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yeast.Rnw
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yeast.Rnw
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% $Id: hdf.Rnw 336 2012-09-19 08:45:51Z jbao $
\documentclass[a4paper]{article}
\usepackage{graphicx,subfig}
\usepackage{pdfpages}
\usepackage{amsmath}
\usepackage[margin=1in]{geometry}
\usepackage{longtable}
\usepackage{color, colortbl}
\definecolor{Gray}{gray}{0.9}
\DeclareMathOperator{\erf}{erf}
\DeclareMathOperator{\IQR}{IQR}
\everymath{\displaystyle}
\renewcommand{\thesubfigure}{\Alph{subfigure}}
\mathchardef\mhyphen="2D
\newenvironment{narrow}[2]{%
\begin{list}{}{%
\setlength{\topsep}{0pt}%
\setlength{\leftmargin}{#1}%
\setlength{\rightmargin}{#2}%
\setlength{\listparindent}{\parindent}%
\setlength{\itemindent}{\parindent}%
\setlength{\parsep}{\parskip}}%
\item[]}{\end{list}}
\title{Cytokine/receptor mRNA expression levels in HDF}
\author{Jie Bao}
\begin{document}
\maketitle
<<label=base,echo=False,fig=FALSE>>=
library(ggplot2)
library(reshape)
library(org.Sc.sgd.db)
library(org.EcK12.eg.db)
library(GOstats)
library(mnormt)
library(mclust)
library(GO.db)
library(igraph)
library(ggdendro)
library(grid)
fontsize <- 15
theme_set(theme_bw(fontsize))
subplot <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)
vplayout <- function(x, y) {
grid.newpage()
pushViewport(viewport(layout=grid.layout(x,y)))
}
@
<<label=degree_response,echo=False,fig=True,eval=True>>=
org <- 'yeast'
org.dir <- 'yeast'
if (org == 'yeast') {
type <- c('alpha', 'cdc15', 'cdc28', 'elu')
size <- 6178
dat <- read.delim('~/data/yeast/yeast_alpha.dat', sep='|')
#ppi <- read.graph('/home/jbao/data/network/biogrid/biogrid_yeast_3.1.93.xml', 'graphml')
elist <- read.delim('~/gnw/src/ch/epfl/lis/gnwgui/rsc/net/yeast_transcriptional_network_Balaji2006.tsv', header=F)
ppi <- graph.data.frame(elist[,1:2], directed=F)
} else {
type <- c('cold', 'heat', 'oxidative', 'lactose')
size <- 4400
dat <- read.delim('~/data/ecoli/GSE20305_cold.dat', sep='|')
elist <- read.delim('~/gnw/src/ch/epfl/lis/gnwgui/rsc/net/ecoli_transcriptional_network_regulonDB_6_2.tsv', header=F)
ppi <- graph.data.frame(elist[,1:2], directed=F)
}
all.dat <- NULL
for (t in 1:length(type)) {
ts <- read.delim(paste('~/data/',org.dir,'/',org,'_',type[t],'.dat',sep=''), sep='|')
pval <- read.delim(paste('~/data/',org.dir,'/mds/pval_',org,'_',type[t],'_',size,'.dat',sep=''), header=F)$V1
common <- intersect(V(ppi)$name, ts$Gene)
ppi.idx <- match(common, V(ppi)$name)
pval.idx <- match(common, ts$Gene)
deg <- degree(ppi, ppi.idx)
pval <- pval[pval.idx]
all.dat <- rbind(all.dat, data.frame(degree=deg,response=-log10(pval),
cond=type[t], gene=common))
}
print(ggplot(all.dat, aes(response,degree,colour=cond)) +
geom_point() +
scale_colour_brewer('',palette='Set1') +
#geom_text(data=ts[ts$pval<1e-10|ts$degree>200,],aes(label=Gene),size=3,
# vjust=-0.5,alpha=0.3) +
#geom_abline(intercept=0,slope=1,colour='blue',linetype='dashed') +
#xlim(0,20) + ylim(0,1000) +
labs(x='Response',y='Degree',title=expression(atop(paste('Time resolved response of'~italic(S.cerevisiae)),paste('to synchronization')))) +
#labs(x='Response',y='Degree',title=expression(atop(paste('Time resolved response of'~italic(E.coli)),paste('to stress')))) +
theme(legend.position=c(0.9,0.85),legend.key=element_blank(),
legend.background=element_blank())
)
@
% ----------------------------------------------------------------------------
<<label=go_data,echo=False,fig=False,cache=True,eval=True>>=
res <- NULL
for (t in 1:length(type)) {
sig.gene <- all.dat$gene[all.dat$cond==type[t]&all.dat$response>-log10(0.05)]
#sig.gene <- all.dat$gene[all.dat$cond==type[t]&all.dat$response < -log10(0.95)]
# GO enrichment
if (org == 'GSE20305') {
sample <- unlist(mget(as.character(sig.gene),
org.EcK12.egSYMBOL2EG, ifnotfound=NA))
universe <- unlist(mget(as.character(dat$Gene),
org.EcK12.egSYMBOL2EG, ifnotfound=NA))
#universe <- dat$Gene
params <- new("GOHyperGParams", geneIds = sample,
universeGeneIds = universe, annotation = "org.EcK12.eg", ontology = "BP",
pvalueCutoff = 0.05, conditional = FALSE, testDirection = "under")
}
else {
universe <- dat$Gene
params <- new("GOHyperGParams", geneIds = sig.gene,
universeGeneIds = universe, annotation = "org.Sc.sgd", ontology = "BP",
pvalueCutoff = 0.05, conditional = FALSE, testDirection = "over")
}
go_over <- hyperGTest(params)
stat <- summary(go_over)
if (t == 1)
common <- stat$Term
else
common <- intersect(common, stat$Term)
tmp <- stat[match(common,stat$Term),]
tmp$cond <- type[t]
res <- rbind(res, tmp)
}
res <- res[res$Term %in% common,]
# Get the level for all GO ids
res$level <- NA
for (ii in 1:nrow(res)) {
k <- 1
gg <- res$GOBPID[ii]
while (gg != "all") {
gg <- unlist(mget(gg,GOBPPARENTS))[1]
k <- k+1
}
res$level[ii] <- k
}
@
% ----------------------------------------------------------------------------
<<label=go,echo=False,fig=True,eval=True,width=7,height=8>>=
res <- subset(res, level==4)
final <- reshape(res[,c('cond','Term','Pvalue')], v.names='Pvalue',
idvar='Term', timevar='cond', direction='wide')
rownames(final) <- final$Term
final <- final[,-1]
dd.col <- as.dendrogram(hclust(dist(t(final))))
col.ord <- order.dendrogram(dd.col)
df <- final[,col.ord]
names(df) <- sub('Pvalue\\.','',names(df))
df.m <- melt(as.matrix(df))
df.m <- within(df.m, X2 <- factor(X2, levels = names(df)))
df.m$X1 <- sapply(df.m$X1, function(x) strwrap(x,width=40)[1])
mycolor <- gray.colors(128,0.9,0)
p1 <- ggplot(df.m,aes(X2,X1)) +
#geom_tile(aes(fill=-log10(value))) +
geom_tile(aes(fill=-log10(value))) +
scale_fill_gradientn(colours=mycolor) +
guides(fill = guide_colorbar(barwidth = 0.5, barheight = 10,
title.hjust=1.6, title.vjust=0,
title=expression(paste(-log[10]~italic(p),'-value')))) +
labs(x = "",y = "") + scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
opts(axis.ticks = theme_blank(),axis.text.x=theme_text(angle=90),
panel.border=theme_blank(),legend.title=theme_text(angle=90),
plot.margin=unit(c(0, 0, 0, 0), "cm"))
### Use ggdendro to extract dendrogram data ###
ddata_x <- dendro_data(dd.col)
### Set up a blank theme
theme_none <- opts(
panel.grid.major = theme_blank(),
panel.grid.minor = theme_blank(),
panel.background = theme_blank(),
panel.border = theme_blank(),
axis.title.x = theme_text(colour=NA),
axis.title.y = theme_blank(),
axis.text.x = theme_blank(),
axis.text.y = theme_blank(),
axis.line = theme_blank(),
axis.ticks = theme_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")
#axis.ticks.length = theme_blank()
)
# Dendrogram 1
p2 <- ggplot(segment(ddata_x)) +
geom_segment(aes(x=x, y=y, xend=xend, yend=yend)) +
theme_none
grid.newpage()
print(p1, vp=viewport(1, 0.9, x=0.5, y=0.45))
print(p2, vp=viewport(0.4, 0.1, x=0.65, y=0.95))
@
% ----------------------------------------------------------------------------
<<label=tfbs,echo=False,fig=False,cache=True,eval=False>>=
# GAGE
library(gage)
data(kegg.gs)
data(egSymb)
kegg.gs.sym <- lapply(kegg.gs, eg2sym)
mSigDB <- readLines("~/MicroarrayAnalysis/trunk/r/c3.tft.v3.0.symbols.gmt")
mSigDB <- strsplit(mSigDB, "\t")
names(mSigDB) <- sapply(mSigDB, function(x) x[1])
mSigDB <- sapply(mSigDB, function(x) x[3:length(x)])
label <- 1:24
diff <- read.delim('~/data/hdf/hdf_diff_16091.tsv')
rownames(diff) <- diff$Gene
diff <- diff[,-1]
get.tfbs <- function() {
all.path <- NULL
for (i in 2:ncol(diff)) {
#glist <- data.frame(diff=diff[,i], row.names=diff$Gene)
#test with direction: either up or down
kegg.rk <- gage(diff, gsets = mSigDB, ref = 1, samp = i,
saaTest=gs.KSTest, same.dir=T)
current <- as.data.frame(kegg.rk$greater)
current <- current[!is.na(current$q.val),]
all.path <- c(all.path, list(current))
idx <- current$p.val < 0.2
if (i == 2)
common <- rownames(current)[idx]
else
common <- intersect(common, rownames(current)[idx])
}
final <- matrix(0,length(common),length(all.path))
for (j in 1:length(all.path)) {
for (i in 1:length(common)) {
final[i,j] = all.path[[j]]$q.val[rownames(all.path[[j]])==common[i]]
}
}
list(common=common, final=final)
}
tfbs <- get.tfbs()
@
% ----------------------------------------------------------------------------
<<label=tf_skewt,echo=False,fig=False,cache=True,eval=False>>=
tf <- read.delim('~/data/tf/tf_table_final.csv', sep=';')
sig.tf <- foreach(i=2:ncol(diff), .combine='c') %dopar% {
fit <- st.mle(y=diff[,i])
pval <- 1 - pst(diff[,i], dp=fit$dp)
list(rownames(diff)[pval<0.01 & rownames(diff) %in% tf$Gene_Abbrev])
}
@
% ----------------------------------------------------------------------------
<<label=tfbs_sig,echo=False,fig=False,cache=True,eval=False>>=
#tfbs.entrez <- list(t2=c('ZEB1','NFKB','TF65'),t3=c('NFKB','TF65'),
# t4=c('NFKB','TF65','IRF'),t5=c('CEBP','IRF','NFKB','TF65','NFAT'),
# t6=c('CEBP','IRF','NFKB','TF65','NFAT'),
# t7=c('ZEB1','CEBP','IRF','NFKB','TF65','NFAT'),
# t8=c('ZEB1','CEBP','IRF','NFKB','TF65','NFAT'),
# t9=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','FOXJ2','SREBF1','STAT5B'),
# t10=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','FOXJ2','SREBF1','STAT5B','^REL$'),
# t11=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','FOXJ2','SREBF1','STAT5B','^REL$'),
# t12=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','SREBF1','STAT5B','^REL$'),
# t13=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','SREBF1','STAT5B','^REL$'),
# t14=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','SREBF1','STAT5B','^REL$'),
# t15=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','SREBF1','STAT5B','^REL$'),
# t16=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','SREBF1','STAT5B','^REL$'),
# t17=c('ZEB1','PAX3','CEBP','IRF','NFKB','TF65','NFAT','SREBF1','STAT5B','^REL$'),
# t18=c('PAX3','CEBP','IRF','NFKB','TF65','SREBF1','STAT5B','^REL$'),
# t19=c('PAX3','CEBP','IRF','NFKB','TF65','SREBF1','STAT5B','^REL$'),
# t20=c('PAX3','CEBP','IRF','NFKB','TF65','SREBF1','STAT5B','^REL$'),
# t21=c('CEBP','IRF','NFKB','TF65','STAT5B'),
# t22=c('CEBP','IRF','NFKB','TF65','STAT5B'),
# t23=c('IRF','NFKB','TF65'),
# t24=c('IRF','NFKB','TF65')
# )
tf.interesting <- list(tf=c('CEBPD','RELB'))
tpoint <- 1:24
tf.entrez <- NULL
for (i in 1:length(tf.interesting)) {
tf <- unlist(sapply(tf.interesting[[i]], function(x) hippie.map$idmap$official[grep(x,hippie.map$idmap$official)]))
tf.entrez <- rbind(tf.entrez, data.frame(tf.entrez=hippie.map$idmap$entrez[match(tf,hippie.map$idmap$official)],time=paste(tpoint[i],'h')))
}
receptor <- read.delim('~/MicroarrayAnalysis/trunk/python/hsa04060.receptor',
header=F)$V1
receptor.entrez <- unlist(mget(as.character(receptor), org.Hs.egSYMBOL2EG,
ifnotfound=NA))
receptor.entrez <- receptor.entrez[receptor.entrez %in% hippie.map$idmap$entrez]
tf.entrez <- tf.entrez[tf.entrez$tf.entrez %in% hippie.map$idmap$entrez,]
#mfpt.sp <- NULL
mfpt.sp <- foreach(i=1:length(receptor.entrez), .combine='rbind') %:%
foreach(j=1:nrow(tf.entrez), .combine='rbind') %dopar% {
#for (i in 1:10) {
# for (j in 1:10) {
ii <- hippie.map$idmap$vertex[which(receptor.entrez[i] == hippie.map$idmap$entrez)] + 1
jj <- hippie.map$idmap$vertex[which(tf.entrez$tf.entrez[j] == hippie.map$idmap$entrez)] + 1
receptor.idx <- match(receptor.entrez[i], V(hippie.net)$gene)
tf.idx <- match(tf.entrez$tf.entrez[j], V(hippie.net)$gene)
sp <- shortest.paths(hippie.net, receptor.idx, tf.idx, weights=NA) # BFS
data.frame(mfpt=hippie$mfpt[ii,jj], sp=as.numeric(sp),
receptor=get(receptor.entrez[i], org.Hs.egSYMBOL),
tf=hippie.map$idmap$official[which(tf.entrez$tf.entrez[j]==hippie.map$idmap$entrez)], time=tf.entrez$time[j])
#}
}
@
% ----------------------------------------------------------------------------
<<label=plot_tfbs,echo=False,fig=True,width=10,height=8,eval=False>>=
#mycolor <- topo.colors(128)
mycolor <- gray.colors(128,0.9,0)
truncated <- sub('.*V\\$','',tfbs$common)
df <- as.data.frame(tfbs$final[order(truncated),])
names(df) <- paste(1:24,'h',sep='')
df$TFBS <- tfbs$common[order(truncated)]
df$group <- 'Other'
df$group[grep('STAT',df$TFBS)] <- 'STAT'
df$group[grep('NFK',df$TFBS)] <- 'NFKB'
df$group[grep('RELA',df$TFBS)] <- 'NFKB'
df$group[grep('CEBP',df$TFBS)] <- 'CEBP'
df <- melt(df, id.vars=c('TFBS','group'))
df <- within(df, TFBS <- factor(TFBS, levels = tfbs$common[order(truncated)]))
with(df, levels(TFBS))
df <- within(df, group <- factor(group, levels = c('NFKB','CEBP','STAT','Other')))
with(df, levels(group))
df$time <- rep(1:24, each=length(tfbs$common))
print(ggplot(df,aes(variable,TFBS)) + geom_tile(aes(fill=-log10(value))) +
scale_fill_gradientn(colours=mycolor) +
guides(fill = guide_colorbar(barwidth = 0.5, barheight = 10, title.hjust=2.2,
title=expression(paste(-log[10],italic(p),'-value')))) +
labs(x = "",y = "") + scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
opts(axis.ticks = theme_blank(),axis.text.x=theme_text(angle=90),
panel.border=theme_blank(),legend.title=theme_text(angle=90))
)
#print(ggplot(df,aes(time,-log10(value),group=TFBS)) +
# geom_point() + geom_line() +
# facet_wrap(~group) +
# scale_colour_brewer('',palette='Set1') +
# xlab('time (h)') + ylab(expression(paste(-log[10],' ',italic(p),'-value'))) +
# opts(legend.position='none',legend.key=theme_blank(),
# title='Transcription factor target enrichment\n')
#)
@
% ----------------------------------------------------------------------------
<<label=pval,echo=False,fig=False,cache=True,eval=False>>=
core <- cbind(ctrl[,-1],treated[,-1])
pval <- apply(core,1,lrtest)
qval <- p.adjust(pval, 'BH')
@
% ----------------------------------------------------------------------------
\begin{figure}[ht]
\centering
<<label=fit_ex,echo=False,fig=True,eval=True,width=7,height=7>>=
#idx <- symbol == 'CEBPD'
#idx <- grep('CEBPD|RELB|BHLHB5', symbol)
#ctrl$cond <- 'Control'
#treated$cond <- 'NHK medium'
#df.ctrl <- melt(ctrl[idx,], id.vars=c('Gene','cond'))
#df.ctrl$time <- rep(ctrl.tpoint, each=length(idx))
#df.treated <- melt(treated[idx,], id.vars=c('Gene','cond'))
#df.treated$time <- rep(treated.tpoint, each=length(idx))
#df <- rbind(df.ctrl, df.treated)
#df <- within(df, cond <- factor(cond, levels = c('NHK medium','Control')))
#with(df, levels(cond))
#
## full/reduced model fit
#core <- cbind(ctrl[idx,2:(length(ctrl.tpoint)+1)],treated[idx,2:(length(treated.tpoint)+1)])
#red <- apply(core,1,fit.red)
#fit.reduced <- as.data.frame(red[1:length(ctrl.tpoint),])
#names(fit.reduced) <- symbol[idx]
#full <- apply(core,1,fit.full)
#fit.ctrl <- as.data.frame(full[1:length(ctrl.tpoint),])
#names(fit.ctrl) <- symbol[idx]
#fit.treated <- as.data.frame(full[-(1:length(ctrl.tpoint)),])
#names(fit.treated) <- symbol[idx]
#
#df.param <- apply(cbind(ctrl[idx,2:(length(ctrl.tpoint)+1)],
# treated[idx,2:(length(treated.tpoint)+1)]),1,lrtest)
##stop()
#df.param <- melt(df.param)
#df.param <- transform(df.param, Gene=symbol[idx],
# label=sprintf("italic(p)==%.1e",df.param$value))
#
#fit.reduced <- transform(fit.reduced, time=ctrl.tpoint)
#fit.reduced <- melt(fit.reduced, id.vars='time')
#fit.reduced <- transform(fit.reduced, Gene=rep(symbol[idx],each=length(ctrl.tpoint)))
#
#fit.ctrl <- transform(fit.ctrl, time=ctrl.tpoint)
#fit.ctrl <- melt(fit.ctrl, id.vars='time')
#fit.ctrl <- transform(fit.ctrl, Gene=rep(symbol[idx],each=length(ctrl.tpoint)))
#
#fit.treated <- transform(fit.treated, time=treated.tpoint)
#fit.treated <- melt(fit.treated, id.vars='time')
#fit.treated <- transform(fit.treated, Gene=rep(symbol[idx],each=length(treated.tpoint)))
#
#p <- ggplot(df, aes(time,value)) + geom_point(aes(colour=cond)) +
# geom_line(aes(colour=cond)) +
# facet_wrap(~Gene) +
# geom_line(data=fit.reduced,aes(time,value),alpha=0.2,linetype='dotdash') +
# geom_line(data=fit.ctrl,aes(time,value),alpha=0.2,linetype='dashed') +
# geom_line(data=fit.treated,aes(time,value),alpha=0.2,linetype='dashed') +
# geom_text(data=df.param, aes(x=4.5,y=4.5,label=label),size=5,parse=T) +
# scale_colour_brewer('',palette='Set1') +
# xlab('Time (h)') + ylab(expression(paste(log[2],' fold expression')))
#print(p+opts(legend.position=c(0.9,0.9),legend.key=theme_blank()))
# #title='CEBPD\n'))
treated.df <- list(dat=treated[which(treated$Gene=='CEBPD'),], cond='NHK',
tpoint=treated.tpoint)
df.ex <- plot.logistic.example(treated.df, 'CEBPD')
@
\caption{Example of the differential time series analysis using the
full/reduced model fitting (Mar and Quackenbush \emph{PLoS Comput Biol} 2009).
Gene expression time course from 2 conditions
are fitted with a cubic polynomial either together (reduced model, dot-dashed
line) or separately (full model, dashed line). The deviances of the 2 model
fits are compared using the ANOVA test and a $p\mhyphen$value is generated,
which compares the goodness-of-fit of both model fits and
implies the probability that this gene is differentially expressed under
the 2 given conditions (equivalent to the alternative hypothesis that
the full model fits the data better).}
\label{fig:fit_ex}
\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=pval_hist,echo=False,fig=True>>=
%#core <- cbind(ctrl[,-1],treated[,-1])
%#pval <- apply(core,1,lrtest)
%pval <- read.delim('~/data/hdf/mds/pval_hdf_nhk_16091.dat', header=F)$V1
%qval <- p.adjust(pval, 'BH')
%res <- hist(-log10(qval), plot=F)
%df <- data.frame(x=res$mids, y=res$counts, type='q-value')
%
%print(ggplot(df,aes(x,y)) +
% geom_bar(stat='identity',fill='gray',colour='black') +
% #xlab(expression(paste(-log[10],' ',italic(q),'-value','\n'))) +
% xlab('MDS-based response metric') +
% ylab('count') +
% opts(title='MDS')
%)
%@
%\end{center}
%\caption{Histogram of the $-\log_{10}$ transformed and multiple testing
%corrected (Benjamini-Hochberg) $p\mhyphen$values according
%to a analysis of deviance test of the full/reduced model fit of the time series.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
<<label=tf_volcano,echo=False,fig=True,cache=True,eval=True>>=
ctrl <- read.delim('~/data/hdf/hdf_ctrl_16091.tsv')
treated <- read.delim('~/data/hdf/hdf_nhk_16091.tsv')
diff <- read.delim('~/data/hdf/hdf_diff_16091.tsv')
tf <- read.delim('~/data/tf/tf_table_final.csv', sep=';')
goi <- tf$Gene_Abbrev
genes <- tf$Class
#goi <- c('^IL[0-9]+$','^TNF[^R]','^CXCL','^CCL')
#genes <- c('ILs','TNFs','CXCLs','CCLs')
ctrl.tpoint <- c(0,2,4,8,12,24)
treated.tpoint <- c(0,1,2,3,4,6,8,10,12,18,24)
diff.tpoint <- 0:24
plot.ts <- function(goi, genes) {
df <- NULL
for (i in 1:length(goi)) {
idx <- grep(goi[i],ctrl$Gene)
tmp <- data.frame(tp=ctrl.tpoint,mean=colMeans(ctrl[idx,-1]),
sd=apply(ctrl[idx,-1],2,sd),gene=rep(genes[i],length(ctrl.tpoint)),
cond=rep('Control',length(ctrl.tpoint)))
df <- rbind(df,tmp)
tmp <- data.frame(tp=treated.tpoint,mean=colMeans(treated[idx,-1]),
sd=apply(treated[idx,-1],2,sd),gene=rep(genes[i],length(treated.tpoint)),
cond=rep('NHK',length(treated.tpoint)))
df <- rbind(df,tmp)
}
df <- within(df, cond <- factor(cond, levels = c('Control','NHK')))
with(df, levels(cond))
print(ggplot(df,aes(tp,mean)) +
geom_ribbon(aes(ymin=mean-sd,ymax=mean+sd),fill='gray',colour='gray') +
geom_point() + scale_colour_brewer(palette='Set1') +
geom_line(aes(colour=cond)) +
facet_grid(cond~gene) + xlab('\nTime (h)') +
#ylim(-1,3) +
ylab(expression(paste(log[2],' fold expression','\n'))) +
opts(title='Gene expression time course of HDF\n',legend.position='none')
)
}
plot.pval <- function(goi, genes) {
df <- NULL
for (i in 1:length(goi)) {
idx <- grep(goi[i],ctrl$Gene)
core <- cbind(ctrl[idx,-1], treated[idx,-1])
pval <- apply(core,1,lrtest)
df <- rbind(df, data.frame(pval=pval,gene=ctrl$Gene[idx],group=genes[i],pos=i))
}
jitter <- runif(nrow(df),-0.2,0.2)
print(ggplot(df,aes(group,-log10(pval))) +
geom_boxplot(width=0.5,outlier.colour='white') +
geom_point(aes(pos+jitter,-log10(pval))) +
#scale_x_discrete(breaks=1:length(goi),labels=genes) +
geom_text(aes(pos+jitter,-log10(pval),label=gene),size=3,hjust=-0.2,alpha=0.3) +
xlab('') + ylab(expression(paste(-log[10],' ',italic(p),'-value','\n'))) +
opts(title='Gene expression time course of HDF\n',legend.position='none')
)
df
}
#source('stat-ellipse.R')
plot.tf.pval <- function() {
idx <- match(goi, ctrl$Gene)
cat <- genes[!is.na(idx)]
idx <- na.omit(idx)
core <- cbind(ctrl[idx,-1], treated[idx,-1])
pval <- apply(core,1,lrtest)
qval <- p.adjust(pval, 'BH')
diff <- rowMeans(diff[idx,-1])
df <- data.frame(qval=qval,gene=ctrl$Gene[idx],group=cat,diff=diff)
pdf('~/data/hdf/hdf-tf_volcano.pdf',8,11)
group.level <- levels(factor(df$group))
get.contour <- function(df) {
z <- kde2d(df$diff, -log10(df$qval), n=50)
data.frame(x=rep(z$x,50), y=rep(z$y,each=50), z=melt(z$z)$value,
group=df$group[1])
}
for (i in 1:ceiling(length(group.level)/12)) {
#for (i in 1:4) {
slice <- seq((i-1)*12+1,i*12)
slice <- slice[!(slice > length(group.level))]
current <- group.level[slice]
to.plot <- subset(df, group %in% current)
#contour.data <- ddply(to.plot, .(group), get.contour)
#calculating ellipses
library(ellipse)
df.ell <- data.frame()
for (g in levels(factor(to.plot$group))) {
current.data <- subset(to.plot, group==g)
current.df <- as.data.frame(ellipse(cor(current.data$diff,
-log10(current.data$qval)),
scale=c(sd(current.data$diff),sd(-log10(current.data$qval))),
centre=c(mean(current.data$diff),mean(-log10(current.data$qval)))))
current.df$group <- g
df.ell <- rbind(df.ell, current.df)
}
print(ggplot(to.plot,aes(diff,-log10(qval))) +
facet_wrap(~group,ncol=3) + #stat_ellipse() +
geom_point() +
geom_text(aes(label=gene),size=3,hjust=-0.2) +
#geom_contour(data=contour.data,aes(x,y,z=z),bins=3) +
geom_path(data=df.ell, aes(x=x, y=y), colour='blue') +
geom_vline(aes(xintercept=0),colour='gray',linetype='dashed') +
geom_hline(aes(yintercept=-log10(0.05)),colour='red',linetype='dashed') +
xlim(-1,4) + ylim(-1,5)
)
}
dev.off()
df
}
#df <- plot.tf.pval()
#idx <- match(goi, ctrl$Gene)
#cat <- genes[!is.na(idx)]
#idx <- na.omit(idx)
idx <- 1:nrow(ctrl)
core <- cbind(ctrl[idx,-1], treated[idx,-1])
pval <- apply(core,1,lrtest)
qval <- p.adjust(pval, 'BH')
diff.mean <- rowMeans(diff[idx,-1])
df <- data.frame(qval=qval,gene=ctrl$Gene[idx],diff=diff.mean)
print(ggplot(df,aes(diff.mean,-log10(qval))) +
geom_point() +
geom_text(aes(label=gene),size=3,hjust=-0.2) +
geom_vline(aes(xintercept=0),colour='gray',linetype='dashed',size=1) +
geom_hline(aes(yintercept=-log10(0.05)),colour='red',linetype='dashed',size=1) +
#xlim(-1,3) + #ylim(-1,5)
labs(x=expression(paste('Mean'~log[2]~'fold change')),
y=expression(paste(-log[10]~italic(q),'-value')),
title='TF differential expression over time (fibroblast)\n')
)
@
%\end{center}
%\caption{Time course of 4 different groups of cytokines. The average
%time-resolved expression profiles of 4 different groups of cytokines
%are plotted for control and the NHK conditioned medium case. Gray area
%indicates one times standard deviation.}
%\caption{}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
<<label=tf_seq,echo=False,fig=False,eval=True>>=
tf.sig <- as.vector(unique(df$gene[df$diff>0&df$qval<0.01]))
i <- which(tf.sig=='BHLHB5')
tf.sig[i] <- 'BHLHE22'
i <- which(tf.sig=='CSEN')
tf.sig[i] <- 'KCNIP3'
i <- which(tf.sig=='HOP')
tf.sig[i] <- 'HOPX'
i <- which(tf.sig=='ISGF3G')
tf.sig[i] <- 'IRF9'
i <- which(tf.sig=='SDCCAG33')
tf.sig[i] <- 'TSHZ1'
human.promoter <- read.DNAStringSet('~/data/fimo/human_upstream1000.fa')
tf.entrez <- unlist(mget(as.character(tf.sig), org.Hs.egSYMBOL2EG,
ifnotfound=NA))
tf.entrez <- tf.entrez[!is.na(tf.entrez)]
tf.refseq <- unlist(mget(tf.entrez, org.Hs.egREFSEQ))
idx <- grep(paste(tf.refseq,collapse='|'), names(human.promoter))
refseq2symbol <- function(refseq.str) {
all.str <- unlist(strsplit(refseq.str, '_'))
refseq <- paste(all.str[1:2], collapse='_')
#print(refseq)
entrez <- unlist(mget(refseq,org.Hs.egREFSEQ2EG,ifnotfound=NA))
if (is.na(entrez))
return('')
else
return(get(entrez, org.Hs.egSYMBOL))
}
for (i in 1:length(idx)) {
symbol <- refseq2symbol(names(human.promoter)[idx[i]])
if (symbol != '') {
seq.file <- paste('~/data/hdf/sig_seq/',symbol,'.fa',sep='')
write.XStringSet(human.promoter[idx[i]], seq.file)
}
}
@
% ----------------------------------------------------------------------------
<<label=tf_ts,echo=False,fig=True,eval=False>>=
#idx <- na.omit(match(tf$Gene_Abbrev,treated$Gene))
##stop()
#core <- cbind(ctrl[idx,-1], treated[idx,-1])
#pval <- apply(core,1,lrtest)
#qval <- p.adjust(pval, 'BH')
##diff <- rowMeans(diff[idx,-1])
##df <- data.frame(qval=qval,gene=ctrl$Gene[idx],diff=diff)
#tf.ts <- treated[idx,]
##treated <- list(dat=tf.ts[qval<0.05,], cond='NHK', tpoint=treated.tpoint)
##df <- plot.impulse(treated, '~/data/hdf/hdf-tf_impulse.pdf')
#df <- tf.ts[qval<0.05,]
#mat <- matrix(0,nrow(df),nrow(df))
#for (i in 1:nrow(df)) {
# for (j in i:nrow(df)) {
# #mat[i,j] <- dtw(df[i,-1], df[j,-1])$normalizedDistance
# mat[i,j] <- dtw(df[i,-1], df[j,-1])$distance
# mat[j,i] <- mat[i,j]
# }
#}
#hc <- hclust(as.dist(mat))
#df$cluster <- as.character(cutree(hc, 3))
#cl <- kmeans(df[,-1], 3, nstart=100)
#df$cluster <- factor(cl$cluster)
#
#mdf <- melt(df, id.vars=c('Gene','cluster'))
#mdf$variable <- rep(treated.tpoint, each=nrow(df))
#
#print(ggplot(mdf,aes(variable,value,group=Gene,colour=cluster)) + geom_point() +
# geom_line() + scale_colour_brewer(palette='Set1') +
# xlab('Time (h)') + ylab(expression(paste(log[2]~'fold expression'))) +
# opts(title='TF expression (NHK-conditioned medium)\n',
# legend.position='none')
#)
#print(ggplot(df,aes(diff,-log10(qval))) +
# geom_point() +
# geom_text(aes(label=gene),size=3,hjust=-0.2,alpha=0.3) +
# #geom_contour(data=contour.data,aes(x,y,z=z),bins=3) +
# #geom_path(data=df.ell, aes(x=x, y=y), colour='blue') +
# geom_vline(aes(xintercept=0),colour='gray',linetype='dashed',size=1) +
# #geom_hline(aes(yintercept=-log10(0.05)),colour='red',linetype='dashed',size=1) +
# xlim(-3,3) + #ylim(-1,5) +
# xlab(expression(paste('Average'~log[2]~'fold change (NHK - control)'))) +
# ylab(expression(paste(-log[10]~italic(q),'-value'))) +
# opts(title='TF differential expression over time\n')
#)
tf.sig <- unique(df$gene[df$diff>0&df$qval<0.05])
treated <- list(dat=treated[match(tf.sig,treated$Gene),], cond='NHK',
tpoint=treated.tpoint)
df <- plot.logistic(treated, '~/data/hdf/hdf-tf_logistic.pdf')
idx.treated <- which(names(treated)=='Gene')
fit.treated <- apply(treated[match(tf.sig,treated$Gene),-idx.treated],1,
function(x) fit.logistic(treated.tpoint,x))
nfit <- 100
xfit <- seq(0,max(treated.tpoint),length=nfit)
uptime <- apply(fit.treated,2,function(x)xfit[which.max(abs(diff(x)/diff(xfit)))])
df <- as.data.frame(uptime)
#res <- hist(uptime, plot=F)
#df <- data.frame(x=res$mids, y=res$counts, type='q-value')
print(ggplot(df,aes(uptime)) + geom_histogram() +
#geom_bar(stat='identity',fill='gray',colour='black') +
#geom_segment(aes(x=uptime,y=0,xend=uptime,yend=0.05),alpha=0.3) +
#scale_colour_brewer(palette='Set1') +
#geom_text(data=df[df$TNFR<df$CXCR4,],aes(label=TF),alpha=0.3) +
#xlim(0,6) + ylim(0,6) +
labs(x='Upregulation time (h)',y='Count',
title='Temporal activation of TFs (fibroblast)\n')
)
@
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=pval_cytokine,echo=False,fig=True,width=8,height=5>>=
%df.cytokine <- plot.pval(goi, genes)
%@
%\end{center}
%%\caption{Time course of 4 different groups of cytokine receptors. The average
%%time-resolved expression profiles of 4 different groups of cytokine receptors
%%are plotted for control and the NHK conditioned medium case. Gray area
%%indicates one times standard deviation.}
%\caption{}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=ts_receptor,echo=False,fig=True,width=8,height=5>>=
%goi <- c('^IL[0-9]+R','^TNFR','^CXCR','^CCR')
%genes <- c('IL-Rs','TNFRs','CXCRs','CCRs')
%plot.ts(goi, genes)
%@
%\end{center}
%\caption{Time course of 4 different groups of cytokine receptors. The average
%time-resolved expression profiles of 4 different groups of cytokine receptors
%are plotted for control and the NHK conditioned medium case. Gray area
%indicates one times standard deviation.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=pval_receptor,echo=False,fig=True,width=8,height=5>>=
%df.receptor <- plot.pval(goi, genes)
%@
%\end{center}
%\caption{Control and NHK-conditioned time series were fitted with a
%full/reduced cubic polynomial model. Analysis of deviance of the model fit
%was then performed to judge whether the control and NHK-conditioned time
%series can be explained by a single fit. The higher the negative
%log-transformed $p\mhyphen$value, the more significant the analysis of
%deviance test is and thus the more likely that NHK-conditioned expression
%profile deviates from the control.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=fitstat,echo=False,fig=True>>=
%library(qvalue)
%pval <- melt(apply(treated[,-1],1,gof))
%qval <- p.adjust(pval$value, 'BH')
%qres <- hist(-log10(qval), plot=F)
%#pres <- hist(-log10(qval$pvalues), plot=F)
%df <- data.frame(x=qres$mids, y=qres$counts, type='q-value')
%#df <- rbind(df, data.frame(x=qres$mids, y=qres$counts, type='q-value'))
%#df <- data.frame(p=qval$pvalues, q=p.adjust(qval$pvalues,'BH'))
%
%print(ggplot(df,aes(x,y)) +
% #geom_point()
% geom_bar(stat='identity',fill='gray',colour='black') +
% #facet_wrap(~type)
% #scale_x_log10() +
% xlab('p-value') + ylab('count')
%)
%@
%\end{center}
%\caption{Histogram of the $-\log_{10}$ transformed and multiple testing
%corrected (Benjamini-Hochberg) $p\mhyphen$values according
%to a $\chi^2$ goodness-of-fit test of the logistic fit of the time series.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%<<label=tf_cytokine,echo=False,fig=True>>=
%ligand <- read.delim('~/data/hdf/hdf_nhk_ligand_fit.dat', header=F)$V1
%tf <- tfbs$common[order(truncated)]
%
%pdf('~/data/hdf/hdf-tf_cytokine.pdf',8,11)
%for (i in 1:2) {
% slice <- seq((i-1)*12+1,i*12)
% df <- NULL
% for (i in 1:length(slice)) {
% expr <- diff[,2+slice[i]]
% gene.ranked <- diff$Gene[order(expr,decreasing=T)]
% for (j in 1:length(tf)) {
% target <- eval(parse(text=paste("mSigDB$'",tf[j],"'",sep="")))
% df <- rbind(df, data.frame(TF=tf[j],rank=match(target,gene.ranked),
% tpoint=paste(slice[i],'h')))
% }
% }
%
% print(ggplot(df,aes(TF,rank)) +
% geom_point(shape='-') + facet_wrap(~tpoint,ncol=3) +
% #xlab('half-maximal time (h)') + #xlim(0,30) +
% #ylab('count') +
% ylim(0,200) +
% opts(title='Logistic fitting gene expression time course of HDF\n')
% )
%}
%dev.off()
%@
% ----------------------------------------------------------------------------
<<label=cytokine,echo=False,fig=False,cache=True,eval=False>>=
ligand <- read.delim('~/data/hdf/hdf_nhk_ligand_fit.dat', header=F)
receptor <- read.delim('~/data/hdf/hdf_nhk_receptor_fit.dat', header=F)
df.cytokine <- transform(ligand, type=rep('ligand',nrow(ligand)))
df.cytokine <- rbind(df.cytokine, transform(receptor, type=rep('receptor',nrow(receptor))))
g <- rep('Other',nrow(df.cytokine))
groups <- c('IL/R','TNF/R','CXCL/R','CCL/R')
group.ligand <- c('^IL[0-9]+[^R]*','TNF[^R]','^CXCL','^CCL')
for (i in 1:length(group.ligand)) {
idx <- grep(group.ligand[i], df.cytokine$V1)
g[idx] <- groups[i]
}
group.receptor <- c('^IL[0-9]+R','^TNFR','^CXCR','^CCR')
for (i in 1:length(group.receptor)) {
idx <- grep(group.receptor[i], df.cytokine$V1)
g[idx] <- groups[i]
}
df.cytokine$group <- g
df.cytokine$qval <- qval[match(df.cytokine$V1,ctrl$Gene)]
df.cytokine <- within(df.cytokine, group <- factor(group, levels = c(groups,'Other')))
with(df.cytokine, levels(group))
@
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=tf_cytokine,echo=False,fig=True,width=8,height=11>>=
%tf <- tfbs$common[order(truncated)]
%regulator <- tapply(unlist(mSigDB),unlist(mSigDB),function(x){names(x)})
%df <- NULL
%for (i in 1:nrow(df.cytokine)) {
% if (df.cytokine$V1[i] %in% names(regulator)) {
% reg <- eval(parse(text=paste('regulator$',df.cytokine$V1[i],sep='')))
% for (j in 1:length(reg)) {
% df <- rbind(df, transform(df.cytokine[i,],TF=reg[j]))
% }
% }
%}
%tf.truncated <- sub('.*V\\$','',df$TF)
%df <- within(df, TF <- factor(TF, levels = df$TF[order(tf.truncated)]))
%with(df, levels(TF))
%
%print(ggplot(df[df$V4<0.05&df$qval<0.05&df$type=='ligand',],aes(V2,TF)) +
% geom_point(aes(colour=group),size=1) +
% geom_text(aes(label=V1),size=1,hjust=-0.2,alpha=0.3) +
% scale_colour_brewer('',palette='Set1') +
% guides(colour = guide_legend(ncol = 2)) +
% xlab('starting time') + #xlim(0,12) +
% ylab('') +
% opts(title='Logistic fitting gene expression time course of HDF\n',
% legend.position='none',
% #legend.position=c(0.5,0.9),legend.key=theme_blank(),
% #legend.text=theme_text(size=8),
% #legend.background = theme_rect(),
% axis.text.y=theme_text(size=2))
%)
%@
%\end{center}
%\caption{Histogram of the $-\log_{10}$ transformed and multiple testing
%corrected (Benjamini-Hochberg) $p\mhyphen$values according
%to a $\chi^2$ goodness-of-fit test of the logistic fit of the time series.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=mds,echo=False,fig=True>>=
%dat <- read.delim('~/data/hdf/hdf_nhk_16091.tsv')
%coord <- read.delim('~/data/hdf/mds/pca_hdf_nhk_16091_normed_euc', header=F, sep=' ')
%coord <- coord[,1:2]
%df <- as.data.frame(coord)
%df$pvals <- read.delim('~/data/hdf/mds/pval_hdf_nhk_16091.dat', header=F)$V1
%df$label <- dat$Gene
%
%mycolor <- gray.colors(128,0.9,0)
%print(ggplot(df, aes(x=V1,y=V2)) +
% geom_point(aes(colour=-log10(pvals))) +
% geom_text(data=df[df$pvals<0.001,],aes(x=V1,y=V2,label=label),colour='black',size=2,
% hjust=-0.2,alpha=0.3) +
% scale_colour_gradientn(colours=mycolor) +
% guides(colour = guide_colorbar(barwidth = 0.5, barheight = 10, title.hjust=1.6,
% title.vjust=0.1,title=expression(paste(-log[10],' ',italic(p),'-value')))) +
% xlab("PC 1") + ylab("PC 2") + #ylim(-0.5,0.5) +
% coord_equal() +
% opts(legend.title=theme_text(angle=90),title="MDS")
%)
%@
%\end{center}
%\caption{Histogram of the $-\log_{10}$ transformed and multiple testing
%corrected (Benjamini-Hochberg) $p\mhyphen$values according
%to a $\chi^2$ goodness-of-fit test of the logistic fit of the time series.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=thalf,echo=False,fig=True,width=8,height=8>>=
%df.fit <- as.data.frame(apply(treated[,-1],1,fit))
%thalf <- apply(df.fit,2,function(x)xfit[which.max(abs(diff(x)/diff(xfit)))])
%#thalf <- melt(thalf[pval$value<0.01])
%df <- data.frame(pval=qval, thalf=thalf, max=as.numeric(t(df.fit[nrow(df.fit),])),
% gene=symbol)
%
%print(ggplot(df,aes(thalf,max)) +
% geom_point(aes(colour=-log10(pval))) +
% geom_text(aes(label=gene),size=3,hjust=-0.2,alpha=0.3) + #facet_grid(~type) +
% scale_colour_gradient(expression(paste(-log[10],' p-value')),
% low='skyblue',high='black') +
% guides(colour = guide_colorbar(barwidth = 10, barheight = 0.5)) +
% geom_hline(aes(yintercept=0),linetype='dashed') +
% xlab('half-maximal time (h)') + xlim(0,30) +
% ylab(expression(paste('maximal ',log[2],' fold expression'))) +
% opts(title='Logistic fitting gene expression time course of HDF\n',
% legend.position='top')
%)
%@
%\end{center}
%\caption{Maximal expression vs. half-maximal time for all genes.
%Microarray time series of HDF under NHK conditioned medium were first
%fitted with a logistic function, starting time is defined as the half-maximal
%time of the fit and maximal fold expression is the plateau/steady state of
%the fit. Color code indicates the $-\log_{10} p\mhyphen$values for each gene
%according to a $\chi^2$ test.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=start,echo=False,fig=True,width=8,height=5>>=
%ligand <- read.delim('~/data/hdf/hdf_nhk_ligand_fit.dat', header=F)
%receptor <- read.delim('~/data/hdf/hdf_nhk_receptor_fit.dat', header=F)
%df <- transform(ligand, type=rep('ligand',nrow(ligand)))
%df <- rbind(df, transform(receptor, type=rep('receptor',nrow(receptor))))
%g <- rep('Other',nrow(df))
%groups <- c('IL/R','TNF/R','CXCL/R','CCL/R')
%group.ligand <- c('^IL[0-9]+[^R]*','TNF[^R]','^CXCL','^CCL')
%for (i in 1:length(group.ligand)) {
% idx <- grep(group.ligand[i], df$V1)
% g[idx] <- groups[i]
%}