MAboxplot<-function(gene=NULL,
PDObj,
alpha=0.8,
dot.size=6,
box.size=1,
box.width=1,
reorder=NULL,
stat.test="limma",
annotate=TRUE,
p.value=0.05,
sampleNames=NULL,
error.bars=TRUE,
print=TRUE,
dot.colors=NULL,
gene.short=F,
gene.short.name="Symbol"){
if(class(PDObj)!="PDObj"){stop("PDObj does not appear correct")}
if(is.null(gene)==TRUE){stop("No gene input")}
if(gene.short){if(!gene.short.name %in% colnames(fData(PDObj@eset))){stop("Gene Short Column not found in fData of PD Object")}}
ColObj<-PDObj@ColObj
classvec<-as.factor(ColObj@classvec)
line.cols<-ColObj@match$line
if(is.null(dot.colors)==TRUE) {dot.fill.cols<-ColObj@match$fill} else {
if(length(dot.colors)!=length(levels(classvec))){stop("Dot colors must be equal to the number of factors")}
else{dot.fill.cols<-dot.colors}
}
box.fill.cols<-ColObj@match$fill
if(is.null(reorder)==FALSE){classvec<-factor(classvec,levels(classvec)[reorder])
line.cols<-line.cols[reorder]
dot.fill.cols<-dot.fill.cols[reorder]
box.fill.cols<-box.fill.cols[reorder]
}
if(is.null(ColObj)==TRUE){classvec<-factor(classvec,levels(classvec)[reorder])
line.cols<-line.cols[reorder]
dot.fill.cols<-dot.fill.cols[reorder]
box.fill.cols<-box.fill.cols[reorder]
}
eset<-PDObj@eset
obj<-Biobase::exprs(eset)[gene,]
df<-data.frame(gp=classvec, y=obj)
maxh <- max(df$y)
minh<-min(df$y)
spread<-(maxh-minh)/14
maxh<-maxh+spread
df$maxh<-maxh
df$spread<-spread
ds <- plyr::ddply(df, plyr::as.quoted("gp"), plyr::summarise, mean = mean(y), sd = sd(y))
if(stat.test=="pairwiset"){
stats<-pairwise.t.test(df$y, classvec, p.adjust="fdr")
}
if(stat.test=="limma"){
stat.df<-data.frame(Comp1=as.factor(PDObj@LimmaObj@Contrasts$comp1), Comp2=as.factor(PDObj@LimmaObj@Contrasts$comp2), p.value=ExtractLimmaObj(gene, PDObj@LimmaObj)$adj.P.Val, stringsAsFactors=FALSE)
stat.df<-stats.table(stat.df)
if(length(levels(classvec))==2){
if(is.null(reorder)==FALSE){
stat.df<-t(data.frame(stat.df[reorder,]))
colnames(stat.df)<-levels(classvec)[reorder]
}
else{
stat.df<-t(data.frame(stat.df))
}
rownames(stat.df)<-levels(classvec)[2]
}
else{
if(is.null(reorder)==FALSE){stat.df<-stat.df[,reorder]
}
rownames(stat.df)<-levels(classvec)[order(levels(classvec))][2:length(levels(classvec))]
}
#stat.df[is.na(stat.df)]<-1
stats<-list(p.value=stat.df)
array.ind<-as.data.frame(which(stats$p.value < p.value, arr.ind=T))
if(length(levels(classvec))==2){
if(is.null(reorder)==TRUE){
array.ind$row<-match(rownames(stat.df)[array.ind$row], levels(classvec))
}
}
else{array.ind$row<-match(rownames(stat.df)[array.ind$row], levels(classvec))}
rownames(array.ind)<-NULL
colnames(array.ind)<-c("start","end")
array.ind$y<-seq(from=maxh+0.5*(spread), by=spread/2, length.out=nrow(array.ind))
}
if(stat.test=="pairwiset"){
array.ind<-as.data.frame(which(stats$p.value < p.value, arr.ind=T))
rownames(array.ind)<-NULL
array.ind$row<-array.ind$row+1
colnames(array.ind)<-c("start","end")
array.ind$y<-seq(from=maxh+0.5*(spread), by=spread/2, length.out=nrow(array.ind))
}
if(gene.short){gene<-fData(PD@eset)[[gene.short.name]][rownames(fData(PD@eset)) %in% gene]}
#add sampleNames to df
if(length(sampleNames)!=0){
df$sampleNames<-sampleNames
g<-ggplot2::ggplot(df, ggplot2::aes(x = gp, y = y)) +
ggplot2::geom_boxplot(size=box.size, alpha=0.6, fill=box.fill.cols, colour=line.cols, width=box.width, outlier.size=NULL, width=box.width) +
ggplot2::geom_point(size=dot.size, shape=21, colour=line.cols[classvec], fill=dot.fill.cols[classvec], alpha=alpha, position = ggplot2::position_jitter(width = .1)) +
ggplot2::geom_text(data=df, ggplot2::aes(x = gp, y = y, label=sampleNames), size = 3, hjust=-1) +
ggplot2::labs(list(x = NULL, y = "Log2 Transformed Data", title=gene)) +
#ylab(expression(paste("Log", [2], " Transformed Data", sep="")))+
ggplot2::theme_bw() +
ggplot2::theme(axis.text=ggplot2::element_text(size=16),
axis.title.x=ggplot2::element_text(size=20, vjust=1),
axis.text.x = ggplot2::element_text(angle = 45, hjust=1),
axis.title.y=ggplot2::element_text(size=16, vjust=0.5), plot.title = ggplot2::element_text(vjust = 0, size=20),
axis.line = ggplot2::element_line(colour = "black"),
#text=ggplot2::element_text(family="Myriad Pro"),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
panel.background = ggplot2::element_blank())
if(nrow(array.ind)==0){
if(print==TRUE){print(g)}else{return(g)}
}
else
{if(error.bars==TRUE){
for(i in 1:nrow(array.ind)){
g<-g+ggplot2::geom_segment(ggplot2::aes_string(x = array.ind$start[i], y = array.ind$y[i], xend = array.ind$end[i], yend=array.ind$y[i]), lwd=0.5,arrow = arrow(angle=90, ends="both", length = grid::unit(0.1, "cm")))
}
}
}
if(annotate==TRUE){
footie1<-ifelse(stat.test=="limma", paste("Mod. Bayesian T statistic corrected using ", PD@LimmaObj@Inputs$p.adjust, sep=""), "Pairwise T Test, FDR-corrected")
Footnote.txt<-paste("Horizontal bars indicate p <0.05 using ", footie1, sep="")
makeFootnote(Footnote.txt, color = "black")}
if(print==TRUE){print(g)}else{return(g)}
}
else
{
g<-ggplot2::ggplot(df, ggplot2::aes(x = gp, y = y)) +
ggplot2::geom_boxplot(size=box.size, alpha=0.6, fill=box.fill.cols, width=box.width, colour=line.cols, outlier.size=NULL) +
ggplot2::geom_point(size=dot.size, shape=21, colour=line.cols[classvec], fill=dot.fill.cols[classvec], alpha=alpha, position = ggplot2::position_jitter(width = .1)) +
ggplot2::labs(list(x = NULL, y = "Log2 Transformed Data", title=gene)) +
#ylab(expression(paste("Log", [2], " Transformed Data", sep="")))+
ggplot2::theme_bw() +
ggplot2::theme(axis.text=ggplot2::element_text(size=16),
axis.title.x=ggplot2::element_text(size=20, vjust=1),
axis.text.x = ggplot2::element_text(angle = 45, hjust=1),
axis.title.y=ggplot2::element_text(size=16, vjust=0.5), plot.title = ggplot2::element_text(vjust = 0, size=20),
axis.line = ggplot2::element_line(colour = "black"),
#text=ggplot2::element_text(family="Myriad Pro"),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
panel.background = ggplot2::element_blank())
if(nrow(array.ind)==0){
}
else
{if(error.bars==TRUE){
for(i in 1:nrow(array.ind)){
g<-g+ggplot2::geom_segment(ggplot2::aes_string(x = array.ind$start[i], y = array.ind$y[i], xend = array.ind$end[i], yend=array.ind$y[i]), lwd=0.5,arrow = ggplot2::arrow(angle=90, ends="both", length = grid::unit(0.1, "cm")))
}
}
}
if(annotate==TRUE){
footie1<-ifelse(stat.test=="limma", paste("Mod. Bayesian T statistic corrected using ", PDObj@LimmaObj@Inputs$p.adjust, sep=""), "Pairwise T Test, FDR-corrected")
Footnote.txt<-paste("Horizontal bars indicate p <0.05 using ", footie1, sep="")
makeFootnote(Footnote.txt, color = "black")
}
if(print==TRUE){print(g)}else{return(g)}
}
}
# write a simple function to add footnote
makeFootnote <- function(footnoteText =
format(Sys.time(), "%d %b %Y"),
size = .7, color = grey(.5))
{
grid::pushViewport(grid::viewport())
grid::grid.text(label = footnoteText ,
x = grid::unit(1,"npc") - grid::unit(2, "mm"),
y = grid::unit(2, "mm"),
just = c("right", "bottom"),
gp = grid::gpar(cex = size, col = color))
grid::popViewport()
}
SigLevel<-function(vector){
return(sapply(vector, function(x) ifelse(x>0.001 && x<0.05, "*", ifelse(x<0.001, "**", "NS"))))
}
FindContrasts<-function(object){
return(print(names(object[[3]])))
}
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
#library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid::grid.newpage()
grid::pushViewport(viewport(layout = grid::grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
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