This script accpets file paths to analysis metrics tables and produces tearsheet data and data processing report graphs

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@3585 348d0f76-0448-11de-a6fe-93d51630548a

R/tearsheet.r

@@ -1,10 +1,26 @@
 #Before executing this file, save squid files as csv, then as tab deliminated files with only the column values as the header, change the format of all cells to numbers. Assign the path to these files to "samples" and "lanes" respectively. 
 
-tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4, eval5, eval6){
+args<-commandArgs(TRUE)
+lanes<-args[1]
+samples<-args[2]
+sample_sets<-args[3]
+eval<-args[4]
+noveltitv<-args[5]
+knowntitv<-args[6]
+DOC<-args[7]
 
+if(is.na(sample_sets)){
+	print("Please specify sample set for file naming and press enter.")
+	scan("stdin", what="character",n=1)->sample_sets
+	print("Thanks!")
+	}
+
+	if(is.na(lanes) == FALSE && is.na(samples)==FALSE){
+		#this makes a table & graphs using Picard data
 		read.delim(file=lanes, header= TRUE)->bylane; 
     	read.delim(file=samples, header= TRUE)->bysample; 
 
+		#Calc by lane metrics
 	   	attach(bylane);
 	   	callable.target<-HS_TARGET_TERRITORY[1]; 
    		singlelanes<-length(which(Lane.Type=="Single"));
@@ -22,13 +38,12 @@ tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4,
     	sd.20x.lane<-round(sd(HS_PCT_TARGET_BASES_20X, na.rm=TRUE));
     	sd.30x.lane<-round(sd(HS_PCT_TARGET_BASES_30X, na.rm=TRUE));
 	   		
+	   		
 	   	names<-paste(Project, " ", External.ID, "-", Lane, sep="")
        
    		 #makes a plot of the number of SNPS called per lane
-
 		library(graphics)
 			
-
 	    pdf(file=paste(sample_sets, "_SNPS.pdf", sep=""), width=0.2*length(SNP_TOTAL_SNPS), height=0.1*length(SNP_TOTAL_SNPS))        
   
 		layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
@@ -65,11 +80,9 @@ tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4,
             		mtext(paste("Outlier SNP call counts in  ", length(boxplot.stats(SNP_TOTAL_SNPS)$out), "lanes"), side=1, line=4)
    					}
         
-        
     		dev.off()
     
    			 #makes a plot of snp calls ordered by lane
-    
     		pdf(file=paste(sample_sets, "_SNPS_lane.pdf", sep=""), width=0.2*length(SNP_TOTAL_SNPS), height=0.1*length(SNP_TOTAL_SNPS))       
   
 			layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
@@ -89,8 +102,6 @@ tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4,
 		    dev.off()    
         
     		#makes a plot of fingerprint calls and labels them good or bad
-        
-        
         	badsnps<-union(which(FP_CONFIDENT_MATCHING_SNPS<15), which(FP_CONFIDENT_MATCHING_SNPS<15))
 
 		    colors<-c(rep("Blue", length(FP_CONFIDENT_CALLS)))
@@ -106,14 +117,15 @@ tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4,
         		legend("bottomright", legend=c("Confident calls at fingerprint sites by lane", "Confident matching calls at fingerprint sites by lane", "Confident calls in bad lanes", "Confident matching calls in bad lanes"), pch=c(1, 16, 1, 16), col=c("Blue", "Blue", "Red", "Red"))
        			 mtext("Some problematic fingerprint sites", side=3)
   				  }else{
-        legend("bottomright", legend=c("Confident calls at fingerprint sites by lane", "Confident matching calls at fingerprint sites by lane"), pch=c(1, 16), col="Blue")}
+  	      			legend("bottomright", legend=c("Confident calls at fingerprint sites by lane", "Confident matching calls at fingerprint sites by lane"), pch=c(1, 16), col="Blue")
+  	      			}
 
    			dev.off()
  
-    detach(bylane);
+	   		detach(bylane) 
 	   		
+			#Calc by sample metrics   	
 		   	attach(bysample);
-
 			mean.lanes.samp<-signif(mean(X..Lanes.included.in.aggregation, na.rm = TRUE));
 			sd.lanes.samp<-signif(sd(X..Lanes.included.in.aggregation, na.rm=TRUE));
 			mean.mrl.samp<-signif(mean(Mean.Read.Length, na.rm=TRUE));
@@ -134,7 +146,6 @@ tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4,
 			detach(bysample);
 
 			#print all of this stuff out in R. 
-
 			print(paste("Callable Target: ", callable.target, " bases", sep=""), quote = FALSE);
 			print(paste("Used Lanes per Sample: ", mean.lanes.samp, " +/- ", sd.lanes.samp, sep=""), quote=FALSE);
 			print(paste("Parities: ", singlelanes, " single lanes, ", pairedlanes, " paired lanes", sep=""), quote=FALSE);
@@ -152,27 +163,83 @@ tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4,
 			print(paste("% loci covered to 30x per lane: ", mean.30x.lane, "% +/- ", sd.30x.lane, "%", sep=""), quote = FALSE)
 			print(paste("% loci covered to 30x per sample: ", mean.30x.samp, "% +/- ", sd.30x.samp, "%", sep=""), quote = FALSE)
 	   		         
-	#still need to figure out how to get various information from eval file into R, but whatever
+	   	}else{
-
+	   		print("Lane and Sample metrics file paths not provided")
-	read.csv(eval1, header=TRUE)->errpercycle
+	   		}	 
 	   		 
 	
-	errpercycle<-matrix(c(rep("tester", 24*90 ), rep(1:8, 3*90), rep(1:90,24), runif(90*24, min=0, max=0.7)), nrow=90*24, ncol=4); colnames(errpercycle)<-c("sample_set", "plate", "lane", "cycle", "errorrate") #delete this after testing
-	as.data.frame(errpercycle)->errpercycle
-	attach(errpercycle)
 	
-	pdf(paste(sample_set, "error_rate_per_cycle.pdf", sep=""))
+	#Makes Error Rate percycle graph   		 
-	names<-paste(sample)
+    if(is.na(eval)==FALSE){
+    	read.delim(eval, header=TRUE)[2:ncol(read.delim(eval, header=TRUE))]->errpercycle
+	
+		pdf(paste(sample_sets, "_errorrate_per_cycle.pdf", sep=""), width=6, height=5)
+	
+		crazies<-which(errpercycle[75,]>0.3) #this can be changed to any kind of filter for particular lanes
+	
+		colors<-rainbow(ncol(errpercycle), s=0.5, v=0.5)
+		colors[crazies]<-rainbow(length(crazies))
+		weights<-rep(1, ncol(errpercycle))
+		weights[crazies]<-2
+	
+		matplot(errpercycle, type="l", lty="solid", col=colors, lwd=weights,  main="Error Rate per Cycle", ylab="Error Rate", xlab="Cycle", ylim=c(0, 0.7))
+	
+		if(length(crazies)>0){
+			legend("topleft", title="Unusual Lanes", legend=colnames(errpercycle)[crazies], lty="solid", lwd=2, col=colors[crazies], xjust=0.5)
+			}else{
+			legend("topleft", legend="No unusual lanes.", bty="n")
+			}
+		
+		dev.off()
+    	
+    	}else{
+    		print("Error Rate Per Cycle file paths not provided")
+    		}
+	
+	#Makes TI/TV known v novel graph
+	if(is.na(noveltitv)==FALSE && is.na(knowntitv) == FALSE){
+		pdf(paste(sample_set, "_TiTv.pdf", sep=""), width=6, height=5)
+
+		read.table(file=noveltitv, header=FALSE)->novels
+		read.table(file=knowntitv, header=FALSE)->knowns
+
+		plot(novels[,2], col="red", ylim=c(0, 3.5), main="Ti/Tv for 		Novel and Known SNP calls", ylab="Ti/Tv", xlab="", xaxt="n")
+		points(knowns[,2], col="blue")
+
+ 		axis(side=1, at=(1:length(novels[,2])), labels=novels[,1], 		cex.axis=1, las=2)
+
+		legend("bottomright", legend=c("Known Variants", "Novel 		Variants"), col=c("blue", "red"), pch=1, xjust=0.5)
+		mtext("Lower Ti/Tv ratios indicated more false positive SNP calls.", side=1)
+		dev.off()
+		}else{
+			print("Transition/transversion ratio file paths not provided")
+			
+			}
+			
+	#Make DOC graph
+	if(is.na(DOC)==FALSE){
+		pdf(paste(sample_set, "_DOC.pdf", sep=""), width=6, height=5)
+	
+	as.matrix(as.vector(read.delim(DOC, header=TRUE)[,2:502]))->DOCdata
+	DOCdata<-matrix(DOCdata*100/sum(DOCdata[1,]), nrow=501, ncol=29, byrow=TRUE)
+	colnames(DOCdata)<-read.delim(DOC, header=TRUE)[,1]
+	oddies<-which(apply(DOCdata, 2, max)>10) #can be assigned any particular heuristic
+	ncolors<-rainbow(ncol(DOCdata), s=0.5, v=0.5)
+	ncolors[oddies]<-rainbow(length(oddies))
+	nweights<-rep(1, ncol(DOCdata))
+	nweights[oddies]<-2
+	matplot(DOCdata, type="l", main="Depth of Coverage by Sample", ylab="Percent bases covered to a given depth", xlab="log(Depth)", log="x", col=ncolors,  lty="solid", lwd=nweights)
+	
+	if(length(oddies)>0){
+		legend("topright", title="Unusual Cases", legend=colnames(DOCdata)[oddies], lty="solid", lwd=2, col=ncolors[oddies], xjust=0.5)
+		}else{
+			legend("topright", legend="No unusual cases.", bty="n")
+			}
+	
+	dev.off()
+	
+		}else{
+			print("Depth of Coverage filepath not provided")
+			}		
 	
 
-}
-
-lane<-"mennonite_by_lane.txt"
-#insert file path here before running
-sample<-"mennonite_by_sample.txt"
-#insertfilepath here before running
-sample_set<-"Ashuldin_mennonite"
-
-
-tearsheetcalc(lane,sample,sample_set)
-