R/estLODR.R
In arcolombo/Rccdashboard: Assess Differential Gene Expression Experiments with ERCC Controls
#' Estimate Limit of Detection of Ratios (LODR)
#'
#' @param exDat list, contains input data and stores analysis results
#' @param kind "ERCC" or "Sim"
#' @param prob probability, ranging from 0 - 1, default is 0.9
#'
#' @details
#' This is the function to estimate a limit of detection of ratios (LODR) for a
#' a chosen probability and threshold p-value for the fold changes in the ERCC
#' control ratio mixtures.
#'
#' @examples
#' data(SEQC.Example)
#'
#' exDat <- initDat(datType="array", isNorm=FALSE,
#' exTable=UHRR.HBRR.arrayDat,
#' filenameRoot="testRun", sample1Name="UHRR",
#' sample2Name="HBRR", erccmix="RatioPair",
#' erccdilution = 1, spikeVol = 50,
#' totalRNAmass = 2.5*10^(3), choseFDR=0.01)
#'
#' exDat <- est_r_m(exDat)
#'
#' exDat <- dynRangePlot(exDat)
#'
#' exDat <- geneExprTest(exDat)
#'
#' exDat <- estLODR(exDat, kind = "ERCC", prob = 0.9)
#'
#' exDat$Figures$lodrERCCPlot
#'
#' @export
#'
estLODR <- function(exDat,kind = "ERCC", prob=0.9){
##############
#### LODR ####
##############
### Select fitting parameters
# pval.cutoff default is .001
# probability cutoff is .9
# kind can be ERCC or Sim
sampleInfo <- exDat$sampleInfo
erccInfo <- exDat$erccInfo
plotInfo <- exDat$plotInfo
pval.cutoff<- exDat$Results$p.thresh
filenameRoot<-sampleInfo$filenameRoot
FCcode <- erccInfo$FCcode
#FCcode = data.frame(Ratio = c("4:1","1:1","1.5:1","2:1"),FC=c(4,1,.667,.5))
legendLabels = erccInfo$legendLabels
#legendLabels = FCcode$Ratio
colScale <- plotInfo$colScale
fillScale <- plotInfo$fillScale
### Read in results for LODR ####
### Modify this file path to work with other ERCC data
### Note that you must format your provided data in the same manner as the
### provided example
### i.e. the column headings of your selected datamust match those of the
### provided examples
if(kind == "ERCC"){
erccPval <- file.exists(paste(filenameRoot,"ERCC","Pvals.csv"))
allPval <- file.exists(paste0(filenameRoot, ".All.Pvals.csv"))
if((erccPval == TRUE)&(allPval==TRUE)){
pval.res = read.csv(file=paste(filenameRoot,"ERCC","Pvals.csv"),
header=TRUE)
}
if(allPval == TRUE){
pval.res = read.csv(file = paste0(filenameRoot, ".All.Pvals.csv"),
header = TRUE)
pval.res <- pval.res[grep("ERCC-00",pval.res$Feature),]
}else{
cat(paste0("\n",filenameRoot," ERCC Pvals.csv file is missing."))
cat("\nExiting LODR estimation...\n")
return(exDat)
}
}
if (kind == "Sim"){
if(file.exists(paste(filenameRoot,"Sim Pvals.csv"))){
pval.res<-read.csv(paste(filenameRoot,kind,"Pvals.csv"),header=TRUE)
}else{
cat(paste0("\n",filenameRoot," Sim Pvals.csv is missing.",
"\nExiting LODR estimation...\n"))
return(exDat)
}
}
names(pval.res)[1]= "Feature"
#Check number of data points, throw warning if too few
oneSet <- pval.res[which(pval.res$Fold == 1),]
sizeP <- dim(oneSet)[1]
if(!(sizeP > 5)) stop("Not enough data points for LODR estimation")
cat(paste("\nEstimating",kind,"LODR\n"))
#### Function used to estimate LODR and its uncertainty
LODR<-function(pval,mn,cutoff,prob){
cutoff<-log10(cutoff)
fit<-locfit(log10(pval)~lp(log10(mn)),maxk=300)
X<-preplot(fit,band="pred",newdata=log10(mn))
### See plot of fit
#plot(fit,band="pred",get.data=TRUE,xlim=range(log10(mn)))
find.mn<-function(mn,fit,cutoff,prob){
X<-preplot(fit,newdata=mn,band="pred")
(X$fit+qnorm(prob)*X$se.fit-cutoff)^2
}
rng.mn<-range(log10(mn))
### Search in sections to get first crossing
segmented.search<-function(fit){
X<-preplot(fit,newdata=min(log10(mn)),band="pred")
if((X$fit+qnorm(prob)*X$se.fit)<cutoff){
t.lodr<-list(minimum=min(log10(mn)),objective=0)
} else{
ppp<-.2
t.lodr<-optimize(find.mn,c(rng.mn[1],sum(rng.mn*c(1-ppp,ppp))),
fit=fit,cutoff=cutoff,prob=prob)
while(t.lodr$objective>.0001&ppp<=1){
t.lodr<-optimize(find.mn,c(sum(rng.mn*c(1-ppp+.2,ppp-.2)),
sum(rng.mn*c(1-ppp,ppp))),
fit=fit,cutoff=cutoff,prob=prob)
ppp<-ppp+.2
}
}
t.lodr
}
lodr<-segmented.search(fit)
### Bootstrap to estimate uncertainty
lodr.boot<-NULL
for(ii in 1:500){
y.boot<-X$fit+sample(residuals(fit),length(mn))
#if(ii %in%c(20*(1:5))){points(log10(mn),y.boot,col=j); j<-j+1}
fit.boot<-locfit(y.boot~lp(log10(mn)),maxk=300)
lodr.boot<-c(lodr.boot,segmented.search(fit.boot)$minimum)
if (ii %% 100 == 0){
cat ("...")
}
}
return(c(lodr$objective,lodr$minimum,quantile(lodr.boot,c(.05,.95))))
}
#### Apply to loaded data ####
lodr.resPlot<-NULL; set.seed(1)
lodr.resLess<-NULL; set.seed(1)
#lodr.res<-NULL; set.seed(1)
lineDat <-NULL
pval.res$Ratio = "Ratio"
for (i in 1:nlevels(FCcode$Ratio)){
pval.res$Ratio[which(pval.res$Fold ==
FCcode$FC[i])]=as.character(FCcode$Ratio[i])
}
#pval.res$Ratio <- as.factor(pval.res$Ratio)
pval.res$Ratio <- factor(pval.res$Ratio,
levels=as.character(FCcode$Ratio))
for(i in 1:nlevels(FCcode$Ratio)){ # Analyze fold change groups separately
grp<-which(pval.res$Ratio==FCcode$Ratio[i]) ### identify group members
x<-pval.res$MnSignal[grp][pval.res$Pval[grp]!=0]
y<-pval.res$Pval[grp][pval.res$Pval[grp]!=0]
fit<-locfit(log10(y)~lp(log10(x)),maxk=300)
#x.new<-log10(pval.res$MnSignal[grp])
x.new<-seq(min(log10(x)),max(log10(x)),length.out=100)
X<-preplot(fit,band="pred",newdata=x.new)
x.new<-10^x.new
fitLine = 10^(X$fit)
fitUpper = 10^(X$fit+qnorm(prob)*X$se.fit)
fitLower = 10^(X$fit-qnorm(prob)*X$se.fit)
fitData = data.frame(x.new,fitLine,fitUpper,fitLower,
Ratio = FCcode$Ratio[i])
lineDat = rbind(lineDat,fitData)
# ### Estimate LODR
#lodr.res<-rbind(lodr.res,c(abs(log2(FCcode$FC[i])),LODR(y,x,
# cutoff=pval.cutoff,prob=prob)))
### Estimate LODR
if(FCcode$FC[i]!=1){
t.res<-LODR(y,x,cutoff=pval.cutoff,prob=prob)
t.res[-1]<-signif(10^t.res[-1],2)
if(t.res[1]>.01){
t.res[2]<-Inf
t.res[3:4]<-NA
}
t.resLess <- t.res
t.resLess[-1][t.resLess[-1] ==
signif(min(x),2)]<-paste("<",
signif(min(x),2),sep="")
t.res[-1][t.res[-1]==signif(min(x),2)] <- Inf
if(FCcode$FC[i]==1){
t.res<-rep(NA,4)
t.resLess<-rep(NA,4)
}
lodr.resPlot<-rbind(lodr.resPlot,c(round(abs(log2(FCcode$FC[i])),3),
t.res))
lodr.resLess <- rbind(lodr.resLess,c(round(abs(log2(FCcode$FC[i])),3),
t.resLess))
}
}
pval.res = subset(pval.res,pval.res$Pval != 0)
colnames(lodr.resLess)[1:3]<-c("|log2(Fold)|","MinError","Estimate")
colnames(lodr.resPlot)[1:3]<-c("Ratio","MinError","Estimate")
colnames(lodr.resLess)[1:3]<-c("Ratio","MinError","Estimate")
lodr.resPlot <- as.data.frame(lodr.resPlot)
lodr.resLess <- as.data.frame(lodr.resLess)
lodr.resPlot$Ratio <- as.character(legendLabels[-which(FCcode$FC == 1)])
lodr.resLess$Ratio <- as.character(legendLabels[-which(FCcode$FC == 1)])
annoTable <- lodr.resLess[-c(2)]
colnames(annoTable) <- c("Ratio",expression("LODR Estimate"),
expression("90% CI Lower Bound"),
expression("90% CI Upper Bound"))
#### fix this need to get rid of the "1:1" hard coding...
#annoTable <- annoTable[-which(annoTable$Ratio == "1:1"),]
####
cat("\n")
print(annoTable, quote = FALSE, row.names = FALSE)
arrowDat = data.frame(Ratio = lodr.resPlot$Ratio,
x = lodr.resPlot[,3], y = pval.cutoff,
xend = lodr.resPlot[,3], yend = 0)
arrowDat$x[grep('<',lodr.resLess[,3])] <- Inf
#arrowDat = arrowDat[-which(arrowDat$FC == "1"),]
arrowDat = arrowDat[which(is.finite(arrowDat$x)),]
if(dim(arrowDat)[1] == 0){
cat(paste("\nWarning! Estimated distribution of p-values does not",
"cross threshold p-value,\n",
"may be due to insufficient data quantity\n",
"Consider adjusting FDR choice.\n"))
#break
}
if(exDat$sampleInfo$datType == "array"){
xlabDE <- xlab("Average Fluorescence Intensity")
xrange <- c(min(pval.res$MnSignal),max(pval.res$MnSignal))
legendPos <- theme(legend.justification=c(1,0), legend.position=c(1,0))
}
if(exDat$sampleInfo$datType == "count"){
xlabDE <- xlab("Average Counts")
xrange <- c(1,max(pval.res$MnSignal))
legendPos <- theme(legend.justification=c(0,0), legend.position=c(0,0))
}
## create inset table
my_table <- tableGrob(d=annoTable,
# show.rownames=FALSE,
rows=NULL
# gpar.coretext =gpar(fontsize=14),
# gpar.coltext=gpar(fontsize=14),
# gpar.rowtext=gpar(fontsize=14),
# gpar.corefill = gpar(fill = "grey85", col = "white"),
# gpar.rowfill = gpar(fill = "grey80", col = "white"),
# gpar.colfill = gpar(fill = "grey80", col = "white"))
)
# Appease R CMD Check
MnSignal <- Pval <- Ratio <- xend <- yend <- NULL
if(dim(arrowDat)[1]!=0){
LODRplot <- ggplot(pval.res, aes(x=MnSignal,
y=Pval,colour=Ratio)) +
geom_point(size = 6) +
scale_x_log10(limits = xrange) +
scale_y_log10(breaks = c(1e-12,1e-10,1e-8,1e-5,1e-4,
1e-3,1e-2,1e-1,1e0))+
geom_ribbon(data = lineDat, aes(x = x.new,
y = fitLine,
ymin=fitLower,
ymax=fitUpper,
fill = Ratio), alpha = 0.3,
colour=NA,show_guide=FALSE) +
geom_line(data = lineDat,aes(x = x.new,
y=fitLine,
colour = Ratio),show_guide = FALSE) +
colScale + fillScale + xlabDE + ylab("DE Test P-values") +
geom_hline(yintercept = pval.cutoff, linetype = 2, size = 2 ) +
geom_segment(data = arrowDat, aes(x = x,
y = y,
xend = xend,
yend = yend,
colour = Ratio),
lineend = "round", arrow=arrow(length=unit(0.5,"cm")),
size = 2, alpha = 0.6) + theme_bw()+ legendPos
}else{
LODRplot <- ggplot(pval.res, aes(x=MnSignal, y=Pval, colour=Ratio)) +
geom_point(size = 6) +
scale_x_log10(limits = xrange) +
scale_y_log10(breaks = c(1e-12,1e-10,1e-8,1e-5,1e-4,
1e-3,1e-2,1e-1,1e0)) +
geom_ribbon(data = lineDat, aes(x = x.new,
y = fitLine,
ymin=fitLower,
ymax=fitUpper,
fill = Ratio), alpha = 0.3,
colour= NA,show_guide=FALSE) +
geom_line(data = lineDat,aes(x = x.new,
y=fitLine,
colour = Ratio),show_guide = FALSE) +
colScale + fillScale + xlabDE + ylab("DE Test P-values") +
geom_hline(yintercept = pval.cutoff, linetype = 2, size = 2 ) +
theme_bw()+ legendPos
}
annotLODRplot <- arrangeGrob(LODRplot, arrangeGrob(my_table),
ncol = 1, heights = c(2,0.5))
nam <- paste0("lodr",kind,"Plot")
exDat$Figures$plotLODR <- annotLODRplot
#print(exDat$Figures$plotLODR)
names(exDat$Figures)[which(names(exDat$Figures) == "plotLODR")] <- nam
nam <- paste("lodr.res",kind,sep = ".")
exDat$Results$lodr.res <- lodr.resLess
names(exDat$Results)[which(names(exDat$Results) == "lodr.res")] <- nam
return(exDat)
}
arcolombo/Rccdashboard documentation built on May 10, 2019, 12:49 p.m.
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#' Estimate Limit of Detection of Ratios (LODR)
#'
#' @param exDat list, contains input data and stores analysis results
#' @param kind "ERCC" or "Sim"
#' @param prob probability, ranging from 0 - 1, default is 0.9
#'
#' @details
#' This is the function to estimate a limit of detection of ratios (LODR) for a
#' a chosen probability and threshold p-value for the fold changes in the ERCC
#' control ratio mixtures.
#'
#' @examples
#' data(SEQC.Example)
#'
#' exDat <- initDat(datType="array", isNorm=FALSE,
#' exTable=UHRR.HBRR.arrayDat,
#' filenameRoot="testRun", sample1Name="UHRR",
#' sample2Name="HBRR", erccmix="RatioPair",
#' erccdilution = 1, spikeVol = 50,
#' totalRNAmass = 2.5*10^(3), choseFDR=0.01)
#'
#' exDat <- est_r_m(exDat)
#'
#' exDat <- dynRangePlot(exDat)
#'
#' exDat <- geneExprTest(exDat)
#'
#' exDat <- estLODR(exDat, kind = "ERCC", prob = 0.9)
#'
#' exDat$Figures$lodrERCCPlot
#'
#' @export
#'
estLODR <- function(exDat,kind = "ERCC", prob=0.9){
##############
#### LODR ####
##############
### Select fitting parameters
# pval.cutoff default is .001
# probability cutoff is .9
# kind can be ERCC or Sim
sampleInfo <- exDat$sampleInfo
erccInfo <- exDat$erccInfo
plotInfo <- exDat$plotInfo
pval.cutoff<- exDat$Results$p.thresh
filenameRoot<-sampleInfo$filenameRoot
FCcode <- erccInfo$FCcode
#FCcode = data.frame(Ratio = c("4:1","1:1","1.5:1","2:1"),FC=c(4,1,.667,.5))
legendLabels = erccInfo$legendLabels
#legendLabels = FCcode$Ratio
colScale <- plotInfo$colScale
fillScale <- plotInfo$fillScale
### Read in results for LODR ####
### Modify this file path to work with other ERCC data
### Note that you must format your provided data in the same manner as the
### provided example
### i.e. the column headings of your selected datamust match those of the
### provided examples
if(kind == "ERCC"){
erccPval <- file.exists(paste(filenameRoot,"ERCC","Pvals.csv"))
allPval <- file.exists(paste0(filenameRoot, ".All.Pvals.csv"))
if((erccPval == TRUE)&(allPval==TRUE)){
pval.res = read.csv(file=paste(filenameRoot,"ERCC","Pvals.csv"),
header=TRUE)
}
if(allPval == TRUE){
pval.res = read.csv(file = paste0(filenameRoot, ".All.Pvals.csv"),
header = TRUE)
pval.res <- pval.res[grep("ERCC-00",pval.res$Feature),]
}else{
cat(paste0("\n",filenameRoot," ERCC Pvals.csv file is missing."))
cat("\nExiting LODR estimation...\n")
return(exDat)
}
}
if (kind == "Sim"){
if(file.exists(paste(filenameRoot,"Sim Pvals.csv"))){
pval.res<-read.csv(paste(filenameRoot,kind,"Pvals.csv"),header=TRUE)
}else{
cat(paste0("\n",filenameRoot," Sim Pvals.csv is missing.",
"\nExiting LODR estimation...\n"))
return(exDat)
}
}
names(pval.res)[1]= "Feature"
#Check number of data points, throw warning if too few
oneSet <- pval.res[which(pval.res$Fold == 1),]
sizeP <- dim(oneSet)[1]
if(!(sizeP > 5)) stop("Not enough data points for LODR estimation")
cat(paste("\nEstimating",kind,"LODR\n"))
#### Function used to estimate LODR and its uncertainty
LODR<-function(pval,mn,cutoff,prob){
cutoff<-log10(cutoff)
fit<-locfit(log10(pval)~lp(log10(mn)),maxk=300)
X<-preplot(fit,band="pred",newdata=log10(mn))
### See plot of fit
#plot(fit,band="pred",get.data=TRUE,xlim=range(log10(mn)))
find.mn<-function(mn,fit,cutoff,prob){
X<-preplot(fit,newdata=mn,band="pred")
(X$fit+qnorm(prob)*X$se.fit-cutoff)^2
}
rng.mn<-range(log10(mn))
### Search in sections to get first crossing
segmented.search<-function(fit){
X<-preplot(fit,newdata=min(log10(mn)),band="pred")
if((X$fit+qnorm(prob)*X$se.fit)<cutoff){
t.lodr<-list(minimum=min(log10(mn)),objective=0)
} else{
ppp<-.2
t.lodr<-optimize(find.mn,c(rng.mn[1],sum(rng.mn*c(1-ppp,ppp))),
fit=fit,cutoff=cutoff,prob=prob)
while(t.lodr$objective>.0001&ppp<=1){
t.lodr<-optimize(find.mn,c(sum(rng.mn*c(1-ppp+.2,ppp-.2)),
sum(rng.mn*c(1-ppp,ppp))),
fit=fit,cutoff=cutoff,prob=prob)
ppp<-ppp+.2
}
}
t.lodr
}
lodr<-segmented.search(fit)
### Bootstrap to estimate uncertainty
lodr.boot<-NULL
for(ii in 1:500){
y.boot<-X$fit+sample(residuals(fit),length(mn))
#if(ii %in%c(20*(1:5))){points(log10(mn),y.boot,col=j); j<-j+1}
fit.boot<-locfit(y.boot~lp(log10(mn)),maxk=300)
lodr.boot<-c(lodr.boot,segmented.search(fit.boot)$minimum)
if (ii %% 100 == 0){
cat ("...")
}
}
return(c(lodr$objective,lodr$minimum,quantile(lodr.boot,c(.05,.95))))
}
#### Apply to loaded data ####
lodr.resPlot<-NULL; set.seed(1)
lodr.resLess<-NULL; set.seed(1)
#lodr.res<-NULL; set.seed(1)
lineDat <-NULL
pval.res$Ratio = "Ratio"
for (i in 1:nlevels(FCcode$Ratio)){
pval.res$Ratio[which(pval.res$Fold ==
FCcode$FC[i])]=as.character(FCcode$Ratio[i])
}
#pval.res$Ratio <- as.factor(pval.res$Ratio)
pval.res$Ratio <- factor(pval.res$Ratio,
levels=as.character(FCcode$Ratio))
for(i in 1:nlevels(FCcode$Ratio)){ # Analyze fold change groups separately
grp<-which(pval.res$Ratio==FCcode$Ratio[i]) ### identify group members
x<-pval.res$MnSignal[grp][pval.res$Pval[grp]!=0]
y<-pval.res$Pval[grp][pval.res$Pval[grp]!=0]
fit<-locfit(log10(y)~lp(log10(x)),maxk=300)
#x.new<-log10(pval.res$MnSignal[grp])
x.new<-seq(min(log10(x)),max(log10(x)),length.out=100)
X<-preplot(fit,band="pred",newdata=x.new)
x.new<-10^x.new
fitLine = 10^(X$fit)
fitUpper = 10^(X$fit+qnorm(prob)*X$se.fit)
fitLower = 10^(X$fit-qnorm(prob)*X$se.fit)
fitData = data.frame(x.new,fitLine,fitUpper,fitLower,
Ratio = FCcode$Ratio[i])
lineDat = rbind(lineDat,fitData)
# ### Estimate LODR
#lodr.res<-rbind(lodr.res,c(abs(log2(FCcode$FC[i])),LODR(y,x,
# cutoff=pval.cutoff,prob=prob)))
### Estimate LODR
if(FCcode$FC[i]!=1){
t.res<-LODR(y,x,cutoff=pval.cutoff,prob=prob)
t.res[-1]<-signif(10^t.res[-1],2)
if(t.res[1]>.01){
t.res[2]<-Inf
t.res[3:4]<-NA
}
t.resLess <- t.res
t.resLess[-1][t.resLess[-1] ==
signif(min(x),2)]<-paste("<",
signif(min(x),2),sep="")
t.res[-1][t.res[-1]==signif(min(x),2)] <- Inf
if(FCcode$FC[i]==1){
t.res<-rep(NA,4)
t.resLess<-rep(NA,4)
}
lodr.resPlot<-rbind(lodr.resPlot,c(round(abs(log2(FCcode$FC[i])),3),
t.res))
lodr.resLess <- rbind(lodr.resLess,c(round(abs(log2(FCcode$FC[i])),3),
t.resLess))
}
}
pval.res = subset(pval.res,pval.res$Pval != 0)
colnames(lodr.resLess)[1:3]<-c("|log2(Fold)|","MinError","Estimate")
colnames(lodr.resPlot)[1:3]<-c("Ratio","MinError","Estimate")
colnames(lodr.resLess)[1:3]<-c("Ratio","MinError","Estimate")
lodr.resPlot <- as.data.frame(lodr.resPlot)
lodr.resLess <- as.data.frame(lodr.resLess)
lodr.resPlot$Ratio <- as.character(legendLabels[-which(FCcode$FC == 1)])
lodr.resLess$Ratio <- as.character(legendLabels[-which(FCcode$FC == 1)])
annoTable <- lodr.resLess[-c(2)]
colnames(annoTable) <- c("Ratio",expression("LODR Estimate"),
expression("90% CI Lower Bound"),
expression("90% CI Upper Bound"))
#### fix this need to get rid of the "1:1" hard coding...
#annoTable <- annoTable[-which(annoTable$Ratio == "1:1"),]
####
cat("\n")
print(annoTable, quote = FALSE, row.names = FALSE)
arrowDat = data.frame(Ratio = lodr.resPlot$Ratio,
x = lodr.resPlot[,3], y = pval.cutoff,
xend = lodr.resPlot[,3], yend = 0)
arrowDat$x[grep('<',lodr.resLess[,3])] <- Inf
#arrowDat = arrowDat[-which(arrowDat$FC == "1"),]
arrowDat = arrowDat[which(is.finite(arrowDat$x)),]
if(dim(arrowDat)[1] == 0){
cat(paste("\nWarning! Estimated distribution of p-values does not",
"cross threshold p-value,\n",
"may be due to insufficient data quantity\n",
"Consider adjusting FDR choice.\n"))
#break
}
if(exDat$sampleInfo$datType == "array"){
xlabDE <- xlab("Average Fluorescence Intensity")
xrange <- c(min(pval.res$MnSignal),max(pval.res$MnSignal))
legendPos <- theme(legend.justification=c(1,0), legend.position=c(1,0))
}
if(exDat$sampleInfo$datType == "count"){
xlabDE <- xlab("Average Counts")
xrange <- c(1,max(pval.res$MnSignal))
legendPos <- theme(legend.justification=c(0,0), legend.position=c(0,0))
}
## create inset table
my_table <- tableGrob(d=annoTable,
# show.rownames=FALSE,
rows=NULL
# gpar.coretext =gpar(fontsize=14),
# gpar.coltext=gpar(fontsize=14),
# gpar.rowtext=gpar(fontsize=14),
# gpar.corefill = gpar(fill = "grey85", col = "white"),
# gpar.rowfill = gpar(fill = "grey80", col = "white"),
# gpar.colfill = gpar(fill = "grey80", col = "white"))
)
# Appease R CMD Check
MnSignal <- Pval <- Ratio <- xend <- yend <- NULL
if(dim(arrowDat)[1]!=0){
LODRplot <- ggplot(pval.res, aes(x=MnSignal,
y=Pval,colour=Ratio)) +
geom_point(size = 6) +
scale_x_log10(limits = xrange) +
scale_y_log10(breaks = c(1e-12,1e-10,1e-8,1e-5,1e-4,
1e-3,1e-2,1e-1,1e0))+
geom_ribbon(data = lineDat, aes(x = x.new,
y = fitLine,
ymin=fitLower,
ymax=fitUpper,
fill = Ratio), alpha = 0.3,
colour=NA,show_guide=FALSE) +
geom_line(data = lineDat,aes(x = x.new,
y=fitLine,
colour = Ratio),show_guide = FALSE) +
colScale + fillScale + xlabDE + ylab("DE Test P-values") +
geom_hline(yintercept = pval.cutoff, linetype = 2, size = 2 ) +
geom_segment(data = arrowDat, aes(x = x,
y = y,
xend = xend,
yend = yend,
colour = Ratio),
lineend = "round", arrow=arrow(length=unit(0.5,"cm")),
size = 2, alpha = 0.6) + theme_bw()+ legendPos
}else{
LODRplot <- ggplot(pval.res, aes(x=MnSignal, y=Pval, colour=Ratio)) +
geom_point(size = 6) +
scale_x_log10(limits = xrange) +
scale_y_log10(breaks = c(1e-12,1e-10,1e-8,1e-5,1e-4,
1e-3,1e-2,1e-1,1e0)) +
geom_ribbon(data = lineDat, aes(x = x.new,
y = fitLine,
ymin=fitLower,
ymax=fitUpper,
fill = Ratio), alpha = 0.3,
colour= NA,show_guide=FALSE) +
geom_line(data = lineDat,aes(x = x.new,
y=fitLine,
colour = Ratio),show_guide = FALSE) +
colScale + fillScale + xlabDE + ylab("DE Test P-values") +
geom_hline(yintercept = pval.cutoff, linetype = 2, size = 2 ) +
theme_bw()+ legendPos
}
annotLODRplot <- arrangeGrob(LODRplot, arrangeGrob(my_table),
ncol = 1, heights = c(2,0.5))
nam <- paste0("lodr",kind,"Plot")
exDat$Figures$plotLODR <- annotLODRplot
#print(exDat$Figures$plotLODR)
names(exDat$Figures)[which(names(exDat$Figures) == "plotLODR")] <- nam
nam <- paste("lodr.res",kind,sep = ".")
exDat$Results$lodr.res <- lodr.resLess
names(exDat$Results)[which(names(exDat$Results) == "lodr.res")] <- nam
return(exDat)
}
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