R/plot-divergence.r
In jhsiao999/Humanzee: Tools for statistical analysis of genomics data
# draw_legend = function(color, breaks, legend, ...){
# height = min(unit(1, "npc"), unit(100, "bigpts"))
# legend_pos = (legend - min(breaks)) / (max(breaks) - min(breaks))
# legend_pos = height * legend_pos + (unit(1, "npc") - height)
# breaks = (breaks - min(breaks)) / (max(breaks) - min(breaks))
# breaks = height * breaks + (unit(1, "npc") - height)
# h = breaks[-1] - breaks[-length(breaks)]
# rect = rectGrob(x = 0, y = breaks[-length(breaks)], width = unit(10, "bigpts"), height = h, hjust = 0, vjust = 0, gp = gpar(fill = color, col = "#FFFFFF00"))
# text = textGrob(names(legend), x = unit(14, "bigpts"), y = legend_pos, hjust = 0, gp = gpar(...))
# res = grobTree(rect, text)
# return(res)
# }
# generate_breaks = function(x, n, center = F){
# if(center){
# m = max(abs(c(min(x, na.rm = T), max(x, na.rm = T))))
# res = seq(-m, m, length.out = n + 1)
# }
# else{
# res = seq(min(x, na.rm = T), max(x, na.rm = T), length.out = n + 1)
# }
# return(res)
# }
# ##############
# plotDivergenceDensity <- function( dens, main, plot_legend = FALSE,
# xlims_subset = NULL, ylims_subset = NULL) {
# if (is.null(xlims_subset)) {
# xlims = range(sapply(dens,function(xx) xx$x))
# } else {
# xlims = xlims_subset
# }
# if (is.null(ylims_subset)) {
# ylims = range(sapply(dens,function(xx) xx$y))
# } else {
# ylims = ylims_subset
# }
# plot( dens[[1]], col = "grey20", ylim = ylims, xlim = xlims,
# main = "", xlab="",lwd = 2, axes = F )
# title( xlab = "Divergence FC", cex.lab = .6)
# title(main=main, cex.main = .7)
# axis(1, tck = -.04, cex.axis = .4)
# axis(2, tck = -.03, cex.axis = .4)
# polygon(dens[[1]],col=alpha("grey20",.6), lwd = 2 )
# lines( dens[[2]], col = "grey70", lty = 2, lwd = 2 )
# polygon( dens[[2]],col=alpha("grey70", .6), lty = 1, lwd = 2 )
# lines( dens[[3]], col = "black", lty = 2, lwd = 2 )
# if (plot_legend==TRUE) {
# legend("topright", col=c("red","blue","grey20"), lty=1,
# legend = c("RNA divergence", "Ribo divergence", "Protein divergence"))
# }
# }
# plotDivergenceDensity_v2 <- function( dens, main, plot_legend = FALSE,
# xlims_subset = NULL, ylims_subset = NULL,
# main.adj = 0, main.line = 0) {
# if (is.null(xlims_subset)) {
# xlims = range(sapply(dens,function(xx) xx$x))
# } else {
# xlims = xlims_subset
# }
# if (is.null(ylims_subset)) {
# ylims = range(sapply(dens,function(xx) xx$y))
# } else {
# ylims = ylims_subset
# }
# require(broman)
# crayon <- brocolors("crayons")
# plot( dens[[1]], col = crayon["Scarlet"], ylim = ylims, xlim = xlims,
# main = "", xlab="",lwd = 2, axes = F, ylab="" )
# title( xlab = "Divergence effect size", cex.lab = .6)
# title( ylab = "Density", cex.lab = .6)
# title(main=main, cex.main = .7, adj = main.adj, line = main.line)
# axis(1, tck = -.04, cex.axis = .6, at = c(0, 1.5, 3), label = c(0, 1.5, 3))
# axis(2, tck = -.03, cex.axis = .6, at = c(0, 1, 2, 3), label = c(0, 1, 2, 3))
# polygon( dens[[2]],col = alpha(crayon["Laser Lemon"], .6), lty = 1, lwd = 1,
# border = "dodgerblue" )
# lines( dens[[3]], col = crayon["Pine Green"], lty = 1, lwd = 2 )
# # polygon(dens[[2]],col= alpha(crayon["Sea Green"],.6), lwd = 1 )
# # lines( dens[[3]], col = crayon["Lemon Yellow"], lty = 2, lwd = 1 )
# if (plot_legend==TRUE) {
# legend("topright", col=c("red","blue","grey20"), lty=1,
# legend = c("RNA divergence", "Ribo divergence", "Protein divergence"))
# }
# }
# ##############
# require(broman)
# plotDivergenceRatioDensity <- function(dens,
# bk_col, fn_lcol = crayon["Green"],
# col.main ) {
# crayon <- brocolors("crayon")
# par( mfrow = c(1, 1), mar = c(2, 1, 1, 2), mgp = c(.9, .2, 0) )
# plot(dens[[1]], lwd = 1, col = "white", xlab ="", ylab="",
# main = "", axes = F,sub="",ylim=c(0,.8),
# xlim = rev(c(-6,8)) )
# title(xlab="Divergence ratio", cex.lab = .5)
# axis(1, at= rev(seq(-6,8,by=2) ), labels = rev( seq(-6,8,by=2)),
# tck = -.03, cex.axis = .3, lwd = .6, las = 3)
# axis(4, at=seq(0, 1, 0.2), labels=seq(0,1,0.2),
# tck = -.03, cex.axis = .3, lwd = .6)
# mtext("Proportion", side = 4, cex = .5, line = .5)
# polygon(dens[[1]], col = bk_col, lwd = 1, border = NA )
# lines(dens[[2]], col = fn_lcol, lty = 1, lwd = 1.2, xlim=c(-8,8))
# # legend("topleft",pch=21,col="black",pt.bg=c(col.main,"white"),
# # legend=c("log2 Ribo/RNA divergence ratio","log2 Protein/RNA divergence ratio"),
# # box.col="white")
# }
# #############
# plotLegend.sigpro <- function(col.riborna,bg.riborna,
# col.rnapro,bg.rnapro,col.both,numsigs) {
# plot(0,pch="",xlab="",ylab="",axes=F,xlim=c(0,10),ylim=c(0,10))
# points(1,5,pch=21,bg=bg.rnapro[1],col=col.rnapro[1],cex=2)
# points(1,4.3,pch=21,bg=bg.rnapro[2],col=col.rnapro[2],cex=2)
# points(1,3.6,pch=16,col=col.both[1],cex=2)
# points(1,2.9,pch=16,col=col.both[2],cex=2)
# text(2,5,label=paste("mRNA > Protein divergence",numsigs[1],"genes"),adj=0,cex=1)
# text(2,4.3,label=paste("Protein > mRNA divergence",numsigs[2],"genes"),adj=0,cex=1)
# text(2,3.6,label=paste("mRNA > Ribo and mRNA > Protein",numsigs[3],"genes"),adj=0,cex=1)
# text(2,2.9,label=paste("Ribo > mRNA & Protein > mRNA",numsigs[4],"genes"),adj=0,cex=1)
# }
# plotLegend.sigribo <- function(col.riborna,bg.riborna,
# col.rnapro,bg.rnapro,col.both,numsigs) {
# plot(0,pch="",xlab="",ylab="",axes=F,xlim=c(0,10),ylim=c(0,10))
# points(1,5,pch=21,bg=bg.riborna[1],col=col.riborna[1],cex=2)
# points(1,4.3,pch=21,bg=bg.riborna[2],col=col.riborna[2],cex=2)
# points(1,3.6,pch=16,col=col.both[1],cex=2)
# points(1,2.9,pch=16,col=col.both[2],cex=2)
# text(2,5,label=paste("mRNA > Ribo divergence",numsigs[1],"genes"),adj=0,cex=1)
# text(2,4.3,label=paste("Ribo > mRNA divergence",numsigs[2],"genes"),adj=0,cex=1)
# text(2,3.6,label=paste("mRNA > Ribo and mRNA > Protein",numsigs[3],"genes"),adj=0,cex=1)
# text(2,2.9,label=paste("Ribo > mRNA & Protein > mRNA",numsigs[4],"genes"),adj=0,cex=1)
# }
# ##############
# require(broman)
# plotDivergenceScatter <- function(xy,
# ind_points1 = NULL, ind_points2 = NULL,
# xlab, ylab,
# col_bkpoints = "black",
# cex_bkpoints = .4,
# lwd_bkpoints = .5,
# pch_bkpoints = 1,
# col_points1 = brocolors("crayons")["Red Orange"],
# col_points2 = brocolors("crayons")["Red Orange"],
# col_diag = "grey",
# cex_points1 = .13, cex_points2 = .13,
# pch_points1 = 20, pch_points2 = 20,
# col_hline = brocolors("crayons")["Pine Green"],
# col_vline = brocolors("crayons")["Pine Green"],
# lwd_hline = .5,
# lwd_vline = .5,
# cex.axis = .3, cex.lab = .3,
# xlim = c(-6, 10), ylim = c(-6, 10) ) {
# par( mar = c(2.2, 2, 1, .5), mgp = c(.7, .1, 0) )
# plot( xy, pch = "",
# axes = F, ylim = ylim, xlim = xlim, xlab = "", ylab = "" )
# points(xy, col = col_bkpoints, pch = pch_bkpoints, cex = cex_bkpoints, lwd = .5 )
# if ( !is.null(ind_points2) ) {
# points(xy[ind_points2, ], pch = pch_points2, col = col_points2,
# cex = cex_points2)
# }
# points(xy[ind_points1, ], pch = pch_points1, col = col_points1,
# cex = cex_points1)
# if (xlab == "log2 (human/chimp) \n Ribosome occupancy") {
# title( xlab = xlab, cex.lab = cex.lab, line = 1.3)
# } else {
# title( xlab = xlab, cex.lab = cex.lab)
# }
# title( ylab = ylab, cex.lab = cex.lab)
# axis(1, tck = -.06, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# axis(2, tck = -.03, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# abline(h = 0, lty = 1, col = col_hline, lwd = lwd_hline)
# abline(v = 0, lty = 1, col = col_vline, lwd = lwd_vline)
# abline(a = 0, b = 1, lty = 1, col = col_diag, lwd = .5)
# }
# ##############
# require(broman)
# plotDivergenceScatter_goRiboRNA <- function(xy,
# ind_points1 = NULL, ind_points2 = NULL,
# ind_points3 = NULL,
# xlab, ylab,
# col_bkpoints = "black",
# cex_bkpoints = .4,
# lwd_bkpoints = .5,
# pch_bkpoints = 1,
# col_points1 = brocolors("crayons")["Red Orange"],
# col_points2 = brocolors("crayons")["Red Orange"],
# col_points3 = brocolors("crayons")["Black"],
# col_diag = "grey",
# cex_points1 = .13, cex_points2 = .13,
# cex_points3 = .13,
# pch_points1 = 20, pch_points2 = 20,
# pch_points3 = 20,
# col_hline = brocolors("crayons")["Pine Green"],
# col_vline = brocolors("crayons")["Pine Green"],
# lwd_hline = .5,
# lwd_vline = .5,
# cex.axis = .3, cex.lab = .3,
# xlim = c(-6, 10), ylim = c(-6, 10) ) {
# par( mar = c(2.2, 2, 1, .5), mgp = c(.7, .1, 0) )
# plot( xy, pch = "",
# axes = F, ylim = ylim, xlim = xlim, xlab = "", ylab = "" )
# points(xy, col = col_bkpoints, pch = pch_bkpoints, cex = cex_bkpoints, lwd = .5 )
# if ( !is.null(ind_points2) ) {
# points(xy[ind_points2, ], pch = pch_points2, col = col_points2,
# cex = cex_points2)
# }
# points(xy[ind_points1, ], pch = pch_points1, col = col_points1,
# cex = cex_points1)
# points(xy[ind_points3, ], pch = pch_points3, col = col_points3,
# cex = cex_points3)
# if (xlab == "log2 (human/chimp) \n Ribosome occupancy") {
# title( xlab = xlab, cex.lab = cex.lab, line = 1.3)
# } else {
# title( xlab = xlab, cex.lab = cex.lab)
# }
# title( ylab = ylab, cex.lab = cex.lab)
# axis(1, tck = -.06, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# axis(2, tck = -.03, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# abline(h = 0, lty = 1, col = col_hline, lwd = lwd_hline)
# abline(v = 0, lty = 1, col = col_vline, lwd = lwd_vline)
# abline(a = 0, b = 1, lty = 1, col = col_diag, lwd = .5)
# }
# # plotDivergenceScatter.riborna.sigpro <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.riborna,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.riborna,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Ribo log2 RPKM (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.riborna[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)>abs(xy.rnapro$pro)),],
# # pch=pch[1],bg=bg.rnapro[1],col=col.rnapro[1],cex=cex[1])
# # points(xy.riborna[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)<abs(xy.rnapro$pro)),],
# # pch=pch[2],bg=bg.rnapro[2],col=col.rnapro[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.riborna[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.riborna[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# # }
# #
# #
# # plotDivergenceScatter.rnapro.sigpro <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.rnapro,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.rnapro,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Protein log2 FC (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.rnapro[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)>abs(xy.rnapro$pro)),],
# # pch=pch[1],bg=bg.rnapro[1],col=col.rnapro[1],cex=cex[1])
# # points(xy.rnapro[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)<abs(xy.rnapro$pro)),],
# # pch=pch[2],bg=bg.rnapro[2],col=col.rnapro[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.rnapro[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.rnapro[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# # }
# #
# #
# ###############
# # plotDivergenceScatter.riborna.sigribo <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.riborna,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.riborna,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Ribo log2 RPKM (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.riborna[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)),],
# # pch=pch[1],bg=bg.riborna[1],col=col.riborna[1],cex=cex[1])
# # points(xy.riborna[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)),],
# # pch=pch[2],bg=bg.riborna[2],col=col.riborna[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.riborna[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.riborna[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# # }
# #
# #
# # plotDivergenceScatter.rnapro.sigribo <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.rnapro,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.rnapro,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Protein log2 FC (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.rnapro[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)),],
# # pch=pch[1],bg=bg.riborna[1],col=col.riborna[1],cex=cex[1])
# # points(xy.rnapro[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)),],
# # pch=pch[2],bg=bg.riborna[2],col=col.riborna[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.rnapro[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.rnapro[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# goHeatmapData <- function(golist){
# golist_test <- list(golist[[3]], golist[[4]])
# golist <- golist_test
# golist <- golist_bp
# nlist <- length(golist)
# goIDlist <- lapply( 1:nlist, function(i) {
# golist[[i]][ , 1]
# })
# names(goIDlist) <- names(golist)
# goTermlist <- lapply( 1:nlist, function(i) {
# golist[[i]]$Term
# })
# goset <- unique( unlist(goIDlist) )
# gosetTerm <- unique( unlist(goTermlist) )
# heatmap_allgos <- data.frame(ID = goset)
# heatmap_allgos$Term <- gosetTerm
# pvals <- merge(golist[[1]][, c(1,2,7)], golist[[2]][, c(1,2,7)],
# all = TRUE, by = "GOBPID")
# pvals
# # pvals <- lapply(1:nlist, function(i) {
# # foo <- rep(1-10^(-6), length(gosetTerm))
# # foo[ which(goset %in% golist[[i]][ , 1]) ] <- golist[[i]]$Pvalue[ which( golist[[i]][ , 1] %in% goset )]
# # return(foo)
# # })
# # pvals <- do.call(cbind, pvals)
# pvals_bp <- pvals
# pvals_test <- pvals
# colnames(pvals) <- names(golist)
# heatmap_allgos <- cbind(heatmap_allgos, pvals)
# colnames(heatmap_allgos)[1] <- colnames(golist[[1]])[1]
# ind <- which( is.na(heatmap_allgos), arr.ind = TRUE)
# heatmap_allgos[ ind ] <- 1
# rownames(heatmap_allgos) <- heatmap_allgos[ , 1]
# iisig <- pvals > .01
# iisig_gos <- which(rowSums(iisig)!=nlist)
# heatmap_allgos <- heatmap_allgos[iisig_gos, ]
# return(heatmap_allgos)
# }
# #' @example hmap <- plotHeatmap(heatmap_allgos,
# #' labCol = c("Ribo > mRNA", "RNA > Ribo",
# #' "Ribo > Protein", "Protein > Ribo"))
# plotHeatmap <- function(heatmapData, mar = c(0, 0, 0, 0),
# oma = c(0, 0, 0, 0), keysize = 1, cexRow = 1.3, cexCol = 1.3,
# labCol = NULL, key = TRUE, margin = c(5,5),
# dendrogram = "row", breaks = NULL) {
# #heatmapData <- heatmap_allgos_bp
# # mar = c(8, 1, 0, 20)
# # oma = c(2, 5, 0, 40)
# require(broman)
# crayon <- brocolors("crayon")
# heatcols <- colorRampPalette(c("white", crayon["Banana Mania"],
# crayon["Laser Lemon"],
# crayon["Burnt Orange"], crayon["Orange Red"],
# crayon["Plum"]))
# par( mfrow = c(1,1), mar = mar, oma = oma )
# require(gplots)
# mat <- heatmapData[ , -c(1:2)]
# rownames(mat) <- heatmapData$Term
# hmap <- heatmap.2(as.matrix(-log10(mat)), main = "",
# labCol = labCol, cexCol = cexCol,
# cexRow = cexRow,
# breaks = breaks,
# sepcol = "grey20",
# sepwidth=c(0.01, 0.01), lwd = .2,
# colsep = 1:ncol(mat), Colv = FALSE, key = key,
# rowsep = 1:nrow(mat), dendrogram = dendrogram, margin = margin,
# srtCol = 0, adjCol = c(.5,NA), offsetCol = 0, offsetRow = .1,
# keysize = keysize, lwid = c(1.5, 7), lhei = c(.5, 5),
# col = heatcols, trace ="none", key.title = "-log p-value" )
# return(hmap)
# }
# plotBoxes2 <- function(datavec1, datavec2, main, cols,
# labels, ylim = NULL, cex.main = .4,
# main.line = .5) {
# if (!is.null(ylim)) {
# boxplot(datavec1, datavec2,
# outline=F, xlab="",lwd = .4,
# axes = F, col= cols, border = "black", notch = TRUE, ylim = ylim,
# varwidth = TRUE)
# } else {
# boxplot(datavec1, datavec2,
# outline=F, xlab="",lwd = .4,
# axes = F, col= cols, border = "black", notch = TRUE, varwidth = TRUE)
# }
# title(main = main, cex.main = cex.main, line = main.line )
# # axis(1, tck = -.03, cex.axis = .6, lwd = .6,
# # at = 1:2, labels = labels, col= "white")
# axis(2, tck = -.03, cex.axis = .4, lwd = .4)
# }
# # }
jhsiao999/Humanzee documentation built on May 19, 2019, 9:28 a.m.
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# draw_legend = function(color, breaks, legend, ...){
# height = min(unit(1, "npc"), unit(100, "bigpts"))
# legend_pos = (legend - min(breaks)) / (max(breaks) - min(breaks))
# legend_pos = height * legend_pos + (unit(1, "npc") - height)
# breaks = (breaks - min(breaks)) / (max(breaks) - min(breaks))
# breaks = height * breaks + (unit(1, "npc") - height)
# h = breaks[-1] - breaks[-length(breaks)]
# rect = rectGrob(x = 0, y = breaks[-length(breaks)], width = unit(10, "bigpts"), height = h, hjust = 0, vjust = 0, gp = gpar(fill = color, col = "#FFFFFF00"))
# text = textGrob(names(legend), x = unit(14, "bigpts"), y = legend_pos, hjust = 0, gp = gpar(...))
# res = grobTree(rect, text)
# return(res)
# }
# generate_breaks = function(x, n, center = F){
# if(center){
# m = max(abs(c(min(x, na.rm = T), max(x, na.rm = T))))
# res = seq(-m, m, length.out = n + 1)
# }
# else{
# res = seq(min(x, na.rm = T), max(x, na.rm = T), length.out = n + 1)
# }
# return(res)
# }
# ##############
# plotDivergenceDensity <- function( dens, main, plot_legend = FALSE,
# xlims_subset = NULL, ylims_subset = NULL) {
# if (is.null(xlims_subset)) {
# xlims = range(sapply(dens,function(xx) xx$x))
# } else {
# xlims = xlims_subset
# }
# if (is.null(ylims_subset)) {
# ylims = range(sapply(dens,function(xx) xx$y))
# } else {
# ylims = ylims_subset
# }
# plot( dens[[1]], col = "grey20", ylim = ylims, xlim = xlims,
# main = "", xlab="",lwd = 2, axes = F )
# title( xlab = "Divergence FC", cex.lab = .6)
# title(main=main, cex.main = .7)
# axis(1, tck = -.04, cex.axis = .4)
# axis(2, tck = -.03, cex.axis = .4)
# polygon(dens[[1]],col=alpha("grey20",.6), lwd = 2 )
# lines( dens[[2]], col = "grey70", lty = 2, lwd = 2 )
# polygon( dens[[2]],col=alpha("grey70", .6), lty = 1, lwd = 2 )
# lines( dens[[3]], col = "black", lty = 2, lwd = 2 )
# if (plot_legend==TRUE) {
# legend("topright", col=c("red","blue","grey20"), lty=1,
# legend = c("RNA divergence", "Ribo divergence", "Protein divergence"))
# }
# }
# plotDivergenceDensity_v2 <- function( dens, main, plot_legend = FALSE,
# xlims_subset = NULL, ylims_subset = NULL,
# main.adj = 0, main.line = 0) {
# if (is.null(xlims_subset)) {
# xlims = range(sapply(dens,function(xx) xx$x))
# } else {
# xlims = xlims_subset
# }
# if (is.null(ylims_subset)) {
# ylims = range(sapply(dens,function(xx) xx$y))
# } else {
# ylims = ylims_subset
# }
# require(broman)
# crayon <- brocolors("crayons")
# plot( dens[[1]], col = crayon["Scarlet"], ylim = ylims, xlim = xlims,
# main = "", xlab="",lwd = 2, axes = F, ylab="" )
# title( xlab = "Divergence effect size", cex.lab = .6)
# title( ylab = "Density", cex.lab = .6)
# title(main=main, cex.main = .7, adj = main.adj, line = main.line)
# axis(1, tck = -.04, cex.axis = .6, at = c(0, 1.5, 3), label = c(0, 1.5, 3))
# axis(2, tck = -.03, cex.axis = .6, at = c(0, 1, 2, 3), label = c(0, 1, 2, 3))
# polygon( dens[[2]],col = alpha(crayon["Laser Lemon"], .6), lty = 1, lwd = 1,
# border = "dodgerblue" )
# lines( dens[[3]], col = crayon["Pine Green"], lty = 1, lwd = 2 )
# # polygon(dens[[2]],col= alpha(crayon["Sea Green"],.6), lwd = 1 )
# # lines( dens[[3]], col = crayon["Lemon Yellow"], lty = 2, lwd = 1 )
# if (plot_legend==TRUE) {
# legend("topright", col=c("red","blue","grey20"), lty=1,
# legend = c("RNA divergence", "Ribo divergence", "Protein divergence"))
# }
# }
# ##############
# require(broman)
# plotDivergenceRatioDensity <- function(dens,
# bk_col, fn_lcol = crayon["Green"],
# col.main ) {
# crayon <- brocolors("crayon")
# par( mfrow = c(1, 1), mar = c(2, 1, 1, 2), mgp = c(.9, .2, 0) )
# plot(dens[[1]], lwd = 1, col = "white", xlab ="", ylab="",
# main = "", axes = F,sub="",ylim=c(0,.8),
# xlim = rev(c(-6,8)) )
# title(xlab="Divergence ratio", cex.lab = .5)
# axis(1, at= rev(seq(-6,8,by=2) ), labels = rev( seq(-6,8,by=2)),
# tck = -.03, cex.axis = .3, lwd = .6, las = 3)
# axis(4, at=seq(0, 1, 0.2), labels=seq(0,1,0.2),
# tck = -.03, cex.axis = .3, lwd = .6)
# mtext("Proportion", side = 4, cex = .5, line = .5)
# polygon(dens[[1]], col = bk_col, lwd = 1, border = NA )
# lines(dens[[2]], col = fn_lcol, lty = 1, lwd = 1.2, xlim=c(-8,8))
# # legend("topleft",pch=21,col="black",pt.bg=c(col.main,"white"),
# # legend=c("log2 Ribo/RNA divergence ratio","log2 Protein/RNA divergence ratio"),
# # box.col="white")
# }
# #############
# plotLegend.sigpro <- function(col.riborna,bg.riborna,
# col.rnapro,bg.rnapro,col.both,numsigs) {
# plot(0,pch="",xlab="",ylab="",axes=F,xlim=c(0,10),ylim=c(0,10))
# points(1,5,pch=21,bg=bg.rnapro[1],col=col.rnapro[1],cex=2)
# points(1,4.3,pch=21,bg=bg.rnapro[2],col=col.rnapro[2],cex=2)
# points(1,3.6,pch=16,col=col.both[1],cex=2)
# points(1,2.9,pch=16,col=col.both[2],cex=2)
# text(2,5,label=paste("mRNA > Protein divergence",numsigs[1],"genes"),adj=0,cex=1)
# text(2,4.3,label=paste("Protein > mRNA divergence",numsigs[2],"genes"),adj=0,cex=1)
# text(2,3.6,label=paste("mRNA > Ribo and mRNA > Protein",numsigs[3],"genes"),adj=0,cex=1)
# text(2,2.9,label=paste("Ribo > mRNA & Protein > mRNA",numsigs[4],"genes"),adj=0,cex=1)
# }
# plotLegend.sigribo <- function(col.riborna,bg.riborna,
# col.rnapro,bg.rnapro,col.both,numsigs) {
# plot(0,pch="",xlab="",ylab="",axes=F,xlim=c(0,10),ylim=c(0,10))
# points(1,5,pch=21,bg=bg.riborna[1],col=col.riborna[1],cex=2)
# points(1,4.3,pch=21,bg=bg.riborna[2],col=col.riborna[2],cex=2)
# points(1,3.6,pch=16,col=col.both[1],cex=2)
# points(1,2.9,pch=16,col=col.both[2],cex=2)
# text(2,5,label=paste("mRNA > Ribo divergence",numsigs[1],"genes"),adj=0,cex=1)
# text(2,4.3,label=paste("Ribo > mRNA divergence",numsigs[2],"genes"),adj=0,cex=1)
# text(2,3.6,label=paste("mRNA > Ribo and mRNA > Protein",numsigs[3],"genes"),adj=0,cex=1)
# text(2,2.9,label=paste("Ribo > mRNA & Protein > mRNA",numsigs[4],"genes"),adj=0,cex=1)
# }
# ##############
# require(broman)
# plotDivergenceScatter <- function(xy,
# ind_points1 = NULL, ind_points2 = NULL,
# xlab, ylab,
# col_bkpoints = "black",
# cex_bkpoints = .4,
# lwd_bkpoints = .5,
# pch_bkpoints = 1,
# col_points1 = brocolors("crayons")["Red Orange"],
# col_points2 = brocolors("crayons")["Red Orange"],
# col_diag = "grey",
# cex_points1 = .13, cex_points2 = .13,
# pch_points1 = 20, pch_points2 = 20,
# col_hline = brocolors("crayons")["Pine Green"],
# col_vline = brocolors("crayons")["Pine Green"],
# lwd_hline = .5,
# lwd_vline = .5,
# cex.axis = .3, cex.lab = .3,
# xlim = c(-6, 10), ylim = c(-6, 10) ) {
# par( mar = c(2.2, 2, 1, .5), mgp = c(.7, .1, 0) )
# plot( xy, pch = "",
# axes = F, ylim = ylim, xlim = xlim, xlab = "", ylab = "" )
# points(xy, col = col_bkpoints, pch = pch_bkpoints, cex = cex_bkpoints, lwd = .5 )
# if ( !is.null(ind_points2) ) {
# points(xy[ind_points2, ], pch = pch_points2, col = col_points2,
# cex = cex_points2)
# }
# points(xy[ind_points1, ], pch = pch_points1, col = col_points1,
# cex = cex_points1)
# if (xlab == "log2 (human/chimp) \n Ribosome occupancy") {
# title( xlab = xlab, cex.lab = cex.lab, line = 1.3)
# } else {
# title( xlab = xlab, cex.lab = cex.lab)
# }
# title( ylab = ylab, cex.lab = cex.lab)
# axis(1, tck = -.06, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# axis(2, tck = -.03, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# abline(h = 0, lty = 1, col = col_hline, lwd = lwd_hline)
# abline(v = 0, lty = 1, col = col_vline, lwd = lwd_vline)
# abline(a = 0, b = 1, lty = 1, col = col_diag, lwd = .5)
# }
# ##############
# require(broman)
# plotDivergenceScatter_goRiboRNA <- function(xy,
# ind_points1 = NULL, ind_points2 = NULL,
# ind_points3 = NULL,
# xlab, ylab,
# col_bkpoints = "black",
# cex_bkpoints = .4,
# lwd_bkpoints = .5,
# pch_bkpoints = 1,
# col_points1 = brocolors("crayons")["Red Orange"],
# col_points2 = brocolors("crayons")["Red Orange"],
# col_points3 = brocolors("crayons")["Black"],
# col_diag = "grey",
# cex_points1 = .13, cex_points2 = .13,
# cex_points3 = .13,
# pch_points1 = 20, pch_points2 = 20,
# pch_points3 = 20,
# col_hline = brocolors("crayons")["Pine Green"],
# col_vline = brocolors("crayons")["Pine Green"],
# lwd_hline = .5,
# lwd_vline = .5,
# cex.axis = .3, cex.lab = .3,
# xlim = c(-6, 10), ylim = c(-6, 10) ) {
# par( mar = c(2.2, 2, 1, .5), mgp = c(.7, .1, 0) )
# plot( xy, pch = "",
# axes = F, ylim = ylim, xlim = xlim, xlab = "", ylab = "" )
# points(xy, col = col_bkpoints, pch = pch_bkpoints, cex = cex_bkpoints, lwd = .5 )
# if ( !is.null(ind_points2) ) {
# points(xy[ind_points2, ], pch = pch_points2, col = col_points2,
# cex = cex_points2)
# }
# points(xy[ind_points1, ], pch = pch_points1, col = col_points1,
# cex = cex_points1)
# points(xy[ind_points3, ], pch = pch_points3, col = col_points3,
# cex = cex_points3)
# if (xlab == "log2 (human/chimp) \n Ribosome occupancy") {
# title( xlab = xlab, cex.lab = cex.lab, line = 1.3)
# } else {
# title( xlab = xlab, cex.lab = cex.lab)
# }
# title( ylab = ylab, cex.lab = cex.lab)
# axis(1, tck = -.06, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# axis(2, tck = -.03, cex.axis = cex.axis, lwd = .6, at = c(-4, 0, 4, 8)
# , label = c(-4, 0, 4, 8) )
# abline(h = 0, lty = 1, col = col_hline, lwd = lwd_hline)
# abline(v = 0, lty = 1, col = col_vline, lwd = lwd_vline)
# abline(a = 0, b = 1, lty = 1, col = col_diag, lwd = .5)
# }
# # plotDivergenceScatter.riborna.sigpro <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.riborna,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.riborna,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Ribo log2 RPKM (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.riborna[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)>abs(xy.rnapro$pro)),],
# # pch=pch[1],bg=bg.rnapro[1],col=col.rnapro[1],cex=cex[1])
# # points(xy.riborna[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)<abs(xy.rnapro$pro)),],
# # pch=pch[2],bg=bg.rnapro[2],col=col.rnapro[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.riborna[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.riborna[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# # }
# #
# #
# # plotDivergenceScatter.rnapro.sigpro <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.rnapro,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.rnapro,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Protein log2 FC (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.rnapro[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)>abs(xy.rnapro$pro)),],
# # pch=pch[1],bg=bg.rnapro[1],col=col.rnapro[1],cex=cex[1])
# # points(xy.rnapro[res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna)<abs(xy.rnapro$pro)),],
# # pch=pch[2],bg=bg.rnapro[2],col=col.rnapro[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.rnapro[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.rnapro[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# # }
# #
# #
# ###############
# # plotDivergenceScatter.riborna.sigribo <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.riborna,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.riborna,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Ribo log2 RPKM (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.riborna[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)),],
# # pch=pch[1],bg=bg.riborna[1],col=col.riborna[1],cex=cex[1])
# # points(xy.riborna[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)),],
# # pch=pch[2],bg=bg.riborna[2],col=col.riborna[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.riborna[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.riborna[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# # }
# #
# #
# # plotDivergenceScatter.rnapro.sigribo <-
# # function(xy.riborna,xy.rnapro,res.riborna,res.rnapro,pch,
# # bg.riborna,col.riborna,
# # bg.rnapro,col.rnapro,col.both,cex) {
# # # dcols=densCols(xy.rnapro,colramp=colorRampPalette(brewer.pal(9, "Greys")[-(1:2)]))
# # plot(xy.rnapro,pch="",
# # xlab="mRNA log2 RPKM (Human/Chimp)", ylab="Protein log2 FC (Human/Chimp)",
# # axes=F,xlim=c(-7,7),ylim=c(-6,10))
# # axis(1,at=seq(-6,6,2),labels=seq(-6,6,2))
# # axis(2,at=seq(-4,8,2),labels=seq(-4,8,2))
# # points(xy.rnapro[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)),],
# # pch=pch[1],bg=bg.riborna[1],col=col.riborna[1],cex=cex[1])
# # points(xy.rnapro[res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)),],
# # pch=pch[2],bg=bg.riborna[2],col=col.riborna[2],cex=cex[1])
# # ii1 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)>abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) > abs(xy.rnapro$pro))
# # ii2 = res.riborna$int.qval<.01 & (abs(xy.riborna$rna)<abs(xy.riborna$ribo)) & res.rnapro$int.qval<.01 & (abs(xy.rnapro$rna) < abs(xy.rnapro$pro))
# # points(xy.rnapro[ii1,],
# # pch=pch[3],col=col.both[1],cex=cex[2])
# # points(xy.rnapro[ii2,],
# # pch=pch[3],col=col.both[2],cex=cex[2])
# # abline(h=0,v=0,lty=2,col="grey")
# goHeatmapData <- function(golist){
# golist_test <- list(golist[[3]], golist[[4]])
# golist <- golist_test
# golist <- golist_bp
# nlist <- length(golist)
# goIDlist <- lapply( 1:nlist, function(i) {
# golist[[i]][ , 1]
# })
# names(goIDlist) <- names(golist)
# goTermlist <- lapply( 1:nlist, function(i) {
# golist[[i]]$Term
# })
# goset <- unique( unlist(goIDlist) )
# gosetTerm <- unique( unlist(goTermlist) )
# heatmap_allgos <- data.frame(ID = goset)
# heatmap_allgos$Term <- gosetTerm
# pvals <- merge(golist[[1]][, c(1,2,7)], golist[[2]][, c(1,2,7)],
# all = TRUE, by = "GOBPID")
# pvals
# # pvals <- lapply(1:nlist, function(i) {
# # foo <- rep(1-10^(-6), length(gosetTerm))
# # foo[ which(goset %in% golist[[i]][ , 1]) ] <- golist[[i]]$Pvalue[ which( golist[[i]][ , 1] %in% goset )]
# # return(foo)
# # })
# # pvals <- do.call(cbind, pvals)
# pvals_bp <- pvals
# pvals_test <- pvals
# colnames(pvals) <- names(golist)
# heatmap_allgos <- cbind(heatmap_allgos, pvals)
# colnames(heatmap_allgos)[1] <- colnames(golist[[1]])[1]
# ind <- which( is.na(heatmap_allgos), arr.ind = TRUE)
# heatmap_allgos[ ind ] <- 1
# rownames(heatmap_allgos) <- heatmap_allgos[ , 1]
# iisig <- pvals > .01
# iisig_gos <- which(rowSums(iisig)!=nlist)
# heatmap_allgos <- heatmap_allgos[iisig_gos, ]
# return(heatmap_allgos)
# }
# #' @example hmap <- plotHeatmap(heatmap_allgos,
# #' labCol = c("Ribo > mRNA", "RNA > Ribo",
# #' "Ribo > Protein", "Protein > Ribo"))
# plotHeatmap <- function(heatmapData, mar = c(0, 0, 0, 0),
# oma = c(0, 0, 0, 0), keysize = 1, cexRow = 1.3, cexCol = 1.3,
# labCol = NULL, key = TRUE, margin = c(5,5),
# dendrogram = "row", breaks = NULL) {
# #heatmapData <- heatmap_allgos_bp
# # mar = c(8, 1, 0, 20)
# # oma = c(2, 5, 0, 40)
# require(broman)
# crayon <- brocolors("crayon")
# heatcols <- colorRampPalette(c("white", crayon["Banana Mania"],
# crayon["Laser Lemon"],
# crayon["Burnt Orange"], crayon["Orange Red"],
# crayon["Plum"]))
# par( mfrow = c(1,1), mar = mar, oma = oma )
# require(gplots)
# mat <- heatmapData[ , -c(1:2)]
# rownames(mat) <- heatmapData$Term
# hmap <- heatmap.2(as.matrix(-log10(mat)), main = "",
# labCol = labCol, cexCol = cexCol,
# cexRow = cexRow,
# breaks = breaks,
# sepcol = "grey20",
# sepwidth=c(0.01, 0.01), lwd = .2,
# colsep = 1:ncol(mat), Colv = FALSE, key = key,
# rowsep = 1:nrow(mat), dendrogram = dendrogram, margin = margin,
# srtCol = 0, adjCol = c(.5,NA), offsetCol = 0, offsetRow = .1,
# keysize = keysize, lwid = c(1.5, 7), lhei = c(.5, 5),
# col = heatcols, trace ="none", key.title = "-log p-value" )
# return(hmap)
# }
# plotBoxes2 <- function(datavec1, datavec2, main, cols,
# labels, ylim = NULL, cex.main = .4,
# main.line = .5) {
# if (!is.null(ylim)) {
# boxplot(datavec1, datavec2,
# outline=F, xlab="",lwd = .4,
# axes = F, col= cols, border = "black", notch = TRUE, ylim = ylim,
# varwidth = TRUE)
# } else {
# boxplot(datavec1, datavec2,
# outline=F, xlab="",lwd = .4,
# axes = F, col= cols, border = "black", notch = TRUE, varwidth = TRUE)
# }
# title(main = main, cex.main = cex.main, line = main.line )
# # axis(1, tck = -.03, cex.axis = .6, lwd = .6,
# # at = 1:2, labels = labels, col= "white")
# axis(2, tck = -.03, cex.axis = .4, lwd = .4)
# }
# # }
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