Moving BQSR plotting script to public so that it can be used with the substiution-model-only version.

public/R/scripts/org/broadinstitute/sting/gatk/walkers/bqsr/BQSR.R

@@ -0,0 +1,100 @@
+library("ggplot2")
+
+args <- commandArgs(TRUE)
+data <- read.csv(args[1])
+data <- within(data, EventType <- factor(EventType, levels = rev(levels(EventType))))
+
+numRG = length(unique(data$ReadGroup))
+blankTheme = opts(panel.grid.major = theme_blank(), panel.grid.minor = theme_blank(), panel.background = theme_blank(), axis.ticks = theme_blank())
+
+# Viewport (layout 2 graphs top to bottom)
+distributeGraphRows <- function(graphs, heights = c()) {
+  if (length(heights) == 0) {
+    heights <- rep.int(1, length(graphs))
+  }
+  heights <- heights[!is.na(graphs)]
+  graphs <- graphs[!is.na(graphs)]
+  numGraphs <- length(graphs)
+  Layout <- grid.layout(nrow = numGraphs, ncol = 1, heights=heights)
+  grid.newpage()
+  pushViewport(viewport(layout = Layout))
+  subplot <- function(x) viewport(layout.pos.row = x, layout.pos.col = 1)
+  for (i in 1:numGraphs) {
+    print(graphs[[i]], vp = subplot(i))
+  }
+}
+
+
+for(cov in levels(data$CovariateName)) {    # for each covariate in turn  
+  d = data[data$CovariateName==cov,]        # pull out just the data for this covariate so we can treat the non-numeric values appropriately
+  if( cov == "Context" ) {
+    d$CovariateValue = as.character(d$CovariateValue)
+    d$CovariateValue = substring(d$CovariateValue,nchar(d$CovariateValue)-2,nchar(d$CovariateValue))
+  } else {
+    d$CovariateValue = as.numeric(levels(d$CovariateValue))[as.integer(d$CovariateValue)] # efficient way to convert factors back to their real values
+  }
+  #d=subset(d,Observations>2000) # only show bins which have enough data to actually estimate the quality
+  dSub=subset(d,EventType=="Base Substitution")
+  dIns=subset(d,EventType=="Base Insertion")
+  dDel=subset(d,EventType=="Base Deletion")
+  dSub=dSub[sample.int(length(dSub[,1]),min(length(dSub[,1]),2000)),] # don't plot too many values because it makes the PDFs too massive
+  dIns=dIns[sample.int(length(dIns[,1]),min(length(dIns[,1]),2000)),] # don't plot too many values because it makes the PDFs too massive
+  dDel=dDel[sample.int(length(dDel[,1]),min(length(dDel[,1]),2000)),] # don't plot too many values because it makes the PDFs too massive
+  d=rbind(dSub, dIns, dDel)
+
+  if( cov != "QualityScore" ) {    
+    p <- ggplot(d, aes(x=CovariateValue,y=Accuracy,alpha=log10(Observations))) +
+      geom_abline(intercept=0, slope=0, linetype=2) + 
+      xlab(paste(cov,"Covariate")) +
+      ylab("Quality Score Accuracy") +
+      blankTheme
+    if(cov == "Cycle") {
+      b <- p + geom_point(aes(color=Recalibration)) + scale_color_manual(values=c("maroon1","blue")) + facet_grid(.~EventType) +
+        opts(axis.text.x=theme_text(angle=90, hjust=0))
+      
+      p <- ggplot(d, aes(x=CovariateValue,y=AverageReportedQuality,alpha=log10(Observations))) +
+        xlab(paste(cov,"Covariate")) +
+        ylab("Mean Quality Score") +
+        blankTheme
+      e <- p + geom_point(aes(color=Recalibration)) + scale_color_manual(values=c("maroon1","blue")) + facet_grid(.~EventType) +
+        opts(axis.text.x=theme_text(angle=90, hjust=0))
+      
+      
+    } else {
+      c <- p + geom_point(aes(color=Recalibration)) + scale_color_manual(values=c("maroon1","blue")) + facet_grid(.~EventType) +
+        opts(axis.text.x=theme_text(angle=90, hjust=0)) + xlab(paste(cov,"Covariate (3 base suffix)"))
+      p <- ggplot(d, aes(x=CovariateValue,y=AverageReportedQuality,alpha=log10(Observations))) +
+        xlab(paste(cov,"Covariate (3 base suffix)")) +
+        ylab("Mean Quality Score") +
+        blankTheme
+      f <- p + geom_point(aes(color=Recalibration)) + scale_color_manual(values=c("maroon1","blue")) + facet_grid(.~EventType) +
+        opts(axis.text.x=theme_text(angle=90, hjust=0))
+      
+    }
+  } else {
+    p <- ggplot(d, aes(x=AverageReportedQuality,y=EmpiricalQuality,alpha=log10(Observations))) +
+      geom_abline(intercept=0, slope=1, linetype=2) + 
+      xlab("Reported Quality Score") +
+      ylab("Empirical Quality Score") +
+      blankTheme
+    a <- p + geom_point(aes(color=Recalibration)) + scale_color_manual(values=c("maroon1","blue")) + facet_grid(.~EventType)
+    
+    p <- ggplot(d, aes(x=CovariateValue)) +
+      xlab(paste(cov,"Covariate")) +
+      ylab("Number of Observations") +
+      blankTheme
+    d <- p + geom_histogram(aes(fill=Recalibration,weight=Observations),alpha=0.6,binwidth=1,position="identity") + scale_fill_manual(values=c("maroon1","blue")) + facet_grid(.~EventType) +     
+      scale_y_continuous(formatter="comma")
+    
+  }
+}
+
+pdf(args[2],height=9,width=15)
+distributeGraphRows(list(a,b,c), c(1,1,1))
+distributeGraphRows(list(d,e,f), c(1,1,1))
+dev.off()
+
+
+if (exists('compactPDF')) {
+  compactPDF(args[2])
+}