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3_DEanalysis.R
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3_DEanalysis.R
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###############################################################################################################
#
# Filippos Klironomos, Department of Pediatric Hematology, Oncology and SCT Charité University Hospital Berlin
#
###############################################################################################################
##################################################
#
#
# clustering
# differential expression analysis
# GSEA
#
#
##################################################
# Vermeulen et al. The Lancet Oncology (2009) list of prognostic gene set
#{{{
rm(list=ls())
library(GenomicFeatures)
library(rtracklayer)
library(data.table)
library(DESeq2)
library(pheatmap)
library(ggplot2)
library(ggrepel)
library(RColorBrewer)
library(extrafont) # first time used need to run: font_import() , to load all for PDF device run: loadfonts(device='pdf')
loadfonts()
source('~/bio/lib/draw_highlights.R')
# [run once] create list of prognostic genes by manually copying and pasting the Table from the SI
# get the corresponding circRNA/mRNA expression in the neuroblastoma tumors
#{{{
source('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/lib/load_circ_gene_expression.R')
# load reference to resolve gene_ids
hsa<-import('/fast/groups/ag_schulte/work/reference/annotation/GRCh38/GRCh38.gencode.v30.gtf')
hsa<-hsa[ hsa$type %in% 'gene' ]
mcols(hsa)<-mcols(hsa)[, c('gene_id', 'gene_name', 'gene_type')]
verm<-data.frame(gene_name=c('AHCY', 'AKR1C1', 'ARHGEF7', 'BIRC5', 'CADM1', 'CAMTA1', 'CAMTA2', 'CD44', 'CDCA5', 'CDKN3', 'CHD5', 'CLSTN1', 'CPSG3', 'DDC', 'DPYSL3', 'ECEL1', 'ELAVL4', 'EPB41L3', 'EPHA5', 'EPN2', 'FYN', 'GNB1', 'HIVEP2', 'INPP1', 'MAP2K4', 'MAP7', 'MAPT', 'MCM2', 'MRPL3', 'MTSS1', 'MYCN', 'NHLH2', 'NME1', 'NRCAM', 'NTRK1', 'ODC1', 'PAICS', 'PDE4DIP', 'PIK3R1', 'PLAGL1', 'PLAT', 'PMP22', 'PRAME', 'PRDM2', 'PRKACB', 'PRKCZ', 'PTN', 'PTPRF', 'PTPRH', 'PTPRN2', 'QPCT', 'SCG2', 'SLC25A5', 'SLC6A8', 'SNAPC1', 'TNFRSF25', 'TYMS', 'ULK2', 'WSB1'), risk_group_up=c('HR', 'LR', 'LR', 'HR', 'LR', 'LR', 'LR', 'LR', 'HR', 'HR', 'LR', 'LR', 'HR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'HR', 'HR', 'LR', 'HR', 'HR', 'HR', 'LR', 'LR', 'HR', 'HR', 'LR', 'LR', 'LR', 'LR', 'LR', 'HR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'LR', 'HR', 'HR', 'HR', 'LR', 'HR', 'LR', 'LR'))
# CPSG3 (NM_004386) is actually NCAN
setdiff(verm$gene_name, hsa$gene_name)
verm$gene_name[ verm$gene_name %in% 'CPSG3' ]<-'NCAN'
# add gene_ids
verm$gene_id<-hsa$gene_id[ match(verm$gene_name, hsa$gene_name) ]
verm<-verm[, c('gene_id', 'gene_name', 'risk_group_up')]
# load unified set of circRNAs
# limit genes to those of the Vermeulen geneset, the gene list is defined irrespective of whether a circRNA exists in the unified group of circRNAs
# load mRNA expression in TPMs and compute circRNA CPMs
load('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/circRNAs_across_tissues.RData')
GENES<-DataFrame( verm[, c('gene_id', 'gene_name')] )
CIRCS<-CIRCS[ CIRCS$gene_id %in% verm$gene_id ]
length(CIRCS) # 45 circRNAs
length(unique(CIRCS$gene_id)) # 18 genes
l<-load_circ_gene_expression(CIRCS, GENES, META=nb.meta, nb.tumors.only=T, nb.only=T, gene.tpm=T)
nb.meta<-l$meta
nb.meta<-nb.meta[, risk_group:=factor(risk_group, levels=c('ST4S', 'LR', 'IMR', 'HR_nMNA', 'MNA'))][ order(risk_group), ][, risk_group:=as.character(risk_group)]
nb.circs<-l$circs
nb.genes<-l$genes
stopifnot( length(setdiff( GENES$gene_id, rownames(l$counts) ))==0 )
nb.counts<-l$counts[ GENES$gene_id, ] # (genes x samples) matrix order (do not convert gene_id to gene_name because there are duplicates!)
rm(list=c('l','CIRCS.all','GENES',ls(pattern='vt.|hb.')))
# save
save(verm, nb.meta, nb.circs, nb.genes, nb.counts, CIRCS, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/Vermeulen et al_prognostic_geneset.RData')
#}}}
# load the gene set and the circRNA/mRNA expression
load('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/Vermeulen et al_prognostic_geneset.RData')
# [genes, log10-transformed TPMs, all risk groups] boxplot of LR-up and HR-up genes
#{{{
# split by risk group
nb.<-split(nb.genes[, ! colnames(nb.genes) %in% c('risk_group', 'cell_model', 'tissue')], nb.genes$risk_group)
l.LR<-lapply(nb., function(x){ log10(1+unname(unlist(c(x[, colnames(x) %in% verm$gene_name[ verm$risk_group_up %in% 'LR' ]])))) })
l.HR<-lapply(nb., function(x){ log10(1+unname(unlist(c(x[, colnames(x) %in% verm$gene_name[ verm$risk_group_up %in% 'HR' ]])))) })
l.LR<-l.LR[ unique(nb.meta$risk_group) ]
l.HR<-l.HR[ unique(nb.meta$risk_group) ]
L<-append(l.LR, l.HR)
# boxplot
x11(width=18, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
par(mar=c(5.0, 7.0, 0.5, 0.0), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=NA, bty='n', cex.lab=2.4, cex.axis=2.4)
options(scipen=0)
YTICK<-pretty(c(0, max(ceiling(sapply(L, max, na.rm=T)))), 4)
plot(0:1, 0:1, xlim=c(0, length(L))+c(0.5, 0.5), type='n', ylim=range(YTICK), axes=F, ann=F, xaxs='i')
bp<-boxplot(L, col=unique(nb.meta$col), ylab='', xlab='', show.names=F, frame.plot=F, medcol='lightgrey', boxwex=0.8, xpd=F, outline=F, boxcol=unique(nb.meta$col), range=0, add=T)
mtext(expression(log[10](1+TPM)), side=2, line=2, padj=-0.5, las=0, cex=2.4)
mtext(text=c('LR-up', 'HR-up'), side=1, line=1, at=seq(median(seq_along(l.LR)), length(L), length(l.LR)), las=1, adj=0.5, cex=2.4, col='black')
mtext(text=c(paste0('(', table(verm$risk_group_up)['LR'], ')'), paste0('(', table(verm$risk_group_up)['HR'], ')')), side=1, line=3, at=seq(median(seq_along(l.LR)), length(L), length(l.LR)), las=1, adj=0.5, cex=2.4, col='black')
draw_highlights(L=length(L), STEP=length(l.LR), YMAX=max(YTICK))
legend('topleft', legend=names(l.LR), col=unique(nb.meta$col), bty='n', lty=1, lwd=15, pch=NA, cex=1.8, xpd=T, y.intersp=0.60, x.intersp=0.2, seg.len=0.5)
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/boxplot_Vermeulen et al_prognostic_geneset_gene_TPMs.svg', width=18, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
# clean up
dev.off()
#}}}
# [genes] hierarchical clustering and PCA
#{{{
# round up gene counts of all genes to integers and variance-stabilize them
stopifnot( length(setdiff(colnames(nb.counts), nb.meta$bid))==0 )
dds<-DESeqDataSetFromMatrix(countData=as.matrix(ceiling(nb.counts[, nb.meta$bid])), colData=data.frame(Risk=factor(nb.meta$risk_group), row.names=nb.meta$bid), design=~Risk)
nb.sf<-sizeFactors(estimateSizeFactors(dds, type='poscounts'))
nb.vs<-assay(varianceStabilizingTransformation(dds, fitType='local', blind=T))
rm(dds,nb.sf)
# select the genes of interest and convert gene_id to gene_name
stopifnot( length(setdiff(verm$gene_id, rownames(nb.vs)))==0 )
nb.vs<-nb.vs[ verm$gene_id, ]
rownames(nb.vs)<-verm$gene_name[ match(rownames(nb.vs), verm$gene_id) ]
# recycle
x11(width=14, height=12, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
ex<-data.frame(Risk=factor(nb.meta[ match(colnames(nb.vs), bid), risk_group], exclude=F), row.names=colnames(nb.vs))
cl<-setNames(list(setNames( nb.meta[, unique(col)], nb.meta[, unique(risk_group)] )), colnames(ex))
# [log10-transformed TPMs] clustering based on Spearman correlations of complete pairs
x<-log10(1+t(nb.genes[, ! colnames(nb.genes) %in% c('risk_group', 'cell_model', 'tissue')]))
stopifnot( length(setdiff(colnames(x), colnames(nb.vs)))==0 )
x<-x[, colnames(nb.vs)]
d<-cor(x, method='spearman', use='pairwise.complete.obs')
hc<-hclust(as.dist(1-d), method='ward.D2')
ph<-pheatmap(as.matrix(d), color=rev(colorRampPalette(brewer.pal(9,'RdBu'))(20)), border_color=NA, scale='none',
breaks=seq(0, 1, length.out=21), # reduced range
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=F, show_colnames=F,
#display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize=18, fontsize_row=18, fontsize_col=18, fontsize_number=10)
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_Vermeulen et al_prognostic_geneset_genes_tpm_cor.svg', width=20, height=16, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(x)
# [variance-stabilized counts] clustering based on Euclidean distance
d<-dist(t(nb.vs), method='euclidean')
hc<-hclust(d, method='ward.D2')
ph<-pheatmap(as.matrix(d), color=colorRampPalette(brewer.pal(9,'GnBu'))(20), border_color=NA, scale='none',
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=F, show_colnames=F,
#display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize=18, fontsize_row=18, fontsize_col=18, fontsize_number=10)
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_Vermeulen et al_prognostic_geneset_genes_vsc.svg', width=20, height=16, bg='white', antialias='subpixel', pointsize=20, family='Arial')
# [variance-stabilized counts] PCA centered but not scaled
p<-prcomp(t(nb.vs), center=T, scale.=F) # PCA on the samples is done when the samples are the rows
ve<-round(100*p$sdev^2/sum(p$sdev^2), 1)
pca<-as.data.frame(p$x)
pca$'risk group'<-nb.meta[ match( rownames(pca), bid ), risk_group ]
XLIM<-pretty(range(p$x[, 'PC1']), 5)
XLIM<-c(XLIM[1], XLIM[length(XLIM)])
YLIM<-pretty(range(p$x[, 'PC2']), 5)
YLIM<-c(YLIM[1], YLIM[length(YLIM)])
ggplot(pca[, c('PC1', 'PC2', 'risk group')], aes(PC1, PC2, color=`risk group`)) +
geom_point(size=10) +
#geom_text_repel(aes(label=rownames(pca)), size=10, box.padding=0.5, segment.size=1.0, min.segment.length=1.0) +
scale_shape_manual(values=c(18:21)) +
scale_fill_manual(name='risk group', values=cl$Risk) +
scale_color_manual(name='risk group', values=cl$Risk) +
theme(text=element_text(family='Arial'), axis.text.x=element_text(size=34, color='black'), axis.title.x=element_text(size=34),
axis.text.y=element_text(size=34, color='black'), axis.title.y=element_text(size=34),
legend.text=element_text(size=22), legend.title=element_text(size=22, face='plain'),
aspect.ratio=1, panel.background = element_blank(),
axis.line.x=element_line(size=0.5, colour = 'black', linetype='solid'),
axis.line.y=element_line(size=0.5, colour = 'black', linetype='solid'),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
scale_x_continuous(lim=XLIM, breaks=pretty(XLIM,5)) +
scale_y_continuous(lim=YLIM, breaks=pretty(YLIM,5)) +
xlab(paste0('PC1: ', ve[1], '% variance')) + ylab(paste0('PC2: ', ve[2], '% variance'))
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/plotPCA_Vermeulen et al_prognostic_geneset_genes_vsc.svg', width=16, height=16, bg='white', antialias='subpixel', pointsize=20, family='Arial')
dev.off()
#}}}
# [circRNAs, log10-transformed CPMs, all risk groups] boxplot of LR-up and HR-up genes
#{{{
# split by risk group
x<-as.data.table(as.data.frame(mcols(nb.circs)[, c('bid', 'circ_name', 'cpm')]))
x<-dcast(x, bid ~ circ_name, value.var='cpm', fun.aggregate=sum)
y<-as.data.frame(x[, -1])
rownames(y)<-x[, bid]
stopifnot( length(setdiff(rownames(y), rownames(nb.genes)))==0 )
x<-y[ rownames(nb.genes), ]
LRcircs<-colnames(x)[grep(paste0(verm$gene_name[ verm$risk_group_up %in% 'LR' ], collapse='|'), colnames(x))]
HRcircs<-colnames(x)[grep(paste0(verm$gene_name[ verm$risk_group_up %in% 'HR' ], collapse='|'), colnames(x))]
stopifnot( length(LRcircs)+ length(HRcircs)==ncol(x) )
nb.<-split(x, nb.genes$risk_group)
l.LR<-lapply(nb., function(x){ log10(1+unname(unlist(c(x[, LRcircs])))) })
l.HR<-lapply(nb., function(x){ log10(1+unname(unlist(c(x[, HRcircs])))) })
l.LR<-l.LR[ unique(nb.meta$risk_group) ]
l.HR<-l.HR[ unique(nb.meta$risk_group) ]
L<-append(l.LR, l.HR)
# boxplot
x11(width=18, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
par(mar=c(5.0, 10.0, 0.5, 0.0), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=NA, bty='n', cex.lab=2.4, cex.axis=2.4)
options(scipen=0)
#YTICK<-pretty(c(0, max(ceiling(sapply(L, max, na.rm=T)))), 4)
YTICK<-pretty(c(0, 0.2), 4)
plot(0:1, 0:1, xlim=c(0, length(L))+c(0.5, 0.5), type='n', ylim=range(YTICK), axes=F, ann=F, xaxs='i')
bp<-boxplot(L, col=unique(nb.meta$col), ylab='', xlab='', show.names=F, frame.plot=F, medcol='lightgrey', boxwex=0.8, xpd=F, outline=F, boxcol=unique(nb.meta$col), range=0, add=T)
mtext(expression(log[10](1+CPM)), side=2, line=5, padj=-0.5, las=0, cex=2.4)
mtext(text=c('LR-up', 'HR-up'), side=1, line=1, at=seq(median(seq_along(l.LR)), length(L), length(l.LR)), las=1, adj=0.5, cex=2.4, col='black')
mtext(text=c(paste0('(', table(verm$risk_group_up)['LR'], ')'), paste0('(', table(verm$risk_group_up)['HR'], ')')), side=1, line=3, at=seq(median(seq_along(l.LR)), length(L), length(l.LR)), las=1, adj=0.5, cex=2.4, col='black')
draw_highlights(L=length(L), STEP=length(l.LR), YMAX=max(YTICK), YMIN=0.0)
legend('topleft', legend=names(l.LR), col=unique(nb.meta$col), bty='n', lty=1, lwd=15, pch=NA, cex=1.8, xpd=T, y.intersp=0.60, x.intersp=0.2, seg.len=0.5)
#dev.print(device=pdf, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/boxplot_Vermeulen et al_prognostic_geneset_circRNAs_CPMs.pdf', width=18, height=14, bg='white', colormodel='cmyk', pointsize=20, useDingbats=F, family='Arial')
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/boxplot_Vermeulen et al_prognostic_geneset_circRNAs_CPMs.svg', width=18, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
dev.off()
#}}}
#}}}
#
# => /fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/Vermeulen et al_prognostic_geneset.RData
# [run once] Prepare circRNAs and genes for the analysis. We compute:
#
# gene TPMs, raw counts and variance-stabilized log2-transformed counts
# circRNA raw counts and variance-stabilized log2-transformed counts based on size factors computed from gene counts
#
# PCA for genes and circRNAs is done THROUGHOUT TUMORS AND CELL LINES using centered but not scaled variance-stabilized
# (and log2-transformed) counts
#
#{{{
rm(list=ls())
library(GenomicAlignments)
library(rtracklayer)
library(data.table)
library(DESeq2)
library(vsn)
library(RColorBrewer)
library(gplots)
library(ggplot2)
library(pheatmap)
library(openxlsx)
library(extrafont) # first time used need to run: font_import() , to load all for PDF device run: loadfonts(device='pdf')
loadfonts()
source('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/lib/load_circ_gene_expression.R')
# load reference
# discard chrM/chrY genes from the analysis
hsa<-import('/fast/groups/ag_schulte/work/reference/annotation/GRCh38/GRCh38.gencode.v30.gtf')
hsa<-hsa[ hsa$type %in% 'gene']
mcols(hsa)<-mcols(hsa)[, c('gene_id', 'gene_type', 'gene_name')]
seqlevels(hsa, pruning.mode='coarse')<-seqlevels(hsa)[ grep('chrM|chrY', seqlevels(hsa), invert=T) ]
# remove failed samples and Pilot samples
load('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/metadata.RData')
meta<-rbind(meta.tum, meta.cel, fill=T)
meta<-meta[ !(failed) & !grepl('CBPilote', bid) ]
# add sex metadata to the tumor samples
#load('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/sex_determination.RData')
#meta<-sex[ meta, on='bid'][, -c(1:3)]
#rm(sex)
#
# N.B. latest clinical annotation includes sex besides for CB3009 which we find it to be male
meta[, sex:=SEX][, SEX:=NULL]
meta[ PAT_ID_BERLIN %in% 'CB3009', sex:='M' ]
# load our cohort of circRNAs
load('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/circRNAs_across_tissues.RData')
rm(list=ls(pattern='\\.(genes|circs|meta|prefailed|all)|GENES'))
# circRNA counts and gene counts (for all genes) + TPMs (for the selected genes)
# make sure the correct samples are kept throughout
nb<-load_circ_gene_expression(CIRCS=CIRCS, GENES=mcols(hsa)[, c('gene_id', 'gene_name')], META=meta, nb.tumors.only=F, nb.only=T, gene.tpm=T)
circ<-nb$circs[ nb$circs$bid %in% meta$bid ]
gns.tpm<-t(nb$genes[ meta$bid, grep('risk_group|cell_model|tissue', colnames(nb$genes), invert=T)])
stopifnot( length(setdiff(hsa$gene_id, rownames(nb$counts)))==0 )
gns.cnt<-ceiling(nb$counts[hsa$gene_id, meta$bid]) # remove chrM, chrY counts, the resulting matrix form is: (gene_id X sample)
rm(nb)
# remove unexpressed genes
gns.cnt<-gns.cnt[rowSums(gns.cnt)!=0, ]
gns.tpm<-gns.tpm[rowSums(gns.tpm)!=0, ]
# compute size factors across all samples (tumors and cell lines)
# compute variance-stabilized gene counts
dds<-DESeqDataSetFromMatrix(countData=gns.cnt, colData=data.frame(risk_group=factor(meta$risk_group), cell_model=factor(meta$cell_model), row.names=meta$bid), design=~1)
gns.sf<-sizeFactors(estimateSizeFactors(dds, type='poscounts'))
gns.vs<-assay(varianceStabilizingTransformation(dds, fitType='local'))
rm(dds)
# PCA on variance-stabilized (and glog2-transformed) gene counts centered but not scaled
gns.pca<-prcomp(t(gns.vs), center=T, scale.=F)
gns.ve<-round(1000 * gns.pca$sdev^2/sum(gns.pca$sdev^2))/10
# CHECK: compute size factors across tumors
# compute variance-stabilized gene counts for the tumors
#
#{{{
#m<-meta[ !(failed) & !is.na(risk_group) ]
#g<-gns.cnt[, m$bid]
#dds<-DESeqDataSetFromMatrix(countData=g, colData=data.frame(risk_group=factor(m$risk_group), row.names=m$bid), design=~1)
#gns.sf.tumors<-sizeFactors(estimateSizeFactors(dds, type='poscounts'))
#gns.vs.tumors<-assay(varianceStabilizingTransformation(dds, fitType='local'))
#rm(dds)
# how different are the tumor sizefactors between the methods?
#x<-gns.sf[ names(gns.sf.tumors) ]
#y<-gns.sf.tumors
#summary(x-y)
#
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# -0.07566 -0.01798 -0.00850 -0.00619 0.00501 0.04903
# PCA on variance-stabilized (and glog2-transformed) gene counts centered but not scaled
#gns.pca.tumors<-prcomp(t(gns.vs.tumors), center=T, scale.=F)
#gns.ve.tumors<-round(1000 * gns.pca.tumors$sdev^2/sum(gns.pca$sdev^2))/10
#}}}
# summarize circRNA counts at the gene level
# force size factors to be those based from gene counts
# variance-stabilize
# PCA on variance-stabilized (and glog2-transformed) counts centered by not scaled
crs.cnt<-data.table(data.frame(mcols(circ)[, c('gene_id', 'jc_count', 'bid')]))[, .(jc_count=sum(jc_count)), by=.(bid, gene_id)]
crs.cnt<-dcast(crs.cnt, bid ~ gene_id, value.var='jc_count', fun.aggregate=sum)
crs.cnt<-t(as.matrix(data.frame(crs.cnt[, -1], row.names=crs.cnt$bid, check.names=F)))[, meta$bid]
dds<-DESeqDataSetFromMatrix(countData=crs.cnt, colData=data.frame(risk_group=factor(meta$risk_group), cell_model=factor(meta$cell_model), row.names=meta$bid), design=~1)
sizeFactors(dds)<-gns.sf
crs.vs<-assay(varianceStabilizingTransformation(dds, fitType='local'))
crs.pca<-prcomp(t(crs.vs), center=T, scale.=F)
crs.ve<-round(1000 * crs.pca$sdev^2/sum(crs.pca$sdev^2))/10
rm(dds,circ)
# save all including the reference for convenience
save(list=c('hsa', ls(pattern='(gns|crs)\\.'), 'meta', 'CIRCS'), file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/DESeq2_circRNAs+genes_all_libraries.RData')
#}}}
#
# => /fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/DESeq2_circRNAs+genes_all_libraries.RData
# [tumors + cell lines] clustering
# DESeq2 analysis
#{{{
rm(list=ls())
library(GenomicAlignments)
library(rtracklayer)
library(data.table)
library(DESeq2)
library(vsn)
library(RColorBrewer)
library(gplots)
library(ggplot2)
library(ggrepel)
library(pheatmap)
library(openxlsx)
library(extrafont) # first time used need to run: font_import() , to load all for PDF device run: loadfonts(device='pdf')
loadfonts()
# functions
#{{{
gene_results<-function(res=NULL, annot=NULL){
m<-res[ intersect(annot$gene_id, rownames(res)), c('baseMean', 'log2FoldChange')]
m$gene_name<-annot$gene_name[ match( rownames(m), annot$gene_id ) ]
m$col<-annot$col[ match( rownames(m), annot$gene_id ) ]
return(m)
}
do_deseq2<-function(DDS, CT, Type='', condition='Risk', lfcThreshold=0.0, lfcShrinkType='apeglm', include.annot=F, names.trim, ...){
# DDS = DESeqDataSet object of samples belonging to two conditions
# CT = metadata data.frame of only samples belonging to the two conditions
# Type = 'genes' or 'circRNAs' to add to figure/data names when saved
# condition = CT metadata column to use that defines the two conditions so we can look up the corresponding colors
# lfcThreshold = lfcThreshold to use in DESeq2
# lfcShrinkType = which method to use to estimate shrunken MAP log2FC?
# include.annot = shall we annotate special genes in the MAplot?
# names.trim = regular expression to use to trim sample names when plotting, e.g. '-11-R01$', pass '' for no trimming
# N.B. you NEED to define this if you pass down unnamed arguments with ...
# ... = list of plotting parameters for par() and for the y-axis mtext to pass down when doing the MA-plot
# save open graphics devices by start
#DEV_START<-dev.list()
# variance stabilizing transformation including normalization by library size factors glog2-transformed back to counts
VSC<-assay(varianceStabilizingTransformation(DDS, fitType='local', blind=T))
# remove genes that have identical expression throughout the samples
VSC<-VSC[ apply(VSC, 1, function(x){ any(x!=x[1]) }), ]
# PCA on variance-stabilized (and glog2-transformed) counts centered but not scaled
PCA<-prcomp(t(VSC), center=T, scale.=F) # PCA on the samples is done when the samples are the rows
VE<-round(1000 * PCA$sdev^2/sum(PCA$sdev^2))/10
stopifnot( all(rownames(PCA$x)==colnames(VSC)) )
PCA$x<-cbind(PCA$x, CT)
# differential expression analysis
# use Cook's distance to flag outliers but do not replace their values
DDS<-DESeq(DDS, fitType='local', minReplicatesForReplace=Inf, betaPrior=F)
RES<-results(DDS, alpha=0.05, lfcThreshold=lfcThreshold, altHypothesis='greaterAbs', cooksCutoff=T)
RES<-lfcShrink(dds=DDS, coef=tail(resultsNames(DDS), 1), res=RES, type=lfcShrinkType)
RES<-RES[ order(RES$padj, decreasing=F), ]
CND<-paste0(rev(levels(colData(DDS)[, condition])), collapse=' vs ')
x11()
plotDispEsts(DDS) # just to see
dev.off()
# add gene_names
RES$gene_name<-hsa$gene_name[ match(rownames(RES), hsa$gene_id) ]
# MA-plot
x11(width=16, height=16, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
YLIM<-pretty(range(RES$log2FoldChange, na.rm=T))
YLIM<-c(YLIM[1], tail(YLIM, 1))
XLIM<-pretty(range(RES$baseMean, na.rm=T), 5)
XLIM<-c(0.1, tail(XLIM, 1))
if(...length()>0){
dots<-list(...)[[1]] # it is already a list
par(dots$par)
YLAB.LINE<-dots$ylab.line
} else {
par(mar=c(5.0, 7.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2)
YLAB.LINE<-3
}
options(scipen=+20)
plotMA(RES, xlab='', ylab='', main='', ylim=YLIM, xlim=XLIM, cex=1.2, colSig='red3')
mtext(CND, side=3, line=0, padj=+1.5, cex=1.4)
mtext(expression(log[2]('fold change')), side=2, line=YLAB.LINE, padj=-0.2, cex=2.4, las=3)
mtext('Mean expression', side=1, line=4, padj=-0.3, cex=2.4, las=1)
if (include.annot){
r<-gene_results(RES, annot)
points(r$baseMean , r$log2FoldChange, pch=21, lwd=6, col='black', bg=r$col, cex=1.4)
legend('bottomright', legend=r$gene_name, bty='n', lty=0, lwd=0, pch=21, col='black', pt.bg=r$col, pt.cex=1.8, pt.lwd=4, cex=1.2, x.intersp=-0.4, y.intersp=0.4)
}
if(lfcThreshold>0){
abline(h=c(-lfcThreshold, lfcThreshold), lty=1, lwd=4, col='cyan4')
}
#identify(RES$baseMean, RES$log2FoldChange, labels=RES$gene_name, cex=0.7, offset=0.2, xpd=T)
dev.print(device=svg, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/plotMA_', Type, '_', sub(' vs ', '_', CND), '.svg'), width=16, height=16, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(YLIM,XLIM)
dev.off()
# mean-sd plots to see if variance strongly depends on mean
X11(width=12, height=12, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
par(mar=c(5,4,0.1,0.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=1.4, cex.axis=1.4)
meanSdPlot(VSC)
dev.print(device=svg, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/meanSD_', Type, '_', sub(' vs ', '_', CND), '.svg'), width=14, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
dev.off()
# heatmap based on Euclidean distances of variance stabilized (and glog2-transformed) normalized counts
x11(width=14, height=12, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
v<-VSC
ex<-as.data.frame(colData(DDS)[, condition, drop=F])
stopifnot( all.equal( rownames(ex), rownames(CT) ) )
cl<-setNames(list(setNames( unique(CT$col), unique(CT[, condition, drop=T]) )), colnames(ex))
if(names.trim!=''){
colnames(v)<-sub(names.trim, '', colnames(v))
rownames(ex)<-sub(names.trim, '', rownames(ex))
}
d<-dist(t(v), method='euclidean')
hc<-hclust(d, method='ward.D2')
ph<-pheatmap(as.matrix(d), color=colorRampPalette(brewer.pal(9,'GnBu'))(20), border_color=NA, scale='none',
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=T, show_colnames=T,
display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize=18, fontsize_row=18, fontsize_col=18, fontsize_number=10)
dev.print(device=svg, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_', Type, '_vsc_', sub(' vs ', '_', CND), '.svg'), width=16, height=16, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(v,d,cl,ex,hc,ph)
dev.off()
# heatmap based on Spearman correlations of raw counts
x11(width=14, height=12, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
a<-assay(DDS)
ex<-as.data.frame(colData(DDS)[, condition, drop=F])
stopifnot( all.equal( rownames(ex), rownames(CT) ) )
cl<-setNames(list(setNames( unique(CT$col), unique(CT[, condition, drop=T]) )), colnames(ex))
if(names.trim!=''){
colnames(a)<-sub(names.trim, '', colnames(a))
rownames(ex)<-sub(names.trim, '', rownames(ex))
}
d<-cor(a, method='spearman', use='pairwise.complete.obs')
hc<-hclust(as.dist(1-d), method='ward.D2')
ph<-pheatmap(as.matrix(d), color=rev(colorRampPalette(brewer.pal(9,'RdBu'))(20)), border_color=NA, scale='none',
breaks=seq(-1, 1, length.out=21),
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=T, show_colnames=T,
#display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize=18, fontsize_row=18, fontsize_col=18, fontsize_number=10)
dev.print(device=svg, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_', Type, '_cor_', sub(' vs ', '_', CND), '.svg'), width=16, height=16, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(a,d,cl,ex,hc,ph)
dev.off()
# PCA based on variance-stabilized, glog2-transformed, centered but not scaled counts
XLIM<-pretty(range(PCA$x[, 'PC1']), 5)
XLIM<-c(XLIM[1], XLIM[length(XLIM)])
YLIM<-pretty(range(PCA$x[, 'PC2']), 5)
YLIM<-c(YLIM[1], YLIM[length(YLIM)])
x11(width=12, height=12, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
x<-PCA$x[, c('PC1', 'PC2', condition, 'col')]
x[, condition]<-factor(x[, condition], levels=unique(x[, condition]))
if(names.trim!=''){
rownames(x)<-sub(names.trim, '', rownames(x))
}
print(
ggplot(x, aes(PC1, PC2, color=get(condition))) +
geom_point(size=10) +
#geom_text_repel(aes(label=rownames(x)), size=10, box.padding=0.5, segment.size=1.0, min.segment.length=1.0) +
scale_shape_manual(values=18) +
scale_fill_manual(name='sample', values=unique(x$col)) +
scale_color_manual(name='sample', values=unique(x$col)) +
theme(text = element_text(family='Arial'), axis.text.x=element_text(size=34), axis.title.x=element_text(size=34),
axis.title.y=element_text(size=34), axis.text.y=element_text(size=34),
legend.text=element_text(size=22), legend.title=element_text(size=22, face='plain'), aspect.ratio=1, panel.background = element_blank(),
axis.line.x=element_line(size=0.2, colour = 'black', linetype='solid'),
axis.line.y=element_line(size=0.2, colour = 'black', linetype='solid'),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
scale_x_continuous(lim=XLIM, breaks=pretty(XLIM,5)) +
scale_y_continuous(lim=YLIM, breaks=pretty(YLIM,5)) +
xlab(paste0('PC1: ', VE[1], '% variance')) + ylab(paste0('PC2: ', VE[2], '% variance'))
)
dev.print(device=svg, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/plotPCA_', Type, '_vsc_', sub(' vs ', '_', CND), '.svg'), width=16, height=16, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(XLIM,YLIM)
dev.off()
# save DESeq results along with gene counts
options(scipen=0)
save(DDS,CND,RES,VSC,PCA,VE, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/DESeq2_', Type, '_', sub(' vs ', '_', CND), '.RData'), compress=T)
# convert to table
x<-RES
x$gene_id<-rownames(x)
x<-x[, c('gene_id', 'gene_name', 'baseMean', 'log2FoldChange', 'pvalue', 'padj')]
rownames(x)<-NULL
x<-as.data.frame(x)
write.table(x, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/DESeq2_', Type, '_', sub(' vs ', '_', CND), '.tsv'), quote=F, sep='\t', row.names=F, col.names=T)
write.xlsx(x, file=paste0('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/DESeq2_', Type, '_', sub(' vs ', '_', CND), '.xlsx'), row.names=F, col.names=T, sheetName=gsub(' ', '_', CND))
rm(x)
#readline("Hit ENTER to close all figures: ")
#for (n in setdiff(dev.list(), DEV_START)){ dev.off(n) }
return(list(dds=DDS, res=RES, cnd=CND))
}
#}}}
# load pre-prepared counts etc. for all samples
load('/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/DESeq2_circRNAs+genes_all_libraries.RData')
# [tumors, circRNAs + genes] clustering and PCA
#{{{
# [tumors, circRNAs] heatmap based on Euclidean distance of variance stabilized (and glog2-transformed) counts
x11(width=14, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
m<-meta[!is.na(risk_group), ]
x<-crs.vs[, m$bid]
colnames(x)<-sub('-11-R01', '', colnames(x))
d<-dist(t(x), method='euclidean')
hc<-hclust(d, method='ward.D2')
ex<-data.frame(Type=factor(m$risk_group, exclude=F), row.names=colnames(x))
cl<-setNames(list(setNames( unique(m$col), unique(m$risk_group) )), colnames(ex))
ph<-pheatmap(as.matrix(d), color=colorRampPalette(brewer.pal(9,'GnBu'))(20), border_color=NA, scale='none',
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=T, show_colnames=T,
display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize = 12, fontsize_row=12, fontsize_col=12, fontsize_number=5.0)
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_circRNAs_vsc_tumors.svg', width=14, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(m,x,d,ex,hc,ph,cl)
dev.off()
# [tumors, genes] heatmap based on Euclidean distance of variance stabilized (and glog2-transformed) counts
x11(width=14, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
m<-meta[!is.na(risk_group), ]
x<-gns.vs[, m$bid]
colnames(x)<-sub('-11-R01', '', colnames(x))
d<-dist(t(x), method='euclidean')
hc<-hclust(d, method='ward.D2')
ex<-data.frame(Type=factor(m$risk_group, exclude=F), row.names=colnames(x))
cl<-setNames(list(setNames( unique(m$col), unique(m$risk_group) )), colnames(ex))
ph<-pheatmap(as.matrix(d), color=colorRampPalette(brewer.pal(9,'GnBu'))(20), border_color=NA, scale='none',
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=T, show_colnames=T,
display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize = 12, fontsize_row=12, fontsize_col=12, fontsize_number=5.0)
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_genes_vsc_tumors.svg', width=14, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(m,x,d,ex,hc,ph,cl)
dev.off()
# [tumors, circRNAs] heatmaps based on Spearman correlations of raw counts
x11(width=14, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
m<-meta[!is.na(risk_group), ]
x<-crs.cnt[, m$bid]
colnames(x)<-sub('-11-R01', '', colnames(x))
d<-cor(x, method='spearman', use='pairwise.complete.obs')
hc<-hclust(as.dist(1-d), method='ward.D2')
ex<-data.frame(Type=factor(m$risk_group, exclude=F), row.names=colnames(x))
cl<-setNames(list(setNames( unique(m$col), unique(m$risk_group) )), colnames(ex))
ph<-pheatmap(as.matrix(d), color=rev(colorRampPalette(brewer.pal(9,'RdBu'))(20)), border_color=NA, scale='none',
breaks=seq(0, 1, length.out=21),
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=T, show_colnames=T,
#display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize = 12, fontsize_row=12, fontsize_col=12, fontsize_number=5.0)
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_circRNAs_cor_tumors.svg', width=14, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(m,x,d,ex,hc,ph,cl)
dev.off()
# [tumors, genes] heatmaps based on Spearman correlations of raw counts
x11(width=14, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
m<-meta[!is.na(risk_group), ]
x<-gns.cnt[, m$bid]
colnames(x)<-sub('-11-R01', '', colnames(x))
d<-cor(x, method='spearman', use='pairwise.complete.obs')
hc<-hclust(as.dist(1-d), method='ward.D2')
ex<-data.frame(Type=factor(m$risk_group, exclude=F), row.names=colnames(x))
cl<-setNames(list(setNames( unique(m$col), unique(m$risk_group) )), colnames(ex))
ph<-pheatmap(as.matrix(d), color=rev(colorRampPalette(brewer.pal(9,'RdBu'))(20)), border_color=NA, scale='none',
breaks=seq(0.7, 1, length.out=21), # reduce range since samples are highly correlated
cluster_rows=hc,
cluster_cols=hc,
#cutree_row=4, cutree_col=4,
annotation_legend=T, annotation_names_row=T, annotation_names_col=T,
annotation_col=ex, annotation_row=ex, annotation_colors=cl,
drop_levels=F, show_rownames=T, show_colnames=T,
#display_numbers=T, number_format='%.1f', number_color='grey39',
fontsize = 12, fontsize_row=12, fontsize_col=12, fontsize_number=5.0)
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/heatmap_genes_cor_tumors.svg', width=14, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(x,m,d,ex,hc,ph,cl)
dev.off()
# [tumors, circRNAs] PCA based on variance stabilized (and log2-transformed) counts centered but not scaled
x11(width=14, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
m<-meta[!is.na(risk_group), ]
x<-crs.vs[, m$bid]
colnames(x)<-sub('-11-R01', '', colnames(x))
p<-prcomp(t(x), center=T, scale.=F)
v<-round(1000 * p$sdev^2/sum(p$sdev^2))/10
n<-cbind( data.frame( p$x[, c('PC1', 'PC2')] ), Type=m$risk_group, bid=colnames(x))
cl<-list('Type'=setNames( unique(m$col), unique(m$risk_group) ))
XLIM<-pretty(range(n[, 'PC1']), 2)
XLIM<-c(XLIM[1], XLIM[length(XLIM)])
YLIM<-pretty(range(n[, 'PC2']), 2)
YLIM<-c(YLIM[1], YLIM[length(YLIM)])
ggplot(n[, c('PC1', 'PC2', 'Type', 'bid')], aes(PC1, PC2, color=Type)) +
geom_point(size=12) +
scale_shape_manual(values=18) +
scale_fill_manual(name='Type', values=cl$Type) +
scale_color_manual(name='Type', values=cl$Type) +
theme(text=element_text(family='Arial'), axis.text.x=element_text(size=34), axis.title.x=element_text(size=34),
axis.title.y=element_text(size=34), axis.text.y=element_text(size=34),
legend.text=element_text(size=22), legend.title=element_text(size=22, face='plain'),
aspect.ratio=1, panel.background = element_blank(),
axis.line.x=element_line(size=0.2, colour = 'black', linetype='solid'),
axis.line.y=element_line(size=0.2, colour = 'black', linetype='solid'),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
scale_x_continuous(lim=XLIM, breaks=pretty(XLIM,5)) +
scale_y_continuous(lim=YLIM, breaks=pretty(YLIM,5)) +
xlab(paste0('PC1: ', v[1], '% variance')) + ylab(paste0('PC2: ', v[2], '% variance'))
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/plotPCA_circRNAs_vsc_tumors.svg', width=14, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(m,x,p,v,n,cl,XLIM,YLIM)
dev.off()
# [tumors, genes] PCA based on variance stabilized glog2-transformed counts centered but not scaled
x11(width=14, height=14, title='', bg='white', type='cairo', pointsize=20, antialias='subpixel', family='Arial')
m<-meta[!is.na(risk_group), ]
x<-gns.vs[, m$bid]
colnames(x)<-sub('-11-R01', '', colnames(x))
p<-prcomp(t(x), center=T, scale.=F)
v<-round(1000 * p$sdev^2/sum(p$sdev^2))/10
n<-cbind( data.frame( p$x[, c('PC1', 'PC2')] ), Type=m$risk_group, bid=colnames(x))
cl<-list('Type'=setNames( unique(m$col), unique(m$risk_group) ))
#XLIM<-pretty(range(n[, 'PC1']), 2)
#XLIM<-c(XLIM[1], XLIM[length(XLIM)])
#YLIM<-pretty(range(n[, 'PC2']), 2)
#YLIM<-c(YLIM[1], YLIM[length(YLIM)])
XLIM<-c(-150, 250) # force limits
YLIM<-c(-150, 100) # force limits
ggplot(n[, c('PC1', 'PC2', 'Type', 'bid')], aes(PC1, PC2, color=Type)) +
geom_point(size=12) +
scale_shape_manual(values=18) +
scale_fill_manual(name='Type', values=cl$Type) +
scale_color_manual(name='Type', values=cl$Type) +
theme(text=element_text(family='Arial'), axis.text.x=element_text(size=34), axis.title.x=element_text(size=34),
axis.title.y=element_text(size=34), axis.text.y=element_text(size=34),
legend.text=element_text(size=22), legend.title=element_text(size=22, face='plain'),
aspect.ratio=1, panel.background = element_blank(),
axis.line.x=element_line(size=0.2, colour = 'black', linetype='solid'),
axis.line.y=element_line(size=0.2, colour = 'black', linetype='solid'),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
scale_x_continuous(lim=XLIM, breaks=seq(XLIM[1], XLIM[2], length.out=5)) +
scale_y_continuous(lim=YLIM, breaks=seq(YLIM[1], YLIM[2], length.out=6)) +
xlab(paste0('PC1: ', v[1], '% variance')) + ylab(paste0('PC2: ', v[2], '% variance'))
dev.print(device=svg, file='/fast/projects/peifer_wgs/work/work/2017-11-08_Fuchs_totalRNAseq/raw/unified/figures/plotPCA_genes_vsc_tumors.svg', width=14, height=14, bg='white', antialias='subpixel', pointsize=20, family='Arial')
rm(m,x,p,v,n,cl,XLIM,YLIM)
dev.off()
#}}}
# [tumors, circRNAs] all pairwise DE analyses
#{{{
# [MNA vs HR_nMNA] DESeq2 forcing library sizes from the gene counts and including sex in the design
m<-meta[risk_group %in% c('MNA', 'HR_nMNA') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'HR_nMNA') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 7.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=3))
rm(m,M,CT,DDS)
# [HR_nMNA vs IMR] DESeq2 forcing library sizes from the gene counts and including sex in the design
m<-meta[risk_group %in% c('IMR', 'HR_nMNA') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'IMR') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
# [HR_nMNA vs LR] DESeq2 forcing library sizes from the gene counts and including sex in the design
m<-meta[risk_group %in% c('LR', 'HR_nMNA') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'LR') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
# [IMR vs LR] DESeq2 forcing library sizes from the gene counts and including sex in the design
m<-meta[risk_group %in% c('LR', 'IMR') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'LR') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
# [LR vs ST4S] DESeq2 forcing library sizes from the gene counts and including sex in the design
m<-meta[risk_group %in% c('ST4S', 'LR') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'LR') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
#}}}
# [tumors, genes] all pairwise DE analyses
#{{{
# [MNA vs HR_nMNA] DESeq2 including sex in the design
m<-meta[risk_group %in% c('MNA', 'HR_nMNA') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'HR_nMNA') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 7.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=3))
rm(m,M,CT,DDS)
# [HR_nMNA vs IMR] DESeq2 including sex in the design
m<-meta[risk_group %in% c('IMR', 'HR_nMNA') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'IMR') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 7.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=3))
rm(m,M,CT,DDS)
# [HR_nMNA vs LR] DESeq2 including sex in the design
m<-meta[risk_group %in% c('LR', 'HR_nMNA') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'LR') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 7.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=3))
rm(m,M,CT,DDS)
# [IMR vs LR] DESeq2 including sex in the design
m<-meta[risk_group %in% c('LR', 'IMR') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'LR') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 7.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=3))
rm(m,M,CT,DDS)
# [ST4S vs LR] DESeq2 including sex in the design
m<-meta[risk_group %in% c('ST4S', 'LR') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('sex', 'risk_group', 'col'), with=F], row.names=m$bid)
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(risk_group=factor(CT$risk_group), sex=factor(CT$sex), row.names=rownames(CT)), design=~sex+risk_group)
DDS$risk_group<-relevel(DDS$risk_group, 'LR') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='risk_group', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='-11-R01$', list(par=list(mar=c(5.0, 7.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=3))
rm(m,M,CT,DDS)
#}}}
# [cell models, circRNAs] DE analyses
#{{{
# [MYCN +Tet 4h vs ETOH 4h] DESeq2 forcing library sizes from the gene counts
m<-meta[grepl('CB-SKNAS-TR-MYCN', bid) & treatment %in% c('ETOH 4h', '+Tet 4h') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('cell_model', 'treatment', 'col'), with=F], row.names=m$bid)
colnames(CT)<-c('cell_model', 'Treatment', 'col')
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(Treatment=factor(CT$Treatment), row.names=rownames(CT)), design=~Treatment)
DDS$Treatment<-relevel(DDS$Treatment, 'ETOH 4h') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='Treatment', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
# [MYCN +Tet 48h vs ETOH 48h] DESeq2 forcing library sizes from the gene counts
m<-meta[grepl('CB-SKNAS-TR-MYCN', bid) & treatment %in% c('ETOH 48h', '+Tet 48h') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('cell_model', 'treatment', 'col'), with=F], row.names=m$bid)
colnames(CT)<-c('cell_model', 'Treatment', 'col')
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(Treatment=factor(CT$Treatment), row.names=rownames(CT)), design=~Treatment)
DDS$Treatment<-relevel(DDS$Treatment, 'ETOH 48h') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='Treatment', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
# [MYCN +Tet 48h vs +Tet 4h ] DESeq2 forcing library sizes from the gene counts
m<-meta[grepl('CB-SKNAS-TR-MYCN', bid) & treatment %in% c('+Tet 4h', '+Tet 48h') ]
M<-crs.cnt[, m$bid ]
CT<-data.frame(m[, c('cell_model', 'treatment', 'col'), with=F], row.names=m$bid)
colnames(CT)<-c('cell_model', 'Treatment', 'col')
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(Treatment=factor(CT$Treatment), row.names=rownames(CT)), design=~Treatment)
DDS$Treatment<-relevel(DDS$Treatment, '+Tet 4h') # define first level so that the comparison log2(level 2/level 1) is done
sizeFactors(DDS)<-gns.sf[ colnames(DDS) ]
d<-do_deseq2(DDS=DDS, CT=CT, Type='circRNAs', condition='Treatment', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
#}}}
# [cell models, genes] DE analyses
#{{{
# [MYCN +Tet 4h vs ETOH 4h] DESeq2
m<-meta[grepl('CB-SKNAS-TR-MYCN', bid) & treatment %in% c('ETOH 4h', '+Tet 4h') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('cell_model', 'treatment', 'col'), with=F], row.names=m$bid)
colnames(CT)<-c('cell_model', 'Treatment', 'col')
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(Treatment=factor(CT$Treatment), row.names=rownames(CT)), design=~Treatment)
DDS$Treatment<-relevel(DDS$Treatment, 'ETOH 4h') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='Treatment', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
# [MYCN +Tet 48h vs ETOH 48h] DESeq2
m<-meta[grepl('CB-SKNAS-TR-MYCN', bid) & treatment %in% c('ETOH 48h', '+Tet 48h') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('cell_model', 'treatment', 'col'), with=F], row.names=m$bid)
colnames(CT)<-c('cell_model', 'Treatment', 'col')
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(Treatment=factor(CT$Treatment), row.names=rownames(CT)), design=~Treatment)
DDS$Treatment<-relevel(DDS$Treatment, 'ETOH 48h') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='Treatment', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
# [MYCN +Tet 48h vs +Tet 4h ] DESeq2
m<-meta[grepl('CB-SKNAS-TR-MYCN', bid) & treatment %in% c('+Tet 4h', '+Tet 48h') ]
M<-gns.cnt[, m$bid ]
CT<-data.frame(m[, c('cell_model', 'treatment', 'col'), with=F], row.names=m$bid)
colnames(CT)<-c('cell_model', 'Treatment', 'col')
DDS<-DESeqDataSetFromMatrix(countData=M, colData=data.frame(Treatment=factor(CT$Treatment), row.names=rownames(CT)), design=~Treatment)
DDS$Treatment<-relevel(DDS$Treatment, '+Tet 4h') # define first level so that the comparison log2(level 2/level 1) is done
d<-do_deseq2(DDS=DDS, CT=CT, Type='genes', condition='Treatment', lfcThreshold=0.0, lfcShrinkType='normal', include.annot=F, names.trim='', list(par=list(mar=c(5.0, 8.0, 1.0, 2.5), mgp=c(3,1,0), oma=c(0,0,0,0), las=1, xpd=F, bty='n', cex.lab=2.4, cex.axis=2.4, tcl=-0.2), ylab.line=4))
rm(m,M,CT,DDS)
#}}}
#}}}
# [tumors + cell lines] process of all the DESeq2 results into HTML-table-ready objects
# do MSigDB C2 enrichment analysis
# do MSigDB GSEA analysis
#
# combine the MNA vs HR_nMNA, MYCN +-Tet 4h, MYCN +- 48h, MYCN +- 120h results
#
#{{{
# [MNA vs HR_nMNA, circRNAs+genes] enrichment in MYCN targets