Attempt to identify tumor cells in those non-ETP samples without B cells (SCPCP000003)

analyses/cell-type-nonETP-ALL-03/renv.lock

@@ -76,6 +76,41 @@
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@@ -840,6 +901,7 @@
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@@ -848,7 +910,7 @@
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@@ -1083,6 +1145,16 @@
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@@ -1134,6 +1229,21 @@
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@@ -1189,6 +1299,19 @@
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@@ -1278,6 +1401,27 @@
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@@ -1537,6 +1692,17 @@
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@@ -1560,6 +1726,24 @@
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@@ -1748,6 +1932,17 @@
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analyses/cell-type-nonETP-ALL-03/results/README.md

@@ -12,4 +12,8 @@ These are the generated outputs for each sample in the S3 bucket:
 -   `rds` objects: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/rds`
 -   metadata and ScType results: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/`
 -   CopyKat results: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/copykat_output`
--   umap and dot plots: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/plots` (are also found in the repository plots/).
+-   evaluating cluster separation, stability, and purity: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/evalClus`
+-   umap and dot plots: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/plots`
+-   violin and stacked bar plots for exploring the results of CopyKat prediction: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/plots/copykat_exploration`
+
+\*\*All the plots are also found in the repository plots/.

analyses/cell-type-nonETP-ALL-03/scripts/02-03_annotation.R

@@ -99,6 +99,7 @@ plot_modulescore <- function(gs_list, seu, sample.name){
     ggtitle(paste0(sample.name,": cell type module score")) +
     scale_color_gradient2(low = scales::muted("blue"), mid = "whitesmoke", high = scales::muted("red"), midpoint = 0)
   ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.png")), width = 7, height = 7, bg = "white", dpi = 150)
+  return (seu)
 }
 
 run_annot <- function(ind.lib){
@@ -115,7 +116,7 @@ run_annot <- function(ind.lib){
   write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.lib,"_sctype_top10_celltypes_perCluster.txt")),
               row.names = F, sep = "\t", quote = F)
 
-  plot_modulescore(gs_list, seu, ind.lib)
+  seu <- plot_modulescore(gs_list, seu, ind.lib)
 
   #using copykat for tumor cells identification
   norm.cells <- colnames(seu)[which(seu$sctype_classification=="B")]