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fun-heatmap.R
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fun-heatmap.R
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## ==================================================================================== ##
# START Shiny App for analysis and visualization of transcriptome data.
# Copyright (C) 2016 Jessica Minnier
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# You may contact the author of this code, Jessica Minnier, at <[email protected]>
## ==================================================================================== ##
##================================================================================##
## HEATMAP
##================================================================================##
heatmap_colors = c("#2D004B","white","gold3")
heatcols = colorRampPalette(heatmap_colors)(500)
#Returns data for a heatmap
heatmap_subdat <- function(data_analyzed,
yname="log2cpm",
orderby="significance",
usesubset=FALSE,
subsetids=NULL,
FDRcut=0.05,maxgenes=NULL,
view_group=NULL,
sel_test=NULL,
filter_by_go=FALSE,
filter_fdr=FALSE,
filter_maxgene=TRUE,
filter_cpm=FALSE,
filter_fc=FALSE,
fold_change_range=NULL,
fold_change_groups=NULL,
group_filter_range =NULL,
fixed_genes_list=NULL) {
data_results = data_analyzed$results
data_results$unique_id = as.character(data_results$unique_id)
if(is.null(view_group)) view_group=data_analyzed$group_names
if((usesubset)&(!is.null(subsetids))) {thesegenes = subsetids}else{
if(orderby=="significance") {
res = data_results%>%filter(test==sel_test)
#Order by FDR
if(is.null(res$adj.P.Val)) res$adj.P.Val = res$P.Value
#tmpout = res[order(res$adj.P.Val),]
tmpout = res%>%arrange(adj.P.Val,P.Value,abs(logFC))
thesegenes = tmpout$unique_id
print(paste("start",length(thesegenes)))
if(length(thesegenes)==0) {return(NULL)}
## FILTER GENES by
#FDR cut
if((filter_fdr)&&(!is.null(FDRcut))) {
#print("yes"); print(FDRcut)
tmpg = tmpout$unique_id[which(tmpout$adj.P.Val<FDRcut)]
thesegenes = intersect(thesegenes,tmpg)
print(paste("fdrcut",length(thesegenes)))
}
if(length(thesegenes)==0) {return(NULL)}
#Range of fold change, fold_change_range[1:2], based on two selected groups
if((filter_fc)&&(!is.null(fold_change_groups))&&(!is.null(fold_change_range))) {
fcgroup1 = fold_change_groups[1]; fcgroup2 = fold_change_groups[2]
res_fc = data_results%>%filter(test==paste0(fcgroup1,"/",fcgroup2))
fc <- res_fc$logFC
tmpg = res_fc$unique_id[which((fc >= fold_change_range[1])*(fc <= fold_change_range[2])==1)]
thesegenes = intersect(thesegenes,tmpg)
print(paste("filterfc",length(thesegenes)))
}
if(length(thesegenes)==0) {return(NULL)}
tmp = tmpout%>%filter(unique_id%in%thesegenes)%>%arrange(adj.P.Val)
thesegenes = tmp$unique_id
}else {
#filter by standard deviation (SD of log2cpm is coefficient of variation of unlogged data)
tmpdat = data_analyzed$data_long
tmpdat = tmpdat%>%filter(group%in%view_group)
tmpdat = reshape2::dcast(tmpdat,unique_id~sampleid,value.var=yname)
tmpsd = apply(tmpdat[,-1],1,sd)
thesegenes = tmpdat$unique_id[order(tmpsd,decreasing=TRUE)]
}
if(length(thesegenes)==0) {return(NULL)}
if((filter_maxgene)&&(!is.null(maxgenes))) {
tmpg = thesegenes[1:min(maxgenes,length(thesegenes))]
thesegenes = tmpg
print(paste("maxgenes",length(thesegenes)))
}
}#end subsetids
if(length(thesegenes)==0) {return(NULL)}
subdat = filter(data_analyzed$data_long,unique_id%in%thesegenes,group%in%view_group)
data = reshape2::dcast(subdat,unique_id~sampleid,value.var=yname)
data_unique_id = data[,1]
data_geneids = data_analyzed$geneids[match(data_unique_id,data_analyzed$geneids$unique_id),]
data = data[,-1]
rown <- as.character(data_unique_id)
rown[is.na(rown)] <- ""
rownames(data) = rown
return(list("data"=data,"data_geneids"=data_geneids))
}
heatmap_data <- function(...) {
#create the data
tmpdat = heatmap_subdat(...)
if(is.null(tmpdat)) {frame()}else{
heatdat = tmpdat$data
heatdat1 = as.matrix(heatdat)
#order the rows same way heatmap does
Rowv <- rowMeans(heatdat1, na.rm = TRUE)
hcr <- hclust(dist(heatdat1))
ddr <- as.dendrogram(hcr)
ddr <- reorder(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
#hmap1 = heatmap(heatdat1,col=heatcols,margins=c(8,11),Colv=NA,scale="none",keep.dendro=TRUE,show=FALSE)
heatdat = data.frame(tmpdat$data_geneids, heatdat)
heatdat <- heatdat[rowInd,]
heatdat
}
}
heatmap_render <- function(yname,interactive=FALSE,...) {
#possible inputs
tmpdat <- heatmap_subdat(yname,...)
if(is.null(tmpdat)) {frame()}else{
heatdat = as.matrix(tmpdat$data)
myheatmap_colors = heatmap_colors
heatdat_rowmean = sweep(heatdat,1,rowMeans(heatdat))
if(min(heatdat_rowmean)>0) {myheatmap_colors = heatmap_colors[2:3]}
if(max(heatdat_rowmean)<0) {myheatmap_colors = heatmap_colors[1:2]}
color.palette <- colorRampPalette(myheatmap_colors)
#http://stackoverflow.com/questions/15351575/moving-color-key-in-r-heatmap-2-function-of-gplots-package
lmat = rbind(c(0,3),c(2,1),c(0,4))
lwid = c(1,4)
lhei = c(0.2,4,1)
# heatmap.2(heatdat,col=color.palette,
# margins=c(8,11),labRow= rownames(heatdat),
# Colv=FALSE,
# lmat = lmat, lwid = lwid, lhei = lhei,
# key.xlab=yname,
# scale="row",tracecol=NULL,trace='none',density.info='none',
# #changed to scale="row"
# symkey=TRUE)
#par(oma=c(0,0,0,5))
tcols = as.numeric(as.factor(do.call(rbind,strsplit(colnames(heatdat),"_"))[,1]))+1
# heatmap(heatdat_rowmean,labRow = rownames(heatdat),scale="none",col=color.palette(100),
# ColSideColors = rainbow(max(tcols))[tcols],
# margins=c(5,10))
if(interactive) {
heatmaply(heatdat_rowmean,colors=color.palette(100))%>% layout(margin = list(l = 300, b = 100))
}else{
aheatmap(heatdat_rowmean,col=color.palette(100),scale = "none",labRow = rownames(heatdat),revC=TRUE,
annCol=data.frame("group"=as.factor(do.call(rbind,strsplit(colnames(heatdat),"_"))[,1])))
}
}
}
#heatmap_ggvis_render(data_analyzed,sel_group=c("group1","group2"),maxgenes=20)
#tmpdat = heatmap_subdat(data_analyzed,sel_group=c("BE","HP"),maxgenes=200)
#Not actually used in app, rendering is done in server.R
# heatmap_ggvis_render <- function(...) {
#
# tmpdat = heatmap_ggvis_data(...)
# if(is.null(tmpdat)) {return(NULL)}else{
# heatdatmelt = tmpdat
#
#
# all_values <- function(x) {
# if(is.null(x)) return(NULL)
# thisrow <- heatdatmelt[heatdatmelt$id == x$id,]
# thisrow <- thisrow[,c("unique_id","sample",yname)]
# thisrow[,yname] <- round(thisrow[,yname],3)
# paste0(names(thisrow), ": ", format(thisrow), collapse = "<br />")
# }
#
# lb <- linked_brush(keys = 1:nrow(heatdatmelt),fill = "red")
#
# p <- heatdatmelt%>%
# ggvis(~sample, ~unique_id, fill:=~defcolor,stroke:=~defcolor, key:=~id)%>%
# layer_rects(width = band(), height = band()) %>%
# scale_nominal("x", padding = 0, points = FALSE) %>%
# scale_nominal("y", padding = 0, points = FALSE) %>%
# add_tooltip(html=all_values,on = "hover") %>%
# lb$input()%>%set_options(width=600,height=1000)
# return(p)
#
# }}
#Make a dataset for ggvis, needs to be melted first
heatmap_ggvis_data <- function(yname,...) {
tmpdat = heatmap_subdat(yname,...)
if(is.null(tmpdat)) {return(NULL)}else{
heatdat = tmpdat$data
heatdat <- sweep(heatdat, 1L, rowMeans(heatdat, na.rm = TRUE), check.margin = FALSE)
heatdat1 = as.matrix(heatdat)
#browser()
heatdat_scale = heatdat
# x <- heatdat
# x <- sweep(x, 1L, rowMeans(x, na.rm = TRUE), check.margin = FALSE)
# sx <- apply(x, 1L, sd, na.rm = TRUE)
# x <- sweep(x, 1L, sx, "/", check.margin = FALSE)
# heatdat_scale = x
#order the rows same way heatmap does
Rowv <- rowMeans(heatdat1, na.rm = TRUE)
hcr <- hclust(dist(heatdat1))
ddr <- as.dendrogram(hcr)
ddr <- reorder(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
myorder = rowInd
#myorder = myorder[length(myorder):1]
heatdat <- heatdat[myorder,]
heatdat_scale <- heatdat_scale[myorder,]
Colv <- colMeans(heatdat1,na.rm=TRUE)
hcc <- hclust(dist(t(heatdat1)))
ddc <- as.dendrogram(hcc)
colInd = order.dendrogram(ddc)
ddc <- reorder(ddc, Colv)
myorder = order.dendrogram(ddc)
# myorder = myorder[length(myorder):1]
heatdat <- heatdat[,myorder]
heatdat_scale <- heatdat_scale[,myorder]
heatdat$unique_id = rownames(heatdat)
heatdat$order = 1:nrow(heatdat)
print(dim(heatdat))
heatdatmelt = melt(heatdat,value.name=yname,id.vars=c("unique_id","order"),
variable.name="sample")
heatdat_scale$unique_id = rownames(heatdat_scale)
heatdat_scale$order = 1:nrow(heatdat_scale)
print(dim(heatdat_scale))
heatdatmelt_scale = melt(heatdat_scale,value.name=paste0(yname,"_scaled"),id.vars=c("unique_id","order"),variable.name="sample")
heatdatmelt[,paste0(yname,"_scaled")]= heatdatmelt_scale[,paste0(yname,"_scaled")]
#can make custom fill color as a column in heatdatmelt, then need to pass in key to get tooltip to show log2cpm and not color
#http://stackoverflow.com/questions/24810470/custom-fill-color-in-ggvis-and-other-options
heatdatmelt$defcolor = heatcols[1]
heatdatmelt$id = 1:nrow(heatdatmelt)
lenhc = length(heatcols)
ll = heatdatmelt[,paste0(yname,"_scaled")]
minll = -1*ceiling(max(abs(ll)))
maxll = ceiling(max(abs(ll)))
dd = heatdatmelt$defcolor
#print(summary(ll))
#dd[ll< 0] = as.character(cut(ll[ll<0],seq(floor(min(ll)),0,length.out=(lenhc/2+1)),labels=heatcols[1:floor(lenhc/2)]))
#dd[ll>= 0] = as.character(cut(ll[ll>=0],seq(0,ceiling(max(ll)),length.out=(lenhc/2+1)),labels=heatcols[(floor(lenhc/2)+1):lenhc]))
dd[ll< 0] = as.character(cut(ll[ll<0],seq(minll,0,length.out=(lenhc/2+1)),labels=heatcols[1:floor(lenhc/2)]))
dd[ll>= 0] = as.character(cut(ll[ll>=0],seq(0,maxll,length.out=(lenhc/2+1)),labels=heatcols[(floor(lenhc/2)+1):lenhc]))
heatdatmelt$defcolor = dd
#to do: add scale
heatdatmelt
}}