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R/maSignal.R
In erccdashboard: Assess Differential Gene Expression Experiments with ERCC Controls
Defines functions
maSignal
Documented in
maSignal
#' Generate MA plots with or without annotation using LODR estimates
#'
#' @param exDat list, contains input data and stores analysis results
#' @param alphaPoint numeric value, for alpha (transparency) for plotted points,
#' range is 0 - 1
#' @param r_mAdjust default is TRUE, if FALSE then the r_m estimate will not
#' used to offset dashed lines for empirical ratios on figure
#' @param replicate default is TRUE, if FALSE then error bars will not be
#' produced
#'
#' @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)
#' # generate MA plot without LODR annotation
#' exDat <- maSignal(exDat)
#'
#' exDat$Figures$maPlot
#' \donttest{
#' exDat <- estLODR(exDat, kind = "ERCC", prob = 0.9)
#'
#' # Include LODR annotation
#' exDat <- annotLODR(exDat)
#'
#' exDat$Figures$maPlot
#' }
#' @export
# Plots with target ratios and R adjusted ratios, MA plots and Ratio Summaries
maSignal <-function(exDat, alphaPoint=0.8, r_mAdjust=TRUE, replicate=TRUE){
#ReplicateName = "Rep"
# Melt the data
#exDat <- meltexDat(exDat, cnt = exDat$Transcripts,
# designMat = exDat$designMat)
#countPair <- exDat$expressDat_l
sampleInfo <- exDat$sampleInfo
erccInfo <- exDat$erccInfo
plotInfo <- exDat$plotInfo
cnt <- exDat$Transcripts
designMat <- exDat$designMat
sampleInfo <- exDat$sampleInfo
normFactor <- exDat$normFactor
datNames <- colnames(designMat)[-1]
sample1 <- exDat$sampleNames[1]
sample2 <- exDat$sampleNames[2]
if (is.null(exDat$idColsAdj)){
idColsAdj <- exDat$idCols
}else{
idColsAdj <- exDat$idColsAdj
}
datCols = cnt[-c(1)]
if (!is.null(normFactor)){
libAdjust = sweep(datCols, 2, normFactor,"/")
sampleLibeDataNorm = cbind(cnt[c(1)],libAdjust)
myDataERCC = sampleLibeDataNorm
#expressDat = myDataERCC[-c(1)]
}else{
myDataERCC = cnt
}
if(exDat$sampleInfo$isNorm==TRUE){
myDataERCC = cnt
}
sampleNameList = c(sample1,sample2)
dat = cbind(Feature = myDataERCC[c(1)], Ratio = "Endo",
myDataERCC[-c(1)])
dat$Feature <- as.factor(as.character(
dat$Feature))
dat$Ratio <- as.character(dat$Ratio)
#idx1 <- suppressWarnings(which(as.character(dat$Feature) ==
# as.character(exDat$idColsAdj$Feature)))
idxERCC <- grep("ERCC-",dat$Feature)
for (i in 1:length(idxERCC)){
dat$Ratio[i] <- as.character(idColsAdj$Ratio)[match(dat$Feature[i],
idColsAdj$Feature)]
}
#idx1 <- match( exDat$idColsAdj$Feature, dat$Feature,
# nomatch=FALSE)
#dat$Ratio[idx1] <- as.character(exDat$idColsAdj[idx1,c(4)])
dat$Ratio <- as.factor(dat$Ratio)
myYLim = plotInfo$myYLimMA
myXLimMA = plotInfo$myXLimMA
xlimMArange <- myXLimMA[2]-myXLimMA[1]
if (xlimMArange > (plotInfo$myYLim[2] - plotInfo$myYLim[1])){
myXLimMA <- c(plotInfo$myYLim[1],plotInfo$myYLim[2])
}
filenameRoot = sampleInfo$filenameRoot
sample1 = exDat$sampleNames[1]
sample2 = exDat$sampleNames[2]
#idCols = exDat$idColsAdj
r_m.res = exDat$Results$r_m.res
#cutoffs = LODR.annot.ERCC$cutoffs
cutoffs = exDat$Results$LODR.annot.ERCC$AbundCutoffs
FCcode = erccInfo$FCcode
legendLabels=erccInfo$legendLabels
spikeFraction = exDat$spikeFraction
r_m.mn = r_m.res$r_m.mn
r_m.mnse = r_m.res$r_m.mnse
if(is.null(r_m.mn)) {
r_mAdjust = FALSE
}
#theme_update(legend.justification=c(1,0), legend.position=c(1,0))
colScale <- plotInfo$colScale
fillScale <- plotInfo$fillScale
if (length(cutoffs) > 0){
cat("\nLODR estimates are available to code ratio-abundance plot\n")
}
maStats <- function(x, c1, c2){
c(mean(log2(x[c1])-log2(x[c2])),sd(log2(x[c1])-log2(x[c2])),log2(mean(x)))
}
totCol <- ncol(dat[-c(1:2)])
if(odd(totCol)) stop("Uneven number of replicates for the two sample types")
maStatDat <- data.frame(t(apply(dat[-c(1:2)],1,maStats,
c1 = c(1:(totCol/2)),
c2 = c(((totCol/2)+1):totCol))))
colnames(maStatDat) <- c("M.Ave","M.SD","A")
maDatAll <- cbind(dat, maStatDat)
maDatAll <- maDatAll[which(is.finite(maDatAll$M.Ave)),]
### Now subset and continue with just ERCCs
maData <- subset(maDatAll, Ratio != "Endo")
maData$Ratio <- factor(as.character(maData$Ratio),levels=FCcode$Ratio)
maData$Feature <- as.factor(as.character(maData$Feature))
#countPair <- subset(exDat$expressDat_l, subset = Ratio != "Endo")
#countPair$Ratio <- as.factor(as.character(countPair$Ratio))
maData$Nominal = FCcode$FC[1]
FCcode$Ratio <- as.factor(FCcode$Ratio)
for (i in 2:nlevels(FCcode$Ratio)){
maData$Nominal[which(maData$Ratio == FCcode$Ratio[i])] = FCcode$FC[i]
}
if(r_mAdjust == TRUE){
maData$Empirical = maData$Nominal/exp(r_m.mn)
}else{
maData$Empirical = maData$Nominal
}
cutERCCs = unique(maData$Feature[which(is.na(maData$M.SD))])
if(length(cutERCCs) != 0){
cat(paste("\nThese ERCCs were not included in the ratio-abundance",
"plot, \n",
"because not enough non-zero replicate measurements of",
"these \n",
"controls were obtained for both samples:\n"))
cat(paste(as.character(cutERCCs),collapse="\n"))
maData = maData[-(which(maData$Feature %in% cutERCCs)),]
searchCut = paste(cutERCCs,collapse="|")
maData = maData[-(grep(searchCut,maData$Feature)),]
}
#maDataAveSD$Feature = as.factor(as.character(maDataAveSD$Feature))
maData$Feature = as.factor(as.character(maData$Feature))
#write.csv(maData,file = paste(filenameRoot,"maDataFinite.csv",sep = "."))
#avexlabel = exDat$ERCCxlabelAve
if(sampleInfo$datType == "count"){
avexlabel = "Log2 Average of Normalized Counts"
ymalabel = "Log2 Ratio of Normalized Counts"
}
if(sampleInfo$datType == "array"){
avexlabel = "Log2 Average of Normalized Intensity"
ymalabel = "Log2 Ratio of Normalized Intensity"
# myXLimMA = c(min(maData$A)-1, max(maData$A)+1)
# exDat$plotInfo$myXLimMA <- myXLimMA
}
xlabel = xlab(avexlabel)
# Estimate SD for all ratio measurements
# For the ERCCs that are plotted take the log ratio data and subtract the
## log nominal ratio, this will normalize the data
normRats = maData$M.Ave - log2(maData$Nominal)
#print(normRats)
# Take the sd of all of the normalized log ratio data to find a global SD
##for the ERCCs at this site
sdGlobal = sd(normRats)
#cat("\nGlobal Ratio SD for this sample pair is: ")
#cat(sdGlobal, "\n")
ratVarDat <- maData
#ratVarDat <- subset(ratVarDat, A > 0)
#ratVarDat$A <- log2((2^(ratVarDat$A.Ave))/(spikeFraction))
# set null values to appease R CMD Check
Ratio <- A <- M.Ave <- M.SD <- LODR <- Nominal <- Empirical <- NULL
if (length(cutoffs)>0){
maData$LODR = "below"
FCcodeC = FCcode[-c(which(FCcode$FC == 1)),]
for (i in 1:length(cutoffs)){
maData$LODR[which((maData$A > cutoffs[i])&
(maData$Ratio ==
FCcodeC$Ratio[i]))] = "above"
}
maData$LODR <- as.factor(maData$LODR)
if (is.null(r_m.mn)){
rm_dat = data.frame("Mean r_m" = as.character("'No Estimate'"),
"Weighted SE" = as.character(" "))
}else{
rm_dat = data.frame("Mean r_m" = signif(r_m.mn,digits = 4),
"Weighted SE" = signif(r_m.mnse,digits = 4))
}
colnames(rm_dat) <- c("'Weighted Mean'",
"'(+/-) Weighted Standard Error'")
rownames(rm_dat) <- expression(log(r[m]))
tt <- ttheme_default(colhead=list(fg_params = list(parse=TRUE)),
rowhead=list(fg_params = list(parse=TRUE)))
# Plot ratio-signal data coding points below the LODR with open circles
maPlot <- ggplot(maData, aes(x = A, y = M.Ave) ) +
geom_point(data = subset(maDatAll, maDatAll$Ratio == "Endo"),
aes(x = A, y = M.Ave),
colour = "grey80", alpha = 0.5) +
geom_errorbar(aes(ymax = M.Ave + M.SD, ymin = M.Ave - M.SD,
colour = Ratio),size = 1,alpha = alphaPoint) +
geom_point(aes(colour = Ratio),size = 5, alpha = alphaPoint) +
geom_point(data = subset(maData, (LODR == "below")),
colour = "white",size = 2.5) +
geom_hline(aes(yintercept = log2(Nominal), colour = Ratio),
alpha = 0.7) +
geom_hline(aes(yintercept = log2(Empirical), colour = Ratio),
size = 1, linetype = "longdash") +
ylab(ymalabel) + xlabel +
coord_cartesian(xlim = myXLimMA, ylim = myYLim) + colScale +
annotation_custom(tableGrob(rm_dat,theme=tt),
# gpar.corefill = gpar(fill = "grey85",
# col = "white"),
# gpar.rowfill = gpar(fill = "grey80",
# col = "white"),
# gpar.colfill = gpar(fill = "grey80",
# col = "white")),
#xmin = quantile(maData$A,probs=0.25),
#xmax = max(maData$A),
ymin = (myYLim[2]) - 0.25*myYLim[2],
ymax = myYLim[2]) +
scale_y_continuous(breaks = seq(myYLim[1],myYLim[2],1))+ theme_bw()+
theme( legend.justification = c(1,0),legend.position=c(1,0)) +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank())
}else{
# Plot ratio-signal data without LODR coding
maPlot <- ggplot(maData, aes(x = A, y = M.Ave, colour = Ratio) ) +
geom_errorbar(aes(ymax = M.Ave + M.SD, ymin = M.Ave - M.SD),
size = 1, alpha = alphaPoint) +
geom_point(size = 5, alpha = alphaPoint) +
geom_hline(aes(yintercept = log2(Nominal), colour = Ratio),
alpha = 0.7) +
geom_hline(aes(yintercept = log2(Empirical), colour = Ratio),
size = 1, linetype = "longdash") +
ylab(ymalabel) + xlabel + colScale+
coord_cartesian(xlim = myXLimMA, ylim = myYLim) + theme_bw()+
theme( legend.justification = c(1,0),legend.position=c(1,0)) +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank())
}
ratioVarPlot <- ggplot(maData) + geom_violin(aes(x = Ratio,
y = M.SD,
fill = Ratio),
alpha = alphaPoint) +
ylab("SD of Log2 Ratios") + colScale + fillScale + theme_bw()
exDat$Figures$maPlot <- maPlot
#exDat$Results$ratVarDat <- ratVarDat
#exDat$Results$modRatVar <- stdevCoef
exDat$Results$maDatAll <- maDatAll
#exDat$Figures$ratioSDPlot <- sdRatioplotFit
#exDat$Figures$ratioSDPlot <- ratioVarPlot
return(exDat)
}
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Defines functions maSignal
Documented in maSignal
#' Generate MA plots with or without annotation using LODR estimates
#'
#' @param exDat list, contains input data and stores analysis results
#' @param alphaPoint numeric value, for alpha (transparency) for plotted points,
#' range is 0 - 1
#' @param r_mAdjust default is TRUE, if FALSE then the r_m estimate will not
#' used to offset dashed lines for empirical ratios on figure
#' @param replicate default is TRUE, if FALSE then error bars will not be
#' produced
#'
#' @examples
#'
#' data(SEQC.Example)
#'
#' exDat <- initDat(datType="array", isNorm=FALSE,
#' exTable=UHRR.HBRR.arrayDat,
#' filenameRoot="testRun", sample1Name="UHRR",
#' sample2Name="HBRR", erccmix="RatioPair",
#' erccdilution = 1, spikeVol = 50,
#' totalRNAmass = 2.5*10^(3), choseFDR=0.01)
#'
#' exDat <- est_r_m(exDat)
#'
#' exDat <- dynRangePlot(exDat)
#'
#' exDat <- geneExprTest(exDat)
#' # generate MA plot without LODR annotation
#' exDat <- maSignal(exDat)
#'
#' exDat$Figures$maPlot
#' \donttest{
#' exDat <- estLODR(exDat, kind = "ERCC", prob = 0.9)
#'
#' # Include LODR annotation
#' exDat <- annotLODR(exDat)
#'
#' exDat$Figures$maPlot
#' }
#' @export
# Plots with target ratios and R adjusted ratios, MA plots and Ratio Summaries
maSignal <-function(exDat, alphaPoint=0.8, r_mAdjust=TRUE, replicate=TRUE){
#ReplicateName = "Rep"
# Melt the data
#exDat <- meltexDat(exDat, cnt = exDat$Transcripts,
# designMat = exDat$designMat)
#countPair <- exDat$expressDat_l
sampleInfo <- exDat$sampleInfo
erccInfo <- exDat$erccInfo
plotInfo <- exDat$plotInfo
cnt <- exDat$Transcripts
designMat <- exDat$designMat
sampleInfo <- exDat$sampleInfo
normFactor <- exDat$normFactor
datNames <- colnames(designMat)[-1]
sample1 <- exDat$sampleNames[1]
sample2 <- exDat$sampleNames[2]
if (is.null(exDat$idColsAdj)){
idColsAdj <- exDat$idCols
}else{
idColsAdj <- exDat$idColsAdj
}
datCols = cnt[-c(1)]
if (!is.null(normFactor)){
libAdjust = sweep(datCols, 2, normFactor,"/")
sampleLibeDataNorm = cbind(cnt[c(1)],libAdjust)
myDataERCC = sampleLibeDataNorm
#expressDat = myDataERCC[-c(1)]
}else{
myDataERCC = cnt
}
if(exDat$sampleInfo$isNorm==TRUE){
myDataERCC = cnt
}
sampleNameList = c(sample1,sample2)
dat = cbind(Feature = myDataERCC[c(1)], Ratio = "Endo",
myDataERCC[-c(1)])
dat$Feature <- as.factor(as.character(
dat$Feature))
dat$Ratio <- as.character(dat$Ratio)
#idx1 <- suppressWarnings(which(as.character(dat$Feature) ==
# as.character(exDat$idColsAdj$Feature)))
idxERCC <- grep("ERCC-",dat$Feature)
for (i in 1:length(idxERCC)){
dat$Ratio[i] <- as.character(idColsAdj$Ratio)[match(dat$Feature[i],
idColsAdj$Feature)]
}
#idx1 <- match( exDat$idColsAdj$Feature, dat$Feature,
# nomatch=FALSE)
#dat$Ratio[idx1] <- as.character(exDat$idColsAdj[idx1,c(4)])
dat$Ratio <- as.factor(dat$Ratio)
myYLim = plotInfo$myYLimMA
myXLimMA = plotInfo$myXLimMA
xlimMArange <- myXLimMA[2]-myXLimMA[1]
if (xlimMArange > (plotInfo$myYLim[2] - plotInfo$myYLim[1])){
myXLimMA <- c(plotInfo$myYLim[1],plotInfo$myYLim[2])
}
filenameRoot = sampleInfo$filenameRoot
sample1 = exDat$sampleNames[1]
sample2 = exDat$sampleNames[2]
#idCols = exDat$idColsAdj
r_m.res = exDat$Results$r_m.res
#cutoffs = LODR.annot.ERCC$cutoffs
cutoffs = exDat$Results$LODR.annot.ERCC$AbundCutoffs
FCcode = erccInfo$FCcode
legendLabels=erccInfo$legendLabels
spikeFraction = exDat$spikeFraction
r_m.mn = r_m.res$r_m.mn
r_m.mnse = r_m.res$r_m.mnse
if(is.null(r_m.mn)) {
r_mAdjust = FALSE
}
#theme_update(legend.justification=c(1,0), legend.position=c(1,0))
colScale <- plotInfo$colScale
fillScale <- plotInfo$fillScale
if (length(cutoffs) > 0){
cat("\nLODR estimates are available to code ratio-abundance plot\n")
}
maStats <- function(x, c1, c2){
c(mean(log2(x[c1])-log2(x[c2])),sd(log2(x[c1])-log2(x[c2])),log2(mean(x)))
}
totCol <- ncol(dat[-c(1:2)])
if(odd(totCol)) stop("Uneven number of replicates for the two sample types")
maStatDat <- data.frame(t(apply(dat[-c(1:2)],1,maStats,
c1 = c(1:(totCol/2)),
c2 = c(((totCol/2)+1):totCol))))
colnames(maStatDat) <- c("M.Ave","M.SD","A")
maDatAll <- cbind(dat, maStatDat)
maDatAll <- maDatAll[which(is.finite(maDatAll$M.Ave)),]
### Now subset and continue with just ERCCs
maData <- subset(maDatAll, Ratio != "Endo")
maData$Ratio <- factor(as.character(maData$Ratio),levels=FCcode$Ratio)
maData$Feature <- as.factor(as.character(maData$Feature))
#countPair <- subset(exDat$expressDat_l, subset = Ratio != "Endo")
#countPair$Ratio <- as.factor(as.character(countPair$Ratio))
maData$Nominal = FCcode$FC[1]
FCcode$Ratio <- as.factor(FCcode$Ratio)
for (i in 2:nlevels(FCcode$Ratio)){
maData$Nominal[which(maData$Ratio == FCcode$Ratio[i])] = FCcode$FC[i]
}
if(r_mAdjust == TRUE){
maData$Empirical = maData$Nominal/exp(r_m.mn)
}else{
maData$Empirical = maData$Nominal
}
cutERCCs = unique(maData$Feature[which(is.na(maData$M.SD))])
if(length(cutERCCs) != 0){
cat(paste("\nThese ERCCs were not included in the ratio-abundance",
"plot, \n",
"because not enough non-zero replicate measurements of",
"these \n",
"controls were obtained for both samples:\n"))
cat(paste(as.character(cutERCCs),collapse="\n"))
maData = maData[-(which(maData$Feature %in% cutERCCs)),]
searchCut = paste(cutERCCs,collapse="|")
maData = maData[-(grep(searchCut,maData$Feature)),]
}
#maDataAveSD$Feature = as.factor(as.character(maDataAveSD$Feature))
maData$Feature = as.factor(as.character(maData$Feature))
#write.csv(maData,file = paste(filenameRoot,"maDataFinite.csv",sep = "."))
#avexlabel = exDat$ERCCxlabelAve
if(sampleInfo$datType == "count"){
avexlabel = "Log2 Average of Normalized Counts"
ymalabel = "Log2 Ratio of Normalized Counts"
}
if(sampleInfo$datType == "array"){
avexlabel = "Log2 Average of Normalized Intensity"
ymalabel = "Log2 Ratio of Normalized Intensity"
# myXLimMA = c(min(maData$A)-1, max(maData$A)+1)
# exDat$plotInfo$myXLimMA <- myXLimMA
}
xlabel = xlab(avexlabel)
# Estimate SD for all ratio measurements
# For the ERCCs that are plotted take the log ratio data and subtract the
## log nominal ratio, this will normalize the data
normRats = maData$M.Ave - log2(maData$Nominal)
#print(normRats)
# Take the sd of all of the normalized log ratio data to find a global SD
##for the ERCCs at this site
sdGlobal = sd(normRats)
#cat("\nGlobal Ratio SD for this sample pair is: ")
#cat(sdGlobal, "\n")
ratVarDat <- maData
#ratVarDat <- subset(ratVarDat, A > 0)
#ratVarDat$A <- log2((2^(ratVarDat$A.Ave))/(spikeFraction))
# set null values to appease R CMD Check
Ratio <- A <- M.Ave <- M.SD <- LODR <- Nominal <- Empirical <- NULL
if (length(cutoffs)>0){
maData$LODR = "below"
FCcodeC = FCcode[-c(which(FCcode$FC == 1)),]
for (i in 1:length(cutoffs)){
maData$LODR[which((maData$A > cutoffs[i])&
(maData$Ratio ==
FCcodeC$Ratio[i]))] = "above"
}
maData$LODR <- as.factor(maData$LODR)
if (is.null(r_m.mn)){
rm_dat = data.frame("Mean r_m" = as.character("'No Estimate'"),
"Weighted SE" = as.character(" "))
}else{
rm_dat = data.frame("Mean r_m" = signif(r_m.mn,digits = 4),
"Weighted SE" = signif(r_m.mnse,digits = 4))
}
colnames(rm_dat) <- c("'Weighted Mean'",
"'(+/-) Weighted Standard Error'")
rownames(rm_dat) <- expression(log(r[m]))
tt <- ttheme_default(colhead=list(fg_params = list(parse=TRUE)),
rowhead=list(fg_params = list(parse=TRUE)))
# Plot ratio-signal data coding points below the LODR with open circles
maPlot <- ggplot(maData, aes(x = A, y = M.Ave) ) +
geom_point(data = subset(maDatAll, maDatAll$Ratio == "Endo"),
aes(x = A, y = M.Ave),
colour = "grey80", alpha = 0.5) +
geom_errorbar(aes(ymax = M.Ave + M.SD, ymin = M.Ave - M.SD,
colour = Ratio),size = 1,alpha = alphaPoint) +
geom_point(aes(colour = Ratio),size = 5, alpha = alphaPoint) +
geom_point(data = subset(maData, (LODR == "below")),
colour = "white",size = 2.5) +
geom_hline(aes(yintercept = log2(Nominal), colour = Ratio),
alpha = 0.7) +
geom_hline(aes(yintercept = log2(Empirical), colour = Ratio),
size = 1, linetype = "longdash") +
ylab(ymalabel) + xlabel +
coord_cartesian(xlim = myXLimMA, ylim = myYLim) + colScale +
annotation_custom(tableGrob(rm_dat,theme=tt),
# gpar.corefill = gpar(fill = "grey85",
# col = "white"),
# gpar.rowfill = gpar(fill = "grey80",
# col = "white"),
# gpar.colfill = gpar(fill = "grey80",
# col = "white")),
#xmin = quantile(maData$A,probs=0.25),
#xmax = max(maData$A),
ymin = (myYLim[2]) - 0.25*myYLim[2],
ymax = myYLim[2]) +
scale_y_continuous(breaks = seq(myYLim[1],myYLim[2],1))+ theme_bw()+
theme( legend.justification = c(1,0),legend.position=c(1,0)) +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank())
}else{
# Plot ratio-signal data without LODR coding
maPlot <- ggplot(maData, aes(x = A, y = M.Ave, colour = Ratio) ) +
geom_errorbar(aes(ymax = M.Ave + M.SD, ymin = M.Ave - M.SD),
size = 1, alpha = alphaPoint) +
geom_point(size = 5, alpha = alphaPoint) +
geom_hline(aes(yintercept = log2(Nominal), colour = Ratio),
alpha = 0.7) +
geom_hline(aes(yintercept = log2(Empirical), colour = Ratio),
size = 1, linetype = "longdash") +
ylab(ymalabel) + xlabel + colScale+
coord_cartesian(xlim = myXLimMA, ylim = myYLim) + theme_bw()+
theme( legend.justification = c(1,0),legend.position=c(1,0)) +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank())
}
ratioVarPlot <- ggplot(maData) + geom_violin(aes(x = Ratio,
y = M.SD,
fill = Ratio),
alpha = alphaPoint) +
ylab("SD of Log2 Ratios") + colScale + fillScale + theme_bw()
exDat$Figures$maPlot <- maPlot
#exDat$Results$ratVarDat <- ratVarDat
#exDat$Results$modRatVar <- stdevCoef
exDat$Results$maDatAll <- maDatAll
#exDat$Figures$ratioSDPlot <- sdRatioplotFit
#exDat$Figures$ratioSDPlot <- ratioVarPlot
return(exDat)
}
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