From 3bcf3f1ed32834f250d0b5268f9671f68a8eec35 Mon Sep 17 00:00:00 2001
From: candicechu <candy9087@gmail.com>
Date: Sun, 13 Mar 2016 21:00:03 -0500
Subject: [PATCH] add file

---
 run_edger.R | 140 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 140 insertions(+)
 create mode 100644 run_edger.R

diff --git a/run_edger.R b/run_edger.R
new file mode 100644
index 0000000..c6fe528
--- /dev/null
+++ b/run_edger.R
@@ -0,0 +1,140 @@
+library(edgeR)
+
+# From: http://stackoverflow.com/questions/1815606/rscript-determine-path-of-the-executing-script
+# So we can copy this script to where the analysis output is saved
+thisFile <- function() {
+        cmdArgs <- commandArgs(trailingOnly = FALSE)
+        needle <- "--file="
+        match <- grep(needle, cmdArgs)
+        if (length(match) > 0) {
+                # Rscript
+                return(normalizePath(sub(needle, "", cmdArgs[match])))
+        } else {
+                # 'source'd via R console
+                return(normalizePath(sys.frames()[[1]]$ofile))
+        }
+}
+
+#args = c("lists/organoid","keys/organoid.csv")
+args = c("lists/candice_list","keys/candice-key.csv")
+
+# Get counts file from analysis/fname/fname.T.csv
+bname = basename(args[1]) 
+fname = paste(bname,"-count.T.csv",sep='')
+dat = read.csv(file.path("analysis",bname,fname), header = TRUE, row.names=1)
+
+# Filter low count reads
+keep = rowSums(cpm(dat) > 3) >= 3
+counts = dat[keep,]
+
+## Read in key file
+key = read.csv(args[2], header=TRUE, row.names=1)
+
+#############################################
+## Create model comparison matrix with sample key
+#############################################
+# Lampe Biopsy main
+sel = grepl("MT-.*", rownames(counts)) + grepl("ERCC-.*", rownames(counts)) + grepl("mt-.*", rownames(counts))
+# We have to filter out 6123 because its not full rank
+# > summary(factor(sapply(names(counts), function(x) substr(x,2,5))))      
+# 6102 6103 6105 6106 6108 6110 6121 6122 6123 6127 
+#    8    8    8    8    8    8    8    8    6    8 
+counts = counts[!sel,]
+# counts = counts[!sel,!grepl("6123", names(counts))]
+# key = key[!grepl("6123", rownames(key)),]
+ename = "edger-pair-treatment"
+factors = key[order(rownames(key)), c(sample,type)]
+factors$type = factor(factors$type)
+# factors$treatment = relevel(factors$treatment, "placebo")
+design = model.matrix(~sample+type, data=factors)
+groups = factors$type
+#groups = factor(paste(key$tissue,key$location,sep='.'))
+#############################################
+system(paste("mkdir -p ",file.path("analysis",bname,ename)))
+file.copy(thisFile(), file.path("analysis", bname, ename, "edger_script.R"))
+
+
+counts = counts[,order(names(counts))]
+
+########################
+# run Pairwise analysis ...
+########################
+# y = DGEList(counts=counts, group=factors)
+# y = calcNormFactors(y)
+# y = estimateCommonDisp(y)
+# y = estimateTagwiseDisp(y)
+
+########################
+# or run GLM analysis
+########################
+y = DGEList(counts=counts)
+y = calcNormFactors(y)
+y = estimateGLMCommonDisp(y, design)
+y = estimateGLMTrendedDisp(y, design)
+y = estimateGLMTagwiseDisp(y, design)
+fit = glmFit(y, design)
+
+## get normalized counts for each group for outputting to summary spreadsheet
+scaled.counts = data.frame(mapply(`*`, counts, y$samples$lib.size *
+                                  y$samples$norm.factors/mean(y$samples$lib.size)))
+rownames(scaled.counts) = rownames(counts)
+dfs = split.data.frame(t(scaled.counts), groups)
+dfss = sapply(dfs, colMeans)
+
+#group_names = levels(groups)
+#group_names_means = sapply(group_names, function(x) paste("mean_",x,sep=""), USE.NAMES=FALSE)
+#colnames(dfss) = group_names_means
+
+#### Write results
+run_analysis = function(outfile, contrast=NULL, coef=NULL) {
+  # Pairwise test
+    # lrt = exactTest(y)
+  # GLM Test
+    lrt = glmLRT(fit, contrast=contrast, coef=coef) 
+
+  ot1 = topTags(lrt,n=nrow(counts),sort.by="PValue")$table
+  #if (is.null(contrast)) {
+    #sel = which(as.logical(contrast))
+    #ot1 = merge(ot1, dfss[,sel], by=0)
+  #} else {
+    #ot1 = merge(ot1, dfss, by=0)
+  #}
+  ot1 = merge(ot1, dfss, by=0)
+  ot1 = ot1[order(ot1$FDR),] # Sort by ascending FDR
+  write.csv(ot1,outfile,row.names=FALSE)
+  
+  if (!is.null(lrt$table$logFC)){
+    detags = rownames(ot1)[ot1$FDR < 0.05]
+    png(paste(outfile,".png",sep=""))
+    plotSmear(lrt, de.tags=detags)
+    abline(h=c(-2,2),col="blue")
+    dev.off()
+  }
+  sink(file=file.path(dirname(outfile), "summary.txt"), append=TRUE)
+  print(outfile)
+  print(summary(decideTestsDGE(lrt, p=0.05, adjust="BH")))
+  sink(NULL)
+
+  #print(cpm(y)[detags,])
+  #print(summary(decideTestsDGE(lrt, p=0.05, adjust="none")))
+}
+
+meta_run = function(coef) {run_analysis(file.path("analysis",bname,ename,paste(colnames(design)[coef],".csv",sep="")),coef=coef)}
+
+meta_run(dim(design)[2])
+meta_run(dim(design)[2]-1)
+meta_run(dim(design)[2]-2)
+
+system(paste("cat",file.path("analysis",bname,ename,"summary.txt")))
+
+# Pairwise test
+# run_analysis(file.path("analysis",bname,paste(ename,".csv",sep="")))
+
+## output MA, MDS, etc.., plots
+png(file.path("analysis",bname,ename,"edger-mds.png"))
+p = plotMDS(y)
+dev.off()
+
+png(file.path("analysis",bname,ename,"edger-bcv.png"))
+p = plotBCV(y)
+dev.off()