minor changes for v0.7.8

jr-leary7 · Nov 17, 2023 · 24e7775 · 24e7775
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diff --git 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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
diff --git a/man/stripGLM.Rd b/man/stripGLM.Rd
diff --git a/man/testDynamic.Rd b/man/testDynamic.Rd
diff --git a/man/theme_scLANE.Rd b/man/theme_scLANE.Rd
diff --git a/man/waldTestGEE.Rd b/man/waldTestGEE.Rd
diff --git 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)
@@ -122,15 +121,15 @@ 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$subject,
                       adaptive = TRUE,
                       M.glm = 3,
                       return.basis = TRUE,
                       return.GCV = TRUE)
   # run GLMM model -- with offset
   glmm_mod_offset <- fitGLMM(X_pred = pt_test,
-                             Y = counts_test[, 3],
+                             Y = counts_test[, 4],
                              Y.offset = cell_offset,
                              id.vec = sim_data$subject,
                              adaptive = TRUE,