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h838.Rnw
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% $Id: h838.Rnw 337 2012-09-20 14:20:21Z 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}}
\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{H838/HPAEC Cytokine/Transcriptome Data Analysis}
\author{Jie Bao}
\begin{document}
\maketitle
<<label=base,echo=False,fig=FALSE>>=
library(ggplot2)
library(reshape)
library(genefilter)
library(preprocessCore)
library(marray)
library(gplots)
library(plotrix)
library(org.Hs.eg.db)
library(gage)
library(hdf5)
library(igraph)
library(doMC)
library(plyr)
library(pracma)
library(pheatmap)
library(ggdendro)
library(RColorBrewer)
library(GOstats)
library(devtools)
load_all('~/tool/util')
fontsize <- 15
theme_set(theme_bw(fontsize))
registerDoMC(4)
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=data,echo=False,fig=FALSE,cache=True,eval=True>>=
wd <- '~/data/hpaec/'
dat <- read.table(paste(wd,"all.qnorm.txt",sep=""),sep="\t",header=T,
quote="\"",comment.char="")
#dat.ctrl <- read.delim(paste(wd,'h838_ctrl_20714.dat',sep=''),sep='|')
#dat.treated <- read.delim(paste(wd,'h838_treated_20714.dat',sep=''),sep='|')
#names(dat.ctrl) <- c('Gene','0','0.5','1','2','3','4','5','10','15','24')
#names(dat.treated) <- names(dat.ctrl)
#cytokine <- c('CXCL1','ICAM1','IL2RA','CCL7','MIF','CXCL12','IL6','IL7','IL8',
# 'IL9','IL10','IL12A','IL12B','IL13','IL15','FGF2','CSF3','CSF2','IFNG',
# 'CXCR3','CCL2','PDGFB','CCL5','TNF','VEGFA')
#cytokine.go <- read.delim(paste(wd,'cytokine.go',sep=''), header=F)$V1
col <- c('H838_HMEC_0h_unscratched_5617835005_A.mean',
'H838_Mono_0h_unscratched_5617835005_K.mean',
'H838_HMEC_0h_unscratched_2_5617835033_I.mean',
'H838_Mono_0h_unscratched_2_5617835033_K.mean')
# filter microarray by IQR
mat <- dat[,grep('mean',names(dat))]
all.iqr <- apply(mat,1,IQR,na.rm=T)
dat <- dat[order(all.iqr,decreasing=T),]
cutoff <- sum(all.iqr>3)
#dat <- dat[1:cutoff,]
dat <- dat[!duplicated(dat$Entrez_Gene_ID),]
dat <- dat[!is.na(dat$Entrez_Gene_ID),]
all.basal <- dat[,match(col,names(dat))]
rownames(all.basal) <- dat$Entrez_Gene_ID
@
% ----------------------------------------------------------------------------
<<label=limma,echo=False,fig=True,eval=False>>=
library(limma)
design <- model.matrix(~ 0+factor(c(2,1,2,1)))
colnames(design) <- c('homo','hetero')
rowind <- which(all.basal==0, arr.ind=T)[1]
all.basal <- all.basal[-rowind,]
fit <- lmFit(log2(all.basal), design)
contrast.matrix <- makeContrasts(hetero-homo, levels=design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
res.limma <- topTable(fit2, coef=1, adjust="BH", number=nrow(all.basal))
res.limma$symbol <- unlist(mget(res.limma$ID, org.Hs.egSYMBOL, ifnotfound=NA))
res <- hist(-log10(res.limma$adj.P.Val), plot=F)
df <- data.frame(x=res$mids, y=res$counts, type='q-value')
print(ggplot(df,aes(x,y)) +
geom_bar(stat='identity',fill='gray',colour='black') +
labs(x=expression(paste(-log[10]~italic(q),'-value')),
y='Count',title='Basal differential expression of H838 genes\n')
)
#print(ggplot(res.limma,aes(logFC,-log10(adj.P.Val))) +
# geom_point() +
# #geom_text(data=res.limma[res.limma$adj.P.Val<0.05,],aes(label=symbol),size=3,
# # vjust=-0.2) +
# geom_vline(aes(xintercept=0),colour='blue',linetype='dashed') +
# geom_hline(aes(yintercept=-log10(0.05)),colour='red',linetype='dashed') +
# xlab(expression(paste('Estimated ',log[2],' fold change',
# '(heterogeneous - homogeneous)'))) +
# xlim(-1,1) + ylim(0,2.6) +
# ylab(expression(paste(-log[10],' ',italic(q),'-value'))) +
# opts(title='Linear model fit of basal expression\n')
#)
@
% ----------------------------------------------------------------------------
<<label=gage,echo=False,fig=False,cache=True,eval=True>>=
# GAGE
library(gage)
library(gageData)
data(go.gs)
data(kegg.gs)
library(xtable)
mSigDB <- readLines("~/data/MSigDB/c2.all.v3.0.entrez.gmt")
mSigDB <- strsplit(mSigDB, "\t")
names(mSigDB) <- sapply(mSigDB, function(x) x[1])
mSigDB <- sapply(mSigDB, function(x) x[3:length(x)])
#test with direction: either up or down
#res <- gage(log2(all.basal), gsets = mSigDB, ref = c(2,4), samp = c(1,3),
# compare='as.group', saaTest=gs.tTest, same.dir=T)
#df <- as.data.frame(res$greater)
#df <- subset(df[,c('p.val','set.size')], p.val<0.01)
#tbl <- xtable(df, caption='Significantly ($p < 0.01$) upregulated gene sets in heterogeneous coculture compared to homogeneous coculture.', display=c('s','e','d'))
#print(tbl, tabular.environment = "longtable",
# add.to.row=list(as.list(seq(1,nrow(df),2)),rep("\\rowcolor{Gray} ",
# length(seq(1,nrow(df),2)))), include.rownames=T,
# file='~/code/svn/Publications/branches/h838/table_gage_up.tex',
# caption.placement='top')
#df <- as.data.frame(res$less)
#df <- subset(df[,c('p.val','set.size')], p.val<0.01)
#tbl <- xtable(df, caption='Significantly ($p < 0.01$) downregulated gene sets in heterogeneous coculture compared to homogeneous coculture.', display=c('s','e','d'))
#print(tbl, tabular.environment = "longtable",
# add.to.row=list(as.list(seq(1,nrow(df),2)),rep("\\rowcolor{Gray} ",
# length(seq(1,nrow(df),2)))), include.rownames=T,
# file='~/code/svn/Publications/branches/h838/table_gage_down.tex',
# caption.placement='top')
@
% ----------------------------------------------------------------------------
<<label=colorbar,echo=False,fig=True,eval=False>>=
mycolor <- colorRampPalette(c('white','#8EC13A'))
# Generate data
pp <- function (n,r=4) {
x <- seq(-r*pi, r*pi, len=n)
df <- expand.grid(x=x, y=x)
df$r <- sqrt(df$x^2 + df$y^2)
df$z <- cos(df$r^2)*exp(-df$r/6)
df
}
print(ggplot(pp(20), aes(x=x,y=y)) +
geom_tile(aes(fill=z)) +
scale_fill_gradientn(colours = mycolor(128)) +
#values = values, rescaler = function(x, ...) x, oob = identity,
#limits=c(-max_val,max_val)) +
guides(fill = guide_colorbar(barwidth = 10, barheight = 1,
title.hjust=1.6, title.vjust=0.4,
#title.position='right',
title=expression(paste(log[2]~'fold expression')))) +
labs(x = "",y = "") + scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
theme(axis.ticks = element_blank(),axis.text.x=element_text(angle=90),
panel.border=element_blank(),legend.title=element_text(angle=90),
axis.text.y=element_blank(),legend.position='top')
)
@
% ----------------------------------------------------------------------------
<<label=circle,echo=False,fig=True,eval=False>>=
circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){
r = diameter / 2
tt <- seq(0,2*pi,length.out = npoints)
xx <- center[1] + r * cos(tt)
yy <- center[2] + r * sin(tt)
return(data.frame(x = xx, y = yy))
}
dat1 <- circleFun(c(0,1),2,npoints = 100)
dat2 <- circleFun(c(0,2),4,npoints = 100)
dat3 <- circleFun(c(0,3),6,npoints = 100)
dat4 <- circleFun(c(0,4),8,npoints = 100)
dat5 <- circleFun(c(0,5),10,npoints = 100)
#geom_path will do open circles, geom_polygon will do filled circles
print(ggplot(dat1,aes(x,y)) + geom_path() +
geom_path(data=dat2,aes(x,y)) +
geom_path(data=dat3,aes(x,y)) +
geom_path(data=dat4,aes(x,y)) +
geom_path(data=dat5,aes(x,y)) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
)
@
% ----------------------------------------------------------------------------
<<label=unscratched,echo=False,fig=True,eval=True>>=
cond <- 'Mono'
tpoint.label <- c('30min','1h','2h','3h','4h','5h','10h','15h','24h')
wd <- '/home/jbao/data/hpaec/gage_kegg/'
write.dot <- function(cond, tpoint.label) {
mat <- dat[,grep('mean',names(dat))]
ref.idx <- grep(paste(cond,'0h',sep='_'), names(mat))
samp.idx <- grep(paste(cond,tpoint.label,sep='_'), names(mat))
#test with direction: either up or down
geneset <- kegg.gs
res <- gage(mat, gsets = geneset, ref = ref.idx, samp = samp.idx,
compare='as.group', saaTest=gs.tTest, same.dir=F)
df <- as.data.frame(res$greater)
df <- subset(df[,c('p.val','set.size')], p.val<0.5)
adj <- foreach(i=1:nrow(df), .combine='rbind') %:%
foreach(j=1:nrow(df), .combine='c') %dopar% {
overlap <- length(intersect(geneset[[rownames(df)[i]]],
geneset[[rownames(df)[j]]]))
len.i <- length(geneset[[rownames(df)[i]]])
len.j <- length(geneset[[rownames(df)[j]]])
#if (overlap > len.i*0.25 & overlap > len.j*0.25)
if (overlap > 50)
res <- 1
else
res <- 0
res
}
set.size <- df$set.size
diag(adj) <- 0
# build a graph out of it
term.net <- graph.adjacency(adj, mode="undirected", diag=F)
#if (largest==T) {
# #pick the largest sub graph if largest is TRUE
# c=clusters(groups.net)
# groups.net=subgraph(groups.net,names(mygroups)[which(c$membership==(which.max(c$csize)-1))])
#}
mycolor <- colorRampPalette(c('white','#8EC13A'))
pval.trans <- round(-log10(df$p.val)*100)
pval.trans <- pval.trans - min(pval.trans) + 1
cols <- mycolor(max(pval.trans))[pval.trans]
# make the labels
#V(term.net)$label <- rownames(df)
#V(term.net)$label <- sub('GO:[0-9]+\\s','',rownames(df))
V(term.net)$label <- sub('hsa[0-9]+\\s','',rownames(df))
V(term.net)$width <- sqrt(set.size)*0.1
V(term.net)$shape <- 'circle'
V(term.net)$fixedsize <- 'true'
V(term.net)$style <- 'filled'
#V(term.net)$fillcolor <- "#8EC13A"
V(term.net)$fillcolor <- cols
V(term.net)$fontsize <- sqrt(set.size)
V(term.net)$fontname <- 'Helvetica'
term.net$overlap <- 'scalexy'
term.net$sep <- '+20'
term.net$outputorder <- 'edgefirst'
term.net$splines <- 'true'
term.net$pad <- 2
write.graph(term.net, paste(wd,'h838_',cond,'_',tpoint.label,'_gage_kegg.dot',sep=''), 'dot')
}
for (i in 1:length(tpoint.label)) {
write.dot(cond, tpoint.label[i])
}
for (i in 1:length(tpoint.label)) {
system(paste('neato ',wd,'h838_',cond,'_',tpoint.label[i],'_gage_kegg.dot -Teps -o ',wd,'h838_',cond,'_',tpoint.label[i],'_gage_kegg.eps',sep=''))
}
# create a colour palette depending on the score
# cols=designer.colors(col=c("yellow", "orange", "red"), n=max(scores) )[scores]
#plot(term.net, layout=layout.auto,
# vertex.size=set.size*0.05,
# ,vertex.color=cols
# vertex.label=labels,
# ,vertex.label.cex=1
# vertex.label.family="sans",
# vertex.label.font=2,
# vertex.label.color=1
#)
@
% ----------------------------------------------------------------------------
<<label=enrichment,echo=False,fig=True,cache=True,eval=False>>=
#mSigDB <- readLines("~/work/MSigDB/c2.all.v3.0.symbols.gmt")
mSigDB <- readLines("~/MicroarrayAnalysis/trunk/r/c3.tft.v3.0.symbols.gmt")
mSigDB <- strsplit(mSigDB, "\t")
names(mSigDB) <- sapply(mSigDB, function(x) x[1])
mSigDB <- sapply(mSigDB, function(x) x[3:length(x)])
tpoint <- c(0.5,1:5,10,15,24)
ctrl <- read.delim('~/data/hpaec/h838_ctrl_14922.dat', sep='|')
treated <- read.delim('~/data/hpaec/h838_treated_14922.dat', sep='|')
diff <- read.delim('~/data/hpaec/h838_diff_14922.dat', sep='|')
all <- NULL
threshold <- 0.9465
for (i in 1:length(tpoint)) {
glist <- data.frame(ctrl=ctrl[,i+2], treated=treated[,i+2], row.names=diff$Gene)
#test with direction: either up or down
res <- gage(glist, gsets = mSigDB, ref = 1, samp = 2,
compare='paired', saaTest=gs.tTest, same.dir=T)
current <- as.data.frame(res$greater)
current <- current[!is.na(current$q.val),]
current <- subset(current, p.val<0.05)
current$time <- tpoint[i]
all <- rbind(all, current)
#idx <- current$q.val < threshold
#idx <- 1:nrow(current)
#if (i == 1)
# common <- rownames(current)[idx]
#else
# common <- intersect(common, rownames(current)[idx])
}
#final <- matrix(0,length(common),length(tpoint))
#for (j in 1:length(tpoint)) {
# for (i in 1:length(common)) {
# final[i,j] = all.path[[j]]$q.val[rownames(all.path[[j]])==common[i]]
# }
#}
#list(common=common, final=final)
all$label <- rownames(all)
all <- all[-grep('UNKNOWN',all$label),]
print(ggplot(all,aes(stat.mean,-log10(p.val))) +
geom_point(aes(size=set.size)) +
facet_wrap(~time) +
geom_text(aes(label=label),size=2,hjust=-0.1,alpha=0.5) +
#geom_vline(aes(xintercept=0),colour='blue',linetype='dashed') +
#geom_hline(aes(yintercept=-log10(0.05)),colour='red',linetype='dashed') +
#xlab(expression(paste('Average ',log[2],' fold change (heterogeneous - homogeneous)'))) +
xlim(1.5,4) #+ ylim(0,2.6) +
#ylab(expression(paste(-log[10],' ',italic(q),'-value'))) +
#opts(title='Full/reduced model fitting of\nH838 time course')
)
@
% ----------------------------------------------------------------------------
<<label=hppi,echo=False,fig=False,cache=True,eval=False>>=
mat <- hdf5load('/home/jbao/data/network/hppi/hppi2-directed-MFPT.hdf5', load=F)
map <- hdf5load('/home/jbao/data/network/hppi/hppi2_directed_map.hdf5', load=F)
net <- read.graph('/home/jbao/data/network/hppi/hppi2_directed.xml', 'graphml')
@
% ----------------------------------------------------------------------------
<<label=hippie,echo=False,fig=False,cache=True,eval=True>>=
mat <- hdf5load('/home/jbao/data/network/hippie/HIPPIE-current_cutoff0-MFPT.hdf5', load=F)
map <- hdf5load('/home/jbao/data/network/hippie/HIPPIE-current_cutoff0_map.hdf5', load=F)
net <- read.graph('/home/jbao/data/network/hippie/HIPPIE-current_cutoff0.xml', 'graphml')
@
% ----------------------------------------------------------------------------
<<label=tfbs_sig,echo=False,fig=False,eval=False>>=
tf.sig <- list(t2=c('JUN','FOS','SRY','NKX3-1','GATA6','NFE2','GATA1','SOX9',
'STAT4','NFY','EVI1','MYB$','^DBP'),
t3=c('POU3F2','PPARG$'),
t4=c('TP53$','PAX8','GATA1','LHX3','IRF1','PAX5','CEBPG','YY1$','ZIC1',
'IRF2','JUN','FOS','NFY'),
t5=c('SOX9','IRF1','E2F','YY1$','ELK1'),
t6=c('PAX3','YY1$','SRY','HSF1','E4F1','MYC$','MAX','HLF','ARNT$','GABP',
'E2F'),
t7=c('GABP'),
t8=c('E2F','IRF2','PAX3'),
t9=c('YY1$'),
t10=c('^NF1','IRF1'))
tf.entrez <- NULL
tpoint <- c(0.5,1:5,10,15,24)
for (i in 1:length(tf.sig)) {
tf <- unlist(sapply(tf.sig[[i]], function(x) map$idmap$official[grep(x,map$idmap$official)]))
tf.entrez <- rbind(tf.entrez, data.frame(tf.entrez=map$idmap$entrez[match(tf,map$idmap$official)],time=tpoint[i],tf.symbol=as.vector(tf)))
}
receptor <- read.delim('~/MicroarrayAnalysis/trunk/python/hsa04060.receptor',
header=F)$V1
map$idmap$official[map$idmap$official=='IL8RA'] <- 'CXCR1'
#receptor.entrez <- unlist(mget(as.character(receptor), org.Hs.egSYMBOL2EG,
# ifnotfound=NA))
receptor <- receptor[receptor %in% map$idmap$official]
receptor.entrez <- map$idmap$entrez[match(receptor,map$idmap$official)]
tf.all <- tf.entrez
tf.entrez <- tf.entrez[tf.entrez$tf.entrez %in% 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 1:1) {
# for (j in 1:1) {
ii <- map$idmap$vertex[which(receptor.entrez[i] == map$idmap$entrez)] + 1
jj <- map$idmap$vertex[which(tf.entrez$tf.entrez[j] == map$idmap$entrez)] + 1
receptor.idx <- match(receptor.entrez[i], V(net)$gene)
tf.idx <- match(tf.entrez$tf.entrez[j], V(net)$gene)
sp <- shortest.paths(net, receptor.idx, tf.idx, weights=NA, mode='out') # BFS
tmp <- data.frame(mfpt=mat$mfpt[ii,jj], sp=as.numeric(sp),
#receptor=get(receptor.entrez[i], org.Hs.egSYMBOL),
receptor=map$idmap$official[which(receptor.entrez[i]==map$idmap$entrez)],
tf=map$idmap$official[which(tf.entrez$tf.entrez[j]==map$idmap$entrez)],
time=tf.entrez$time[j])
#mfpt.sp <- rbind(mfpt.sp, tmp)
#}
}
@
% ----------------------------------------------------------------------------
<<label=tf_heatmap,echo=False,fig=True,height=8,eval=False>>=
tf.all$presence <- 1
final <- reshape(tf.all[,c('tf.symbol','time','presence')], v.names='presence',
idvar='tf.symbol', timevar='time', direction='wide')
final.m <- melt(final, id.vars='tf.symbol')
final.m$variable <- paste(rep(tpoint, each=nrow(final)),'h')
#final.m[is.na(final.m)] <- 0
final.m <- within(final.m, variable <- factor(variable, levels = paste(tpoint,'h')))
final.m <- within(final.m, tf.symbol <- factor(tf.symbol, levels = sort(as.character(tf.symbol))))
mycolor <- colorRampPalette(c('grey30','black'))
print(ggplot(final.m,aes(variable,tf.symbol)) +
geom_tile(aes(fill=as.character(value))) +
scale_fill_manual(values='black',na.value='grey80') +
#guides(fill = guide_colorbar(barwidth = 0.5, barheight = 10,
# title.hjust=1.6, title.vjust=0.4,
# #title.position='right',
# title=expression(paste(log[2]~'fold expression')))) +
labs(x = "",y = "",title='Enriched TFs in H838+HPAEC\n') +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
theme(axis.ticks = element_blank(),axis.text.x=element_text(angle=90),
panel.border=element_blank(),legend.position='none',
axis.text.y=element_text(size=8))
)
@
% ----------------------------------------------------------------------------
<<label=top_mfpt,echo=False,fig=True,width=8,height=8,eval=True>>=
#mfpt.sp <- mfpt.sp[-which(mfpt.sp$mfpt==-1),]
#df <- NULL
#for (t in levels(factor(mfpt.sp$time))) {
# current <- subset(mfpt.sp, time==t)
# res <- ddply(current, .(receptor), summarise, mean.mfpt=mean(mfpt),
# mean.sp=mean(sp))
# res$time <- t
# df <- rbind(df, res)
#}
#group <- sub('IL1R.+','IL1R',df$receptor)
#group <- sub('TNFR.+','TNFR',group)
#df$group <- group
#df2 <- NULL
#for (t in levels(factor(df$time))) {
# current <- subset(df, time==t)
# res <- ddply(current, .(group), summarise, mean.mfpt=mean(mean.mfpt),
# mean.sp=mean(mean.sp))
# res$time <- t
# df2 <- rbind(df2, res)
#}
#df.inv <- NULL
#for (t in levels(factor(df$time))) {
# current <- subset(df, time==t)
# #current$inv.mfpt <- (max(current$mean.mfpt)-current$mean.mfpt) /
# # (max(current$mean.mfpt)-min(current$mean.mfpt))
# #current$inv.sp <- (max(current$mean.sp)-current$mean.sp) /
# # (max(current$mean.sp)-min(current$mean.sp))
# current$inv.mfpt <- 1 / current$mean.mfpt
# current$inv.sp <- 1 / current$mean.sp
# df.inv <- rbind(df.inv, current)
#}
#df.inv$time <- as.numeric(df.inv$time)
#df4 <- ddply(df.inv, .(time), function(x) x[order(x$inv.mfpt,decreasing=T)[1:30],])
#df4 <- within(df4, time <- factor(time,
# levels = paste(tpoint,'h')))
#with(df4, levels(time))
#df4$type <- '0'
#df4$type[grep('EDA2R',df4$group)] <- '1'
#df4$type[grep('IFNGR1',df4$group)] <- '2'
#df4$type[grep('IFNAR2',df4$group)] <- '3'
df <- ddply(mfpt.sp, .(receptor,time), summarise, inv.mfpt=1/mean(mfpt),
inv.sp=1/mean(sp), tf.num=length(mfpt))
print(ggplot(df,aes(receptor,0)) + #geom_point() +
#scale_colour_manual(values=c('black','red','blue','green')) +
geom_segment(aes(xend=receptor,yend=inv.mfpt)) + coord_polar() +
facet_wrap(~time,scales='free') +
#geom_text(aes(label=V1),hjust=-0.2,alpha=0.3,size=2) +
labs(x='',y='Normalized inverse MFPT',title='HIPPIE') +
#ylim(0,1.1) #+
theme(axis.text.x=element_text(size=4),legend.position='none')
)
#mfpt <- NULL
#sp <- NULL
#for (t in levels(mfpt.sp$tf)) {
# current <- subset(mfpt.sp, tf==t)
# mfpt.z <- Re((current$mfpt - median(current$mfpt)) / (IQR(current$mfpt)
# /(2*sqrt(2)*erfc(0.5))))
# sp.z <- Re((current$sp - median(current$sp)) / (IQR(current$sp)
# /(2*sqrt(2)*erfc(0.5))))
# mfpt <- cbind(mfpt, mfpt.z)
# sp <- cbind(sp, sp.z)
#}
#mfpt <- as.data.frame(mfpt)
#names(mfpt) <- levels(mfpt.sp$tf)
#mfpt$receptor <- current$receptor
#mfpt$metric <- 'MFPT'
#sp <- as.data.frame(sp)
#names(sp) <- levels(mfpt.sp$tf)
#sp$receptor <- current$receptor
#sp$metric <- 'SP'
#print(ggplot(rbind(mfpt,sp),aes(CEBPD,RELB)) + geom_point() +
# #scale_colour_manual(values=c('black','red','blue','green')) +
# #geom_segment(aes(xend=receptor,yend=mfpt.z)) + coord_polar() +
# #geom_bar() +
# facet_wrap(~metric,scales='free_x') +
# geom_text(aes(label=receptor),hjust=-0.2,alpha=0.3,size=2) +
# geom_abline(intercept=0,slope=1,colour='red',linetype='dashed') +
# xlab('CEBPD') + ylab('RELB') +
# #ylab('Normalized inverse MFPT') +
# #ylab('Normalized inverse shortest path') +
# xlim(-35,15) #+
# #opts(axis.text.x=theme_text(size=4),legend.position='none')
#)
@
% ----------------------------------------------------------------------------
<<label=receptor_ts_ex,echo=False,fig=True,width=8,height=4,eval=True>>=
df.subset <- subset(df, receptor=='EGFR')
df.ex <- melt(df.subset, measure.vars=c('inv.mfpt','inv.sp'))
df.ex$variable <- c(rep('Inverse MFPT',nrow(df.subset)),
rep('Inverse SP',nrow(df.subset)))
print(ggplot(df.ex,aes(time,value)) + geom_point() +
geom_line() +
geom_ribbon(aes(ymin=0,ymax=value),alpha=0.2) +
facet_wrap(~variable,scales='free_y') +
labs(x='Time (h)',y='Distance (EGFR -> TFs)')
)
@
% ----------------------------------------------------------------------------
<<label=sp_bootstrap,echo=False,fig=False,cache=True,eval=True>>=
ppi <- 'hippie'
tf <- read.delim('~/data/tf/tf_table_final.csv', sep=';')
sp.bootstrap <- NULL
unique.receptor <- unique(mfpt.sp$receptor)
#get.sp <- function(receptor, tf) {
# tf.entrez <- map$idmap$entrez[which(map$idmap$official==tf)]
# tf.idx <- match(tf.entrez, V(net)$gene)
# sp <- shortest.paths(net, receptor.idx, tf.idx,
# weights=NA, mode='out') # BFS
#}
#tf.num <- nrow(subset(mfpt.sp,receptor==unique.receptor[1]))
tf.pool <- intersect(tf$Gene_Abbrev,map$idmap$official)
receptor.entrez <- map$idmap$entrez[match(unique.receptor,map$idmap$official)]
receptor.idx <- match(receptor.entrez, V(net)$gene)
tf.entrez <- map$idmap$entrez[match(tf.pool,map$idmap$official)]
tf.idx <- match(tf.entrez, V(net)$gene)
sp.mat <- shortest.paths(net, receptor.idx, tf.idx, weights=NA, mode='out')
tf.access <- colnames(sp.mat)[is.finite(colMeans(sp.mat))]
#stop()
#sp.all <- NULL
#for (k in 1:1) {
mean.inv.sp <- function(receptor, tf.num) {
tf.bootstrap <- sample(tf.access, tf.num)
#all.inv <- rep(0,length(tf.bootstrap))
#for (i in 1:length(tf.bootstrap)) {
# receptor.entrez <- map$idmap$entrez[which(map$idmap$official==receptor)]
# tf.entrez <- map$idmap$entrez[which(map$idmap$official==tf.bootstrap[i])]
# receptor.idx <- match(receptor.entrez, V(net)$gene)
# tf.idx <- match(tf.entrez, V(net)$gene)
# all.inv[i] <- shortest.paths(net, receptor.idx, tf.idx, weights=NA, mode='out')
#}
receptor.entrez <- map$idmap$entrez[which(map$idmap$official==receptor)]
receptor.idx <- match(receptor.entrez, V(net)$gene)
sp.all <- shortest.paths(net, receptor.idx, weights=NA, mode='out')
sp.bootstrap <- sp.all[,tf.bootstrap]
1/mean(sp.bootstrap)
}
mean.inv.mfpt <- function(receptor, tf.num) {
tf.bootstrap <- sample(tf.access, tf.num)
receptor.entrez <- map$idmap$entrez[which(map$idmap$official==receptor)]
tf.entrez <- map$idmap$entrez[match(tf.bootstrap,map$idmap$official)]
ii <- map$idmap$vertex[which(receptor.entrez == map$idmap$entrez)] + 1
jj <- map$idmap$vertex[match(tf.entrez,map$idmap$entrez)] + 1
mfpt.bootstrap <- mat$mfpt[ii,jj]
1/mean(mfpt.bootstrap)
}
#stop()
sp.bootstrap.all <- foreach(k=1:1000, .combine='cbind') %dopar% {
#tf.bootstrap <- sample(tf.access, tf.num)
#mfpt.sp$tf.bootstrap <- tf.bootstrap
#sp.bootstrap <- NULL
#for (i in 1:length(unique.receptor)) {
# receptor.entrez <- map$idmap$entrez[which(map$idmap$official==
# unique.receptor[i])]
# receptor.idx <- match(receptor.entrez, V(net)$gene)
# sp.all <- shortest.paths(net, receptor.idx, weights=NA, mode='out')
# sp.bootstrap <- c(sp.bootstrap, sp.all[,tf.bootstrap])
#}
#mfpt.sp$sp.bootstrap <- sp.bootstrap
#df <- NULL
#for (t in levels(factor(mfpt.sp$time))) {
# current <- subset(mfpt.sp, time==t)
# res <- ddply(current, .(receptor), summarise, mean.sp=mean(sp.bootstrap))
# res$time <- t
# df <- rbind(df, res)
#}
#df$time <- as.numeric(df$time)
#df$inv.sp <- 1 / df$mean.sp
df.bootstrap <- ddply(df, .(receptor,time), function(x) mean.inv.sp(x$receptor,x$tf.num))
df.integral.bootstrap <- ddply(df.bootstrap, .(receptor), function(x) trapz(sort(x$time),x$V1[order(x$time)]))
df.integral.bootstrap$V1
}
mfpt.bootstrap.all <- foreach(k=1:1000, .combine='cbind') %dopar% {
df.bootstrap <- ddply(df, .(receptor,time), function(x) mean.inv.mfpt(x$receptor,x$tf.num))
df.integral.bootstrap <- ddply(df.bootstrap, .(receptor), function(x) trapz(sort(x$time),x$V1[order(x$time)]))
df.integral.bootstrap$V1
}
df.integral.sp <- ddply(df, .(receptor), function(x) trapz(sort(x$time),x$inv.sp[order(x$time)]))
df.integral.sp$pval <- rowSums(df.integral.sp$V1<sp.bootstrap.all) / 1000
df.integral.mfpt <- ddply(df, .(receptor), function(x) trapz(sort(x$time),x$inv.mfpt[order(x$time)]))
df.integral.mfpt$pval <- rowSums(df.integral.mfpt$V1<mfpt.bootstrap.all) / 1000
#mfpt.sp$sp.mean <- rowMeans(sp.bootstrap.all)
#mfpt.sp$sp.sd <- rowSds(sp.bootstrap.all)
#mfpt.sp$pval <- rowSums(mfpt.sp$sp>sp.bootstrap.all) / 1000
##mfpt.sp <- subset(mfpt.sp, !is.infinite(sp.mean))
#mfpt.sp$z <- (mfpt.sp$sp - mfpt.sp$sp.mean) / mfpt.sp$sp.sd
#df.z <- NULL
#for (t in levels(factor(mfpt.sp$time))) {
# current <- subset(mfpt.sp, time==t)
# res <- ddply(current, .(receptor), summarise, mean.z=mean(pval))
# res$time <- t
# df.z <- rbind(df.z, res)
#}
#df.z$time <- as.numeric(df.z$time)
@
% ----------------------------------------------------------------------------
<<label=receptor_pval_ex,echo=False,fig=True,width=8,height=4,eval=True>>=
idx <- which(unique(df$receptor)=='EGFR')
df.ex <- data.frame(type='Inverse MFPT', value=mfpt.bootstrap.all[idx,])
df.ex <- rbind(df.ex, data.frame(type='Inverse SP', value=sp.bootstrap.all[idx,]))
df.cutoff <- data.frame(type='Inverse MFPT', value=df.integral.mfpt$V1[df.integral.mfpt$receptor=='EGFR'],y=max(density(df.ex$value[df.ex$type=='Inverse MFPT'])$y),label='EGFR')
df.cutoff <- rbind(df.cutoff, data.frame(type='Inverse SP', value=df.integral.sp$V1[df.integral.sp$receptor=='EGFR'],y=max(density(df.ex$value[df.ex$type=='Inverse SP'])$y),label='EGFR'))
mfpt.dens <- density(df.ex$value[df.ex$type=='Inverse MFPT'])
sp.dens <- density(df.ex$value[df.ex$type=='Inverse SP'])
df.dens <- data.frame(type='Inverse MFPT', x=mfpt.dens$x[mfpt.dens$x>df.cutoff$value[df.cutoff$type=='Inverse MFPT']],y=mfpt.dens$y[mfpt.dens$x>df.cutoff$value[df.cutoff$type=='Inverse MFPT']])
df.dens <- rbind(df.dens, data.frame(type='Inverse SP', x=sp.dens$x[sp.dens$x>df.cutoff$value[df.cutoff$type=='Inverse SP']],y=sp.dens$y[sp.dens$x>df.cutoff$value[df.cutoff$type=='Inverse SP']]))
print(ggplot(df.ex,aes(value)) + geom_density() +
geom_vline(data=df.cutoff,aes(xintercept=value),linetype='dashed') +
geom_ribbon(data=df.dens,aes(x=x,ymin=0,ymax=y),alpha=0.2) +
geom_text(data=df.cutoff,aes(x=value,y=y,label=label),size=4,hjust=-0.3) +
facet_wrap(~type,scales='free') +
labs(x='AUC over time (EGFR -> TFs)',y='Density')
)
@
% ----------------------------------------------------------------------------
<<label=receptor_ts,echo=False,fig=True,width=10,height=4,eval=True>>=
goi <- c('^IL[0-9]+R','^TNFR','^CXCR','^CCR')
genes <- c('ILRs','TNFRs','CXCRs','CCRs')
#goi <- c('TNFRSF1[AB]','KDR','CCR[135]','IL6R','CXCR4','IL8R[AB]','IL8RB')
#genes <- c('TNF','VEGF','CCL5','IL6','SDF-1a','IL8','CXCL1')
#df <- ddply(df.inv, .(receptor), function(x) trapz(sort(x$time),x$inv.sp[order(x$time)]))
#df <- ddply(df.z, .(receptor), function(x) trapz(sort(x$time),-log10(x$mean.z[order(x$time)])))
df <- data.frame(receptor=df.integral.sp$receptor,sp.auc=df.integral.sp$V1,sp.pval=df.integral.sp$pval,mfpt.auc=df.integral.mfpt$V1,mfpt.pval=df.integral.mfpt$pval)
df$group <- 'Other'
df$group.num <- '0'
for (i in 1:length(goi)) {
df$group[grep(goi[i],df$receptor)] <- genes[i]
df$group.num[grep(goi[i],df$receptor)] <- as.character(5 - i)
}
df <- df[order(df$group.num,df$sp.auc,decreasing=T),]
df <- within(df, receptor <- factor(receptor,
levels = df$receptor))
df <- within(df, group <- factor(group,
levels = df$group))
#df$y1 <- NA
#df$y1[df$sp.pval<0.1] <- df$sp.auc[df$sp.pval<0.1] + 0.1
#df$y2 <- NA
#df$y2[df$sp.pval<0.05] <- df$sp.auc[df$sp.pval<0.05] + 0.5
#df$y3 <- NA
#df$y3[df$sp.pval<0.01] <- df$sp.auc[df$sp.pval<0.01] + 1
df$y1 <- NA
df$y1[df$mfpt.pval<0.1] <- df$mfpt.auc[df$mfpt.pval<0.1] + 0.2
df$y2 <- NA
df$y2[df$mfpt.pval<0.05] <- df$mfpt.auc[df$mfpt.pval<0.05] + 0.4
df$y3 <- NA
df$y3[df$mfpt.pval<0.01] <- df$mfpt.auc[df$mfpt.pval<0.01] + 0.6
print(ggplot(df,aes(receptor,mfpt.auc,fill=group)) +
#print(ggplot(df,aes(receptor,-log10(pval))) +
#geom_point(aes(receptor,y1),shape='*',size=5) +
#geom_point(aes(receptor,y2),shape='*',size=5) +
#geom_point(aes(receptor,y3),shape='*',size=5) +
#geom_line(aes(colour=receptor)) +
geom_bar(colour='black') +
scale_fill_brewer('',palette='Set1') +
#scale_y_continuous(breaks=seq(0,8,2),labels=seq(0,8,2)+2.5) +
#geom_hline(yintercept=-log10(0.05),colour='red',linetype='dashed') +
#facet_wrap(~group,scales='free_x') +
guides(fill=guide_legend(nrow=1)) +
#labs(x='Receptors',y=expression(paste(-log[10]~'p-value')),
# title='Inverse SP after bootstrapping\n') +
labs(x='Receptors',y='AUC',title='Inverse MFPT\n') +
theme(axis.text.x=element_text(angle=90,size=7),legend.position=c(0.5,1.1),
legend.background=element_blank(),legend.key.size=unit(0.2,'cm'))
)
@
% ----------------------------------------------------------------------------
<<label=fit_diff,echo=False,fig=False,cache=True,eval=True>>=
tpoint <- c(0,0.5,1:5,10,15,24)
ctrl <- read.delim('~/data/hpaec/h838_ctrl_14922.dat', sep='|')
treated <- read.delim('~/data/hpaec/h838_treated_14922.dat', sep='|')
diff <- read.delim('~/data/hpaec/h838_diff_14922.dat', sep='|')
symbol <- diff$Gene
diff <- rowMeans(diff[,-1])
core <- cbind(ctrl[,-1], treated[,-1])
pval <- apply(core, 1, lrtest, tpoint, tpoint)
@
% ----------------------------------------------------------------------------
<<label=fit_hist,echo=False,fig=True,eval=False>>=
res <- hist(-log10(p.adjust(pval,'BH')), 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') +
labs(x=expression(paste(-log[10]~italic(q),'-value')),
y='Count',title='Temporal differential expression of H838 genes\n')
)
@
% ----------------------------------------------------------------------------
<<label=fit_ex,echo=False,fig=True,eval=False>>=
#goi <- 'IL6R'
#ctrl.df <- list(dat=ctrl[which(ctrl$Gene==goi),], cond='homo',
# tpoint=c(0,tpoint))
#treated.df <- list(dat=treated[which(treated$Gene==goi),], cond='hetero',
# tpoint=c(0,tpoint))
#df.ex <- plot.full.reduced.example(ctrl.df, treated.df, goi)
ctrl.df <- list(dat=ctrl[order(pval)[1:1000],], cond='homo', tpoint=tpoint)
treated.df <- list(dat=treated[order(pval)[1:1000],], cond='hetero', tpoint=tpoint)
df.ex <- plot.full.reduced(ctrl.df, treated.df, '~/data/hpaec/h838_fit_full_reduced.pdf')
@
% ----------------------------------------------------------------------------
<<label=gage_diff,echo=False,fig=False,cache=False,eval=False>>=
# GAGE
library(gage)
library(gageData)
data(go.gs)
data(kegg.gs)
data(egSymb)
library(xtable)
go.gs.sym <- lapply(go.gs, eg2sym)
kegg.gs.sym <- lapply(kegg.gs, eg2sym)
mSigDB <- readLines("~/data/MSigDB/c2.all.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)])
#test with direction: either up or down
df.diff <- data.frame(diff=-log10(pval))
rownames(df.diff) <- ctrl$Gene
res <- gage(df.diff, gsets = mSigDB, ref = NULL, samp = 1,
compare='paired', saaTest=gs.KSTest, same.dir=T)
df <- as.data.frame(res$greater)
df <- subset(df[,c('p.val','set.size')], p.val<0.01)
rownames(df) <- sapply(rownames(df), function(x) substr(x,1,40))
tbl <- xtable(df, caption='Significantly ($p < 0.01$) upregulated gene sets in heterogeneous coculture compared to homogeneous coculture.', display=c('s','e','d'))
print(tbl, tabular.environment = "longtable",
add.to.row=list(as.list(seq(1,nrow(df),2)),rep("\\rowcolor{Gray} ",
length(seq(1,nrow(df),2)))), include.rownames=T,
file='~/data/hpaec/h838_table_gage_diff_up.tex',
caption.placement='top')
#df <- as.data.frame(res$less)
#df <- subset(df[,c('p.val','set.size')], p.val<0.01)
#tbl <- xtable(df, caption='Significantly ($p < 0.01$) downregulated gene sets in heterogeneous coculture compared to homogeneous coculture.', display=c('s','e','d'))
#print(tbl, tabular.environment = "longtable",
# add.to.row=list(as.list(seq(1,nrow(df),2)),rep("\\rowcolor{Gray} ",
# length(seq(1,nrow(df),2)))), include.rownames=T,
# file='~/data/hpaec/h838_table_gage_diff_down.tex',
# caption.placement='top')
@
% ----------------------------------------------------------------------------
<<label=ts_heatmap,echo=False,fig=True,width=7,height=7,eval=False>>=
#df <- data.frame(diff=diff, qval=p.adjust(pval,'BH'), symbol=symbol)
#comparison <- ddply(df, .(group), function(df) data.frame(text=sprintf("upregulated in CFU-E: %d upregulated in BaF3-EpoR: %d",
# sum(df$diff<0&df$pval<0.05), sum(df$diff>0&df$pval<0.05))))
### Set up a blank theme
theme_none <- theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
axis.title.x = element_text(colour=NA),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank()
#plot.margin = unit(c(0, 0, 0, 0), "cm")
#axis.ticks.length = theme_blank()
)
#mycolor <- gray.colors(128,0.9,0)
mycolor <- colorRampPalette(rev(c("#D73027", "#FC8D59", "#FEE090", "#FFFFBF", "#E0F3F8", "#91BFDB", "#4575B4")))(100)
#mycolor <- rev(brewer.pal(11,'RdBu'))
qval <- p.adjust(pval, 'BH')
final <- treated[qval<0.01,-1]
rownames(final) <- treated$Gene[qval<0.01]
names(final) <- paste(tpoint,'h')
#plot.ts <- function(final) {
dd.col <- as.dendrogram(hclust(dist(final)))
col.ord <- order.dendrogram(dd.col)
max_val <- 1.3
values <- seq(-max_val, max_val, length = 11)
df <- final[col.ord,]
df$term <- rownames(df)
df <- within(df, term <- factor(term, levels = rownames(df)))
with(df, levels(term))
df <- melt(df)
p1 <- ggplot(df,aes(variable,term)) +
#geom_tile(aes(fill=-log10(value))) +
geom_tile(aes(fill=value)) +
#scale_fill_brewer(palette='RdBu') +
scale_fill_gradientn(colours = mycolor,
values = values, rescaler = function(x, ...) x, oob = identity,
limits=c(-max_val,max_val)) +
guides(fill = guide_colorbar(barwidth = 0.5, barheight = 10,
title.hjust=1.6, title.vjust=0.4,
#title.position='right',
title=expression(paste(log[2]~'fold expression')))) +
labs(x = "",y = "") + scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
theme(axis.ticks = element_blank(),axis.text.x=element_text(angle=90),
panel.border=element_blank(),legend.title=element_text(angle=90),
axis.text.y=element_blank())
#plot.margin=unit(c(0, 0, 0, 0), "cm"))
### Use ggdendro to extract dendrogram data ###
ddata_y <- dendro_data(dd.col)
# Dendrogram 2
p3 <- ggplot(segment(ddata_y)) +
geom_segment(aes(x=x, y=y, xend=xend, yend=yend)) +
coord_flip() + theme_none + #ylab('\n\n\n') +
scale_x_discrete(expand = c(0, 0)) +
scale_y_reverse()
grid.newpage()
print(p1, vp=viewport(0.8, 1, x=0.6, y=0.5))
print(p3, vp=viewport(0.2, 0.94, x=0.1, y=0.5))
#}
#plot.ts(df.ts)
#res <- hclust(dist(final))
#tree <- cutree(res, 2)
#sample <- unlist(mget(names(tree[tree==1]),org.Hs.egSYMBOL2EG,ifnotfound=NA))
#universe <- unlist(mget(as.character(treated$Gene),org.Hs.egSYMBOL2EG,
# ifnotfound=NA))
#params <- new("GOHyperGParams", geneIds = sample,
# universeGeneIds = universe, annotation = "org.Hs.eg", ontology = "BP",
# pvalueCutoff = 0.05, conditional = FALSE, testDirection = "over")
#go_over <- hyperGTest(params)
#stat <- summary(go_over)
#print(ggplot(df,aes(diff,-log10(qval))) +
# geom_point() +
# #geom_text(aes(label=symbol),size=3,hjust=-0.2,alpha=0.3) +
# #geom_text(data=comparison,aes(x=0,y=8,label=text),size=4) +
# geom_vline(aes(xintercept=0),colour='blue',linetype='dashed') +
# geom_hline(aes(yintercept=-log10(0.05)),colour='red',linetype='dashed') +
# xlab(expression(paste('Average ',log[2],' fold change (heterogeneous - homogeneous)'))) +
# xlim(-1,1) + ylim(0,2.6) +
# ylab(expression(paste(-log[10],' ',italic(q),'-value'))) +
# opts(title='Full/reduced model fitting of\nH838 time course')
#)
@
% ----------------------------------------------------------------------------
%\section{Cytokine array}
%
%\begin{figure}[ht]
%\begin{center}
%<<label=cytokine,echo=False,fig=True>>=
%library(ggdendro)
%dat <- read.delim('~/work/hmec/h838/cytokine_array.dat', sep='|', row.names=1)
%df <- as.data.frame(t(rbind(log2(dat[2,]/dat[1,]), log2(dat[4,]/dat[3,]))))
%names(df) <- c('H838.HPAEC','H838.H838')
%df[is.nan(df[,1]),1] <- 0
%df[is.nan(df[,2]),2] <- 0
%df$name <- rownames(df)
%df$name[1] <- 'GRO-alpha'
%df$name[6] <- 'SDF-1-alpha'
%df$name[23] <- 'TNF-alpha'
%#df.label <- df$name[df$H838.HPAEC>df$H838.H838]
%#df.label <- c(df.label, 'VEGF')
%df.label <- df$name
%print(ggplot(df,aes(H838.H838,H838.HPAEC)) +
% geom_point() + geom_abline(intercept=0,slope=1,linetype='dashed') +
% geom_segment(data=data.frame(x=0,y=-4,xend=0,yend=0),
% aes(x,y,xend=xend,yend=yend),linetype='dashed',colour='blue') +
% geom_segment(data=data.frame(x=-4,y=0,xend=0,yend=0),
% aes(x,y,xend=xend,yend=yend),linetype='dashed',colour='blue') +
% geom_text(data=df[match(df.label,df$name),],aes(H838.H838,H838.HPAEC,
% label=name),hjust=-0.2,size=3,parse=T) +
% #geom_text(data=df[match('VEGF',df$name),],aes(H838.H838,H838.HPAEC,
% # label=name),hjust=-0.2,size=3,colour='red') +
% xlab('H838 + H838') + ylab('H838 + HPAEC') +
% xlim(-4,2) + ylim(-4,2) +
% opts(title=expression(paste(log[2],' normalized cytokine expression')))
%)
%@
%\end{center}
%\caption{Basal cytokine mRNA expression levels. Upper left:
%the other replicate at 0h.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]
%\begin{center}
%<<label=mono,echo=False,fig=True,width=8,height=4>>=
%df1 <- data.frame(mean=c(164.6266555, 242.3984895, 206.705379, 166.7222301, 186.0556441, 159.6766093), exp=c('Control','Factor Group 1','TNFa','VEGF','Rantes','IL-6'), sd=c(21.20324556, 43.9110865, 33.28455182, 26.98885178, 29.58733858, 29.08766464), cond=rep('Experiment 1',6))
%df1 <- within(df1, exp <- factor(exp, levels = df1$exp))
%with(df1, levels(exp))
%df2 <- data.frame(mean=c(171.3736933, 217.7026949, 234.3290311, 188.225389, 191.9769698, 187.924612), exp=c('Control','Factor Group 2','SDF-1a','MCP-1','IL-8','Gro-a'), sd=c(31.15060737, 28.19990634, 31.91501786, 40.20281597, 33.48534817, 37.05511063), cond=rep('Experiment 2',6))
%df2 <- within(df2, exp <- factor(exp, levels = df2$exp))
%with(df2, levels(exp))
%vplayout(1,2)
%p <- ggplot(df1,aes(x=exp,y=mean)) +
% geom_bar(fill='gray',colour='black',stat='identity',width=0.5) +
% geom_errorbar(aes(ymax=mean+sd,ymin=mean),width=0.5) + ylim(0,350) +
% xlab('') + ylab(expression(paste('Migration area at 24h (',mu,'m'^2,')'))) +
% opts(axis.text.x=theme_text(angle=45))
%print(p, vp=subplot(1,1))
%p <- ggplot(df2,aes(x=exp,y=mean)) +
% geom_bar(fill='gray',colour='black',stat='identity',width=0.5) +
% geom_errorbar(aes(ymax=mean+sd,ymin=mean),width=0.5) +
% xlab('') + ylab('') + ylim(0,350) +
% opts(axis.text.x=theme_text(angle=45))
%print(p, vp=subplot(1,2))
%@
%\end{center}
%\caption{Basal cytokine mRNA expression levels. Upper left:
%the other replicate at 0h.}
%\label{fig:raw}
%\end{figure}
% ----------------------------------------------------------------------------
%\begin{figure}[ht]