From: Jack Leary Date: Fri, 17 Nov 2023 10:16:13 -0500 Subject: [PATCH 1/6] updated vignette for v0.7.8 --- vignettes/scLANE.Rmd | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/vignettes/scLANE.Rmd b/vignettes/scLANE.Rmd index 1c51420..63baf25 100644 --- a/vignettes/scLANE.Rmd +++ b/vignettes/scLANE.Rmd @@ -80,14 +80,15 @@ cell_offset <- createCellOffset(sim_data) top1k_hvgs <- getTopHVGs(modelGeneVar(sim_data), n = 1e3) ``` -The default modeling framework is based on GLMs, which we use here. Parallel processing is turned on by default in order to speed things up. +The default modeling framework is based on GLMs, which we use here. Parallel processing is turned on by default in order to speed things up. Setting the `verbose = TRUE` parameter would print a progress bar to the console. ```{r} scLANE_models <- testDynamic(sim_data, pt = order_df, genes = top1k_hvgs, size.factor.offset = cell_offset, - n.cores = 4) + n.cores = 5, + verbose = FALSE) ``` After model fitting has completed, we generate a tidy table of DE test results and statistics using the `getResultsDE()` function. Each gene is given a binary dynamic vs. static classification (denoted as 1 or 0, respectively) over a given lineage / the trajectory as a whole if it's adjusted *p*-value is less than a specified threshold; the default is 0.01. @@ -127,6 +128,15 @@ knitr::kable(scLANE_models[[scLANE_de_res$Gene[1]]]$Lineage_A$Gene_Dynamics, digits = 3) ``` +We can plot the dynamics of a gene of interest along with its coefficients like so: + +```{r} +plotModelCoefs(scLANE_models, + gene = scLANE_de_res$Gene[1], + pt = order_df, + expr.mat = sim_data, + size.factor.offset = cell_offset) +``` ## Gene dynamics plots @@ -198,15 +208,15 @@ col_anno <- HeatmapAnnotation(Leiden = col_anno_df$leiden, Now we can finally plot the heatmap: -```{r, fig.cap="Expression cascade heatmap"} +```{r, fig.cap="Expression cascade of dynamic genes across pseudotime"} Heatmap(matrix = heatmap_mat, - name = "mRNA", + name = "Scaled\nmRNA", col = circlize::colorRamp2(colors = viridis::inferno(50), breaks = seq(min(heatmap_mat), max(heatmap_mat), length.out = 50)), cluster_columns = FALSE, width = 9, height = 6, - column_title = "Dynamic genes across pseudotime", + column_title = "", cluster_rows = FALSE, top_annotation = col_anno, border = TRUE, @@ -269,5 +279,5 @@ filter(dyn_gene_enrichment$result, source == "GO:BP") %>% # Session info ```{r} -sessionInfo() +sessioninfo::session_info() ``` From 2f03b645cbbf64733ad54c8d433ec7dfcce7fe18 Mon Sep 17 00:00:00 2001 From: Jack Leary Date: Fri, 17 Nov 2023 10:16:43 -0500 Subject: [PATCH 2/6] updated included scLANE models dataset --- data/scLANE_models.rda | Bin 1031612 -> 1215780 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/data/scLANE_models.rda b/data/scLANE_models.rda index 9c6f7587c5d6d310e6d4ac47b983957cc9563d81..fbec6828595293ce369b202f97eaafc003ebaddc 100644 GIT binary patch literal 1215780 zcmd>kWmH_%ODA z@4c_z8$IriH@eTSz2=#Ft~J;CbH?6>DjMa@zdi6LFl0^5)tNhVFLjA`KP1#RLvU~i 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zA7Gg?lA0=2rI3zb^^F8MVqHvPbC~d|`f30Ud3b9sO>e1?WK-Rf=wcQBnZneZvBD0@ z!ziW?E(Vs!wI%u|)LndWS%$ceBZg^K_V*<9)BfkJz2DXKG5}xw7IErD(#Nwop@0LX zZ=4T1hnqM&xD*?G5$~Qbi9pBk{!b8RPnCA)xZqJ#cti3+PfKFrGz+U$V(e|+W2C0B z3(1)13FjSoq&86_q>P6HQ1Qe=9nsMyNAiO-V9(!11ON_L$eA`DgxJy#mo}#}Fxdiw zU5%_jXUiNvB{$=2*}Pbg>y|nibj+N}S5AgG__+l^uxLE%y`(+0A&g}J4*nHD+=nm= zWfA=@BGFGam}4i2pB1f!hs%-g0nIGRU8!nYc-PK%W*?jm&axVx{o*Pz7`>%)!XQve z&8N`($&76K`PCaL7T4Zg%DbwPx`%g~SqE}?ujCahIZ@>xQP>c8K?P=pBnWqsO`eD; z%%2w76>3q>XJJ2M*az|GqVmGqcjMJFoH1 zO0>a1m-MFcP`}$%00N}V`RS1J>;WGc=~ASN;!L_O z1BY7?>-y9!?F|-{5eton9WE)`9bcxpHOcAHz=%0J&W)yPM-tKmz~(00045T8aeFa{vGU From 9e7ed17b261f7771dd22a62fff499a073170599a Mon Sep 17 00:00:00 2001 From: Jack Leary Date: Fri, 17 Nov 2023 10:17:01 -0500 Subject: [PATCH 3/6] bumped version to v0.7.8 --- DESCRIPTION | 4 ++-- NAMESPACE | 7 +++---- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index fe5a4e2..25de865 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: scLANE Type: Package Title: Model gene expression dynamics with spline-based NB GLMs, GEEs, & GLMMs -Version: 0.7.7 +Version: 0.7.8 Authors@R: c(person(given = "Jack", family = "Leary", email = "j.leary@ufl.edu", role = c("aut", "cre"), comment = c(ORCID = "0009-0004-8821-3269")), person(given = "Rhonda", family = "Bacher", email = "rbacher@ufl.edu", role = c("ctb", "fnd"), comment = c(ORCID = "0000-0001-5787-476X"))) Description: This package uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. @@ -24,6 +24,7 @@ Imports: purrr, tidyr, furrr, + doSNOW, gamlss, scales, future, @@ -35,7 +36,6 @@ Imports: parallel, bigstatsr, RcppEigen, - doParallel, tidyselect, broom.mixed, Rcpp (>= 1.0.7) diff --git a/NAMESPACE b/NAMESPACE index e56a2ed..91bf4a7 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -11,7 +11,6 @@ export(getFittedValues) export(getKnotDist) export(getResultsDE) export(marge2) -export(modelLRT) export(nbGAM) export(npConvolve) export(plotClusteredGenes) @@ -19,12 +18,10 @@ export(plotModelCoefs) export(plotModels) export(smoothedCountsMatrix) export(sortGenesHeatmap) -export(stripGLM) export(summarizeModel) export(testDynamic) export(testSlope) export(theme_scLANE) -export(waldTestGEE) import(RcppEigen) import(glm2) import(magrittr) @@ -38,7 +35,7 @@ importFrom(Matrix,t) importFrom(Rcpp,sourceCpp) importFrom(bigstatsr,as_FBM) importFrom(broom.mixed,tidy) -importFrom(doParallel,registerDoParallel) +importFrom(doSNOW,registerDoSNOW) importFrom(dplyr,across) importFrom(dplyr,arrange) importFrom(dplyr,bind_cols) @@ -134,6 +131,8 @@ importFrom(stats,setNames) importFrom(stats,vcov) importFrom(tidyr,pivot_longer) importFrom(tidyselect,everything) +importFrom(utils,setTxtProgressBar) importFrom(utils,tail) +importFrom(utils,txtProgressBar) importFrom(withr,with_output_sink) useDynLib(scLANE, .registration = TRUE) From 24e7775bfbcc9acaedc773bd569b383b0683de64 Mon Sep 17 00:00:00 2001 From: Jack Leary Date: Fri, 17 Nov 2023 10:17:22 -0500 Subject: [PATCH 4/6] minor changes for v0.7.8 --- NEWS.md | 8 ++++++ R/ModelLRT.R | 8 ------ R/embedGenes.R | 3 ++ R/stripGLM.R | 9 ------ R/testDynamic.R | 53 +++++++++++++++++++++--------------- R/theme_scLANE.R | 12 ++++---- R/waldTestGEE.R | 7 +---- man/modelLRT.Rd | 8 ------ man/stripGLM.Rd | 9 ------ man/testDynamic.Rd | 4 +-- man/theme_scLANE.Rd | 12 ++++---- man/waldTestGEE.Rd | 5 ---- tests/testthat/test_scLANE.R | 9 +++--- 13 files changed, 61 insertions(+), 86 deletions(-) diff --git a/NEWS.md b/NEWS.md index 94cc394..39d86c7 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,11 @@ +# Changes in version 0.7.8 + ++ Added progress bar to `testDynamic()`. + + Changed parallel backend in `testDynamic()` from `doParallel` to `doSNOW` in order to make this possible. ++ Updated documentation with more runnable examples. ++ Passing `BiocCheck` with no errors. ++ Reduced set of exported functions to just what's necessary for model fitting & downstream analysis. + # Changes in version 0.7.7 + Added DOI badge to README. diff --git a/R/ModelLRT.R b/R/ModelLRT.R index dbf5aa9..8299a04 100644 --- a/R/ModelLRT.R +++ b/R/ModelLRT.R @@ -8,14 +8,6 @@ #' @param mod.0 The model corresponding to the null hypothesis. Defaults to NULL. #' @param is.glmm Are the models being compared GLMMs? Defaults to FALSE. #' @return A list containing the LRT test statistic, degrees freedom, and the \emph{p}-value computed using the Chi-squared assumption. -#' @export -#' @examples -#' \dontrun{ -#' modelLRT(mod.1 = marge_mod, mod.0 = null_mod) -#' modelLRT(mod.1 = marge_mod, -#' mod.0 = null_mod, -#' is.glmm = TRUE) -#' } modelLRT <- function(mod.1 = NULL, mod.0 = NULL, diff --git a/R/embedGenes.R b/R/embedGenes.R index 8af54f5..e75afdc 100644 --- a/R/embedGenes.R +++ b/R/embedGenes.R @@ -121,5 +121,8 @@ embedGenes <- function(smoothed.counts = NULL, umap1 = smoothed_counts_umap[, 1], umap2 = smoothed_counts_umap[, 2]) gene_df <- dplyr::bind_cols(gene_df, pca_df) + if (!cluster.genes) { + gene_df <- dplyr::select(gene_df, -leiden) + } return(gene_df) } diff --git a/R/stripGLM.R b/R/stripGLM.R index 72a0923..1465472 100644 --- a/R/stripGLM.R +++ b/R/stripGLM.R @@ -5,16 +5,7 @@ #' @description This function removes a \emph{lot} of components from the default GLM object in order to make it take up less memory. It does however retain enough pieces for \code{predict()} to still work. No promises beyond that. #' @param glm.obj An object of class GLM from which you'd like to strip out unnecessary components. Defaults to NULL. #' @return A slimmed-down \code{glm} object. -#' @export #' @seealso \code{\link{glm}} -#' @examples -#' data(sim_counts) -#' data(sim_pseudotime) -#' cell_offset <- createCellOffset(sim_counts) -#' marge_model <- marge2(sim_pseudotime, -#' Y = BiocGenerics::counts(sim_counts)[4, ], -#' Y.offset = cell_offset) -#' smaller_model <- stripGLM(marge_model$final_mod) stripGLM <- function(glm.obj = NULL) { # check inputs diff --git a/R/testDynamic.R b/R/testDynamic.R index 5efff51..67c1d3d 100644 --- a/R/testDynamic.R +++ b/R/testDynamic.R @@ -7,8 +7,9 @@ #' @import magrittr #' @importFrom Matrix t #' @importFrom bigstatsr as_FBM +#' @importFrom utils txtProgressBar setTxtProgressBar #' @importFrom foreach foreach %dopar% registerDoSEQ -#' @importFrom doParallel registerDoParallel +#' @importFrom doSNOW registerDoSNOW #' @importFrom parallel makeCluster stopCluster clusterEvalQ clusterExport clusterSetRNGStream #' @importFrom withr with_output_sink #' @importFrom MASS glm.nb negative.binomial theta.mm @@ -30,7 +31,7 @@ #' @param n.cores (Optional) If running in parallel, how many cores should be used? Defaults to 2. #' @param approx.knot (Optional) Should the knot space be reduced in order to improve computation time? Defaults to TRUE. #' @param glmm.adaptive (Optional) Should the basis functions for the GLMM be chosen adaptively? If not, uses 4 evenly spaced knots. Defaults to FALSE. -#' @param verbose (Optional) A boolean indicating whether the amount of time the function takes to run should be tracked and printed to the console. Defaults to TRUE. +#' @param verbose (Optional) A boolean indicating whether a progress bar should be printed to the console. Defaults to TRUE. #' @param random.seed (Optional) The random seed used to initialize RNG streams in parallel. Defaults to 312. #' @details #' \itemize{ @@ -110,14 +111,21 @@ testDynamic <- function(expr.mat = NULL, if (is.gee && !(cor.structure %in% c("ar1", "independence", "exchangeable"))) { stop("GEE models require a specified correlation structure.") } # set up time tracking + start_time <- Sys.time() + + # set up progress bar if (verbose) { - start_time <- Sys.time() + pb <- utils::txtProgressBar(0, length(genes), style = 3) + progress_fun <- function(n) utils::setTxtProgressBar(pb, n) + snow_opts <- list(progress = progress_fun) + } else { + snow_opts <- list() } # set up parallel processing if (parallel.exec) { cl <- parallel::makeCluster(n.cores) - doParallel::registerDoParallel(cl) + doSNOW::registerDoSNOW(cl) parallel::clusterSetRNGStream(cl, iseed = random.seed) } else { cl <- foreach::registerDoSEQ() @@ -155,7 +163,8 @@ testDynamic <- function(expr.mat = NULL, .noexport = no_export, .errorhandling = "pass", .inorder = TRUE, - .verbose = FALSE) %dopar% { + .verbose = FALSE, + .options.snow = snow_opts) %dopar% { lineage_list <- vector("list", n_lineages) for (j in seq(n_lineages)) { # pull cells assigned to lineage j @@ -367,24 +376,24 @@ testDynamic <- function(expr.mat = NULL, } }) + # finalize time tracking + end_time <- Sys.time() + total_time <- end_time - start_time + total_time_units <- attributes(total_time)$units + total_time_numeric <- as.numeric(total_time) + time_message <- paste0("\nscLANE testing completed for ", + length(genes), + " genes across ", + n_lineages, + " ", + ifelse(n_lineages == 1, "lineage ", "lineages "), + "in ", + round(total_time_numeric, 3), + " ", + total_time_units) + message(time_message) + # return results - if (verbose) { - end_time <- Sys.time() - total_time <- end_time - start_time - total_time_units <- attributes(total_time)$units - total_time_numeric <- as.numeric(total_time) - time_message <- paste0("scLANE testing completed for ", - length(genes), - " genes across ", - n_lineages, - " ", - ifelse(n_lineages == 1, "lineage ", "lineages "), - "in ", - round(total_time_numeric, 3), - " ", - total_time_units) - message(time_message) - } class(test_stats) <- "scLANE" return(test_stats) } diff --git a/R/theme_scLANE.R b/R/theme_scLANE.R index 84a7142..4738017 100644 --- a/R/theme_scLANE.R +++ b/R/theme_scLANE.R @@ -15,12 +15,12 @@ #' data(scLANE_models) #' data(sim_pseudotime) #' cell_offset <- createCellOffset(sim_counts) -#' plotModels(scLANE_models, -#' gene = names(scLANE_models)[1], -#' pt = sim_pseudotime, -#' expr.mat = sim_counts, -#' size.factor.offset = cell_offset) + -#' theme_scLANE() +#' model_plot <- plotModels(scLANE_models, +#' gene = names(scLANE_models)[1], +#' pt = sim_pseudotime, +#' expr.mat = sim_counts, +#' size.factor.offset = cell_offset) + +#' theme_scLANE() theme_scLANE <- function(base.size = 12, base.lwd = 0.75, diff --git a/R/waldTestGEE.R b/R/waldTestGEE.R index ddbcf0e..33ab6cc 100644 --- a/R/waldTestGEE.R +++ b/R/waldTestGEE.R @@ -17,11 +17,6 @@ #' @seealso \code{\link[aod]{wald.test}} #' @seealso \code{\link[geeM]{geem}} #' @seealso \code{\link{modelLRT}} -#' @export -#' @examples -#' \dontrun{ -#' waldTestGEE(mod.1 = full_model, mod.0 = null_model) -#' } waldTestGEE <- function(mod.1 = NULL, mod.0 = NULL) { # check inputs @@ -32,7 +27,7 @@ waldTestGEE <- function(mod.1 = NULL, mod.0 = NULL) { Notes = "No test performed due to model failure.") return(res) } - + mod.1 <- mod.1$final_mod if (is.null(mod.1) || is.null(mod.0) || !(inherits(mod.1, "geem") && inherits(mod.0, "geem"))) { stop("You must provide two geeM models to wald_test_gee().") } if (length(coef(mod.1)) <= length(coef(mod.0))) { diff --git a/man/modelLRT.Rd b/man/modelLRT.Rd index cdf910a..7d14066 100644 --- a/man/modelLRT.Rd +++ b/man/modelLRT.Rd @@ -19,14 +19,6 @@ A list containing the LRT test statistic, degrees freedom, and the \emph{p}-valu \description{ This function compares two models using a likelihood ratio test (LRT) under the assumption that the test statistic is asymptotically Chi-squared with degrees freedom equal to the difference in the number of parameters between the larger and smaller model. } -\examples{ -\dontrun{ -modelLRT(mod.1 = marge_mod, mod.0 = null_mod) -modelLRT(mod.1 = marge_mod, - mod.0 = null_mod, - is.glmm = TRUE) -} -} \author{ Jack Leary } diff --git a/man/stripGLM.Rd b/man/stripGLM.Rd index a4f1f4f..48d1dbd 100644 --- a/man/stripGLM.Rd +++ b/man/stripGLM.Rd @@ -15,15 +15,6 @@ A slimmed-down \code{glm} object. \description{ This function removes a \emph{lot} of components from the default GLM object in order to make it take up less memory. It does however retain enough pieces for \code{predict()} to still work. No promises beyond that. } -\examples{ -data(sim_counts) -data(sim_pseudotime) -cell_offset <- createCellOffset(sim_counts) -marge_model <- marge2(sim_pseudotime, - Y = BiocGenerics::counts(sim_counts)[4, ], - Y.offset = cell_offset) -smaller_model <- stripGLM(marge_model$final_mod) -} \seealso{ \code{\link{glm}} } diff --git a/man/testDynamic.Rd b/man/testDynamic.Rd index 0ec53cf..5299865 100644 --- a/man/testDynamic.Rd +++ b/man/testDynamic.Rd @@ -18,7 +18,7 @@ testDynamic( parallel.exec = TRUE, n.cores = 2, approx.knot = TRUE, - track.time = TRUE, + verbose = TRUE, random.seed = 312 ) } @@ -49,7 +49,7 @@ testDynamic( \item{approx.knot}{(Optional) Should the knot space be reduced in order to improve computation time? Defaults to TRUE.} -\item{track.time}{(Optional) A boolean indicating whether the amount of time the function takes to run should be tracked and printed to the console. Defaults to TRUE.} +\item{verbose}{(Optional) A boolean indicating whether a progress bar should be printed to the console. Defaults to TRUE.} \item{random.seed}{(Optional) The random seed used to initialize RNG streams in parallel. Defaults to 312.} } diff --git a/man/theme_scLANE.Rd b/man/theme_scLANE.Rd index b023210..9eaf1fb 100644 --- a/man/theme_scLANE.Rd +++ b/man/theme_scLANE.Rd @@ -31,12 +31,12 @@ data(sim_counts) data(scLANE_models) data(sim_pseudotime) cell_offset <- createCellOffset(sim_counts) -plotModels(scLANE_models, - gene = names(scLANE_models)[1], - pt = sim_pseudotime, - expr.mat = sim_counts, - size.factor.offset = cell_offset) + -theme_scLANE() +model_plot <- plotModels(scLANE_models, + gene = names(scLANE_models)[1], + pt = sim_pseudotime, + expr.mat = sim_counts, + size.factor.offset = cell_offset) + + theme_scLANE() } \author{ Jack Leary diff --git a/man/waldTestGEE.Rd b/man/waldTestGEE.Rd index 97f2cd9..fdc8570 100644 --- a/man/waldTestGEE.Rd +++ b/man/waldTestGEE.Rd @@ -23,11 +23,6 @@ Performs a basic Wald test to determine whether an alternate model is significan \item The \emph{p}-value is calculated using an asymptotic \eqn{\Chi^2} distribution, with the degrees of freedom equal to the number of non-intercept coefficients in the alternative model. } } -\examples{ -\dontrun{ -waldTestGEE(mod.1 = full_model, mod.0 = null_model) -} -} \seealso{ \code{\link[multgee]{waldts}} diff --git a/tests/testthat/test_scLANE.R b/tests/testthat/test_scLANE.R index 6ecfdfd..3fd4e18 100644 --- a/tests/testthat/test_scLANE.R +++ b/tests/testthat/test_scLANE.R @@ -43,8 +43,7 @@ withr::with_output_sink(tempfile(), { is.gee = TRUE, cor.structure = "ar1", id.vec = sim_data$subject, - n.cores = 2, - track.time = FALSE) + n.cores = 2) glmm_gene_stats <- testDynamic(sim_data, pt = pt_test, genes = genes_to_test, @@ -54,7 +53,7 @@ withr::with_output_sink(tempfile(), { glmm.adaptive = TRUE, id.vec = sim_data$subject, n.cores = 2, - track.time = FALSE) + verbose = FALSE) # get results tables overall glm_test_results <- getResultsDE(glm_gene_stats) gee_test_results <- getResultsDE(gee_gene_stats) @@ -122,7 +121,7 @@ withr::with_output_sink(tempfile(), { return.WIC = TRUE) # run GLMM model -- no offset glmm_mod <- fitGLMM(X_pred = pt_test, - Y = counts_test[, 3], + Y = counts_test[, 4], id.vec = sim_data$subject, adaptive = TRUE, M.glm = 3, @@ -130,7 +129,7 @@ withr::with_output_sink(tempfile(), { return.GCV = TRUE) # run GLMM model -- with offset glmm_mod_offset <- fitGLMM(X_pred = pt_test, - Y = counts_test[, 3], + Y = counts_test[, 4], Y.offset = cell_offset, id.vec = sim_data$subject, adaptive = TRUE, From 6cac85cc7b0ee0ab18fc7b7b83610d87ec815688 Mon Sep 17 00:00:00 2001 From: Jack Leary Date: Fri, 17 Nov 2023 10:42:58 -0500 Subject: [PATCH 5/6] tweaked README --- README.Rmd | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.Rmd b/README.Rmd index a3cf586..ec42ec6 100644 --- a/README.Rmd +++ b/README.Rmd @@ -115,7 +115,8 @@ scLANE_models_glm <- testDynamic(sim_data, pt = order_df, genes = gene_sample, size.factor.offset = cell_offset, - n.cores = 4) + n.cores = 4, + verbose = FALSE) ``` After the function finishes running, we use `getResultsDE()` to generate a sorted table of DE test results, with one row for each gene & lineage. The GLM mode uses a simple likelihood ratio test to compare the null & alternate models, with the test statistic assumed to be [asymptotically Chi-squared distributed](https://en.wikipedia.org/wiki/Likelihood-ratio_test). From 16af9627d7b3145a68c6ffa7299e84c648d843e6 Mon Sep 17 00:00:00 2001 From: Jack Leary Date: Fri, 17 Nov 2023 15:14:23 -0500 Subject: [PATCH 6/6] fixed README --- README.Rmd | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/README.Rmd b/README.Rmd index ec42ec6..c362a72 100644 --- a/README.Rmd +++ b/README.Rmd @@ -142,7 +142,8 @@ scLANE_models_gee <- testDynamic(sim_data, is.gee = TRUE, id.vec = sim_data$subject, cor.structure = "ar1", - n.cores = 4) + n.cores = 4, + verbose = FALSE) ``` We again generate the table of DE test results. The variance of the estimated coefficients is determined using [the sandwich estimator](https://online.stat.psu.edu/stat504/lesson/12/12.3), and a Wald test is used to compare the null & alternate models. @@ -169,7 +170,8 @@ scLANE_models_glmm <- testDynamic(sim_data, is.glmm = TRUE, glmm.adaptive = TRUE, id.vec = sim_data$subject, - n.cores = 4) + n.cores = 4, + verbose = FALSE) ``` **Note:** The GLMM mode is still under development, as we are working on further reducing runtime and increasing the odds of the underlying optimization process converging successfully. As such, updates will be frequent and functionality / results may shift slightly. @@ -193,7 +195,7 @@ We can use the `plotModels()` to visually compare different types of models. It ```{r plot-models-glm} plotModels(scLANE_models_glm, - gene = "JARID2", + gene = scLANE_res_glm$Gene[1], pt = order_df, expr.mat = sim_data, size.factor.offset = cell_offset, @@ -207,7 +209,7 @@ Model comparison using the GEE mode is similar, with the only change being that ```{r plot-models-gee} plotModels(scLANE_models_gee, - gene = "DGUOK", + gene = scLANE_res_gee$Gene[1], is.gee = TRUE, id.vec = sim_data$subject, pt = order_df, @@ -223,7 +225,7 @@ When plotting the models generated using the GLMM mode, we split by lineage & co ```{r plot-models-glmm, fig.width=9, fig.height=9} plotModels(scLANE_models_glmm, - gene = "FLOT2", + gene = scLANE_res_glmm$Gene[1], pt = order_df, expr.mat = sim_data, size.factor.offset = cell_offset, @@ -246,6 +248,16 @@ scLANE_models_glm[["JARID2"]]$Lineage_A$Gene_Dynamics %>% col.names = c("Gene", "Lineage", "Breakpoint", "First Slope", "Second Slope", "First Trend", "Second Trend")) ``` +Coefficients can also be plotted like so: + +```{r} +plotModelCoefs(scLANE_models_glm, + gene = "JARID2", + pt = order_df, + expr.mat = sim_data, + size.factor.offset = cell_offset) +``` + ### Knot distribution Lastly, we can pull the locations in pseudotime of all the knots fitted by `scLANE`. Visualizing this distribution gives us some idea of where transcriptional switches are occurring in the set of genes classified as dynamic.