run monocle nb in serial

sceptre_paper/analysis_drivers/analysis_drivers_xie/run_monocle_nb_at_scale.R

@@ -1,21 +1,22 @@
 args <- commandArgs(trailingOnly = TRUE)
+library(monocle)
 code_dir <- if (is.na(args[1])) "/Users/timbarry/research_code/sceptre-manuscript" else args[1]
 source(paste0(code_dir, "/sceptre_paper/analysis_drivers/analysis_drivers_xie/paths_to_dirs.R"))
 source(paste0(code_dir, "/sceptre_paper/analysis_drivers/analysis_drivers_xie/gasp_custom_functs.R"))
 cds <- readRDS(paste0(processed_dir, "/monocole_obj.rds"))
 pairs <- fst::read_fst(paste0(processed_dir, "/gRNA_gene_pairs.fst"))
-pairs <- pairs[1:40,]
+pairs <- pairs[1:100,]
 
 # set formula
 reduced_formula <- "~ log_n_umis + batch + log_n_gRNA_umis"
-cds <- cds[as.character(pairs$gene_id),]
 
 # set up parallel
-future::plan(future::multisession())
+# future::plan(future::multisession())
 
 # Loop over all gRNAs and genes; subset by gene, and regress on the corresponding gRNA using Molly's function
-res <- future.apply::future_lapply(X = seq(1, nrow(pairs)), FUN = function(i) {
-  print(i)
+# res <- future.apply::future_lapply(X = seq(1, nrow(pairs)), FUN = function(i) {
+res <- purrr::map_dfr(.x = seq(1, nrow(pairs)), .f = function(i) {
+  if (i %% 50 == 0) print (i)
   gRNA <- as.character(pairs$gRNA_id[i])
   gene <- as.character(pairs$gene_id[i])
   # subset cds