R/MAboxplot5.R

In scfurl/probedeeper: Making the Analysis of Microarray Data More Efficient

# #####MABoxPlot6#####
# #MABoxPlot4
# #Bug fix to not plot NA statistical values as significant
# #New feature to allow input of statistical cutoff
# #150809 Fixed bug that prevented boxplot of only two groups.
# #MABoxPlot5
# #New feature to plot sample names on plot
# #MABoxPlot6
# #Allow for further manipulation of color
#
# # #Dummy Data
# # gene<-AURKA.PROBES[2]
# # sampleNames<-paste(finaleset$sampleNames, finaleset$Batch, sep="-")
# # array<-data
# # limma.obj<-limma.out
# # classvec<-classvec
# # stat.test="pairwiset"
# # p.value=0.05
#
# MAboxplot5<-function(gene, array, limma.obj, classvec, cols=brewer.pal(length(levels(classvec)), "Set3"),
#                      reorder=NULL, stat.test="limma", annotate=TRUE, p.value=0.05, sampleNames=NULL){
#   library(grid)
#   classvec<-as.factor(classvec)
#   if(is.null(reorder)==FALSE){classvec<-factor(classvec,levels(classvec)[reorder])
#                               cols<-cols[reorder]
#   }
#   obj<-array[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 <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))
#   if(stat.test=="pairwiset"){
#     stats<-pairwise.t.test(df$y, classvec, p.adjust="fdr")
#   }
#   if(stat.test=="limma"){
#     stat.df<-data.frame(Comp1=as.factor(limma.obj[[5]]$Comp1), Comp2=as.factor(limma.obj[[5]]$Comp2), p.value=ExtractLIMMA(limma.obj, gene)$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))
#   }
#   #add sampleNames to df
#   if(length(sampleNames)!=0){
#     df$sampleNames<-sampleNames
#     g<-ggplot(df, aes(x = gp, y = y)) +
#       geom_boxplot(size=0.5, alpha=0.6, fill=cols,outlier.size=NULL, width=0.6) +
#       geom_point(size=4, colour=cols[classvec], alpha=0.8,position = position_jitter(width = .1)) +
#       geom_text(data=df, aes(x = gp, y = y, label=sampleNames), size = 3, hjust=-1) +
#       labs(list(x = "Treatment Group", y = "Log2 Transformed Data", title=gene)) +
#       #ylab(expression(paste("Log", [2], " Transformed Data", sep="")))+
#       theme_bw() +
#       theme(axis.text=element_text(size=16),
#           axis.title.x=element_text(size=20, vjust=1),
#           axis.text.x = element_text(angle = 45, hjust=1),
#           axis.title.y=element_text(size=16, vjust=0.5), plot.title = element_text(vjust = 0, size=20),
#           axis.line = element_line(colour = "black"),
#           #text=element_text(family="Myriad Pro"),
#           panel.grid.major = element_blank(),
#           panel.grid.minor = element_blank(),
#           panel.border = element_blank(),
#           panel.background = element_blank())
#     if(nrow(array.ind)==0){
#       print(g)
#     }
#     else
#     {
#       for(i in 1:nrow(array.ind)){
#         g<-g+geom_segment(aes_string(x = array.ind$start[i], y = array.ind$y[i], xend = array.ind$end[i], yend=array.ind$y[i]), lwd=0.5,arrow = arrow(angle=90, ends="both", length = unit(0.1, "cm")))
#       }
#     }
#     print(g)
#     footie1<-ifelse(stat.test=="limma", paste("Mod. Bayesian T statistic corrected using ", limma.obj[[6]]$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")
#   }
#   else
#   {
#       g<-ggplot(df, aes(x = gp, y = y)) +
#         geom_boxplot(size=0.5, alpha=0.6, fill=cols,outlier.size=NULL, width=0.6) +
#         geom_point(size=4, colour=cols[classvec], alpha=0.8,position = position_jitter(width = .1)) +
#         labs(list(x = "Treatment Group", y = "Log2 Transformed Data", title=gene)) +
#         #ylab(expression(paste("Log", [2], " Transformed Data", sep="")))+
#         theme_bw() +
#         theme(axis.text=element_text(size=16),
#               axis.title.x=element_text(size=20, vjust=1),
#               axis.text.x = element_text(angle = 45, hjust=1),
#               axis.title.y=element_text(size=16, vjust=0.5), plot.title = element_text(vjust = 0, size=20),
#               axis.line = element_line(colour = "black"),
#               #text=element_text(family="Myriad Pro"),
#               panel.grid.major = element_blank(),
#               panel.grid.minor = element_blank(),
#               panel.border = element_blank(),
#               panel.background = element_blank())
#       if(nrow(array.ind)==0){
#         print(g)
#       }
#       else
#       {
#         for(i in 1:nrow(array.ind)){
#           g<-g+geom_segment(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 = unit(0.1, "cm")))
#         }
#       }
#       print(g)
#       footie1<-ifelse(stat.test=="limma", paste("Mod. Bayesian T statistic corrected using ", limma.obj[[6]]$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")
#     }
# }
#
# # write a simple function to add footnote
# makeFootnote <- function(footnoteText =
#                            format(Sys.time(), "%d %b %Y"),
#                          size = .7, color = grey(.5))
# {
#   require(grid)
#   pushViewport(viewport())
#   grid.text(label = footnoteText ,
#             x = unit(1,"npc") - unit(2, "mm"),
#             y = unit(2, "mm"),
#             just = c("right", "bottom"),
#             gp = gpar(cex = size, col = color))
#   popViewport()
# }