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rnaseq.r
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rnaseq.r
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options(width=222)
options(warnings = -1)
library(log4r)
logfile = paste0('_', resDir, '_log')
create.logger() -> logger
logfile(logger) = logfile
level(logger) = 'INFO'
library(data.table)
library(DESeq2)
library(gplots);
library(pheatmap);
library(ggplot2)
library(tximport)
library(readxl)
library(survival)
library(Cairo)
## target file
## base: sample ID
## input: folder that contain fastq files
## fastq1.file full path
## fastq2.file full path
## group
fread(target.file) -> target
target[, rsem.jobname := paste0(base, ".rsem")]
target[, rsem.cmd := bsub.head(rsem.jobname, mem=55, cpu = 9)]
target[, rsem.cmd := paste0(rsem.cmd, " \"", RSEM, "/rsem-calculate-expression --paired-end -p 8 --star --star-path ", STAR_DIR, " --gzipped-read-file --append-names --estimate-rspd --output-genome-bam ")]
target[, rsem.cmd := paste0(rsem.cmd, fastq1.file, " ", fastq2.file, " ", RSEM_REF, " ", input, "/", base, " \"")]
target[, rsem.cmd]
exe.jobs(target$rsem.cmd, logger)
## bam files
target[, bamfile := paste0(input, '/', base, '.bam', )]
wait4jobs(target$rsem.jobname, logger)
## RSeQC geneBody_coverage.py
bamfiles = paste(target$bamfile, collapse=',')
rseqc.jobname = paste0(cfg$pre, ".rseqc")
rseqc.cmd = bsub.head(rseqc.jobname, mem=55, cpu = 9, postdone=paste(target$rsem.jobname, collapse=' '))
rseqc.cmd = paste0(rseqc.cmd, " \"", cfg$anaconda2, '/bin/geneBody_coverage.py ')
rseqc.cmd = paste0(rseqc.cmd, " -i ", bamfiles, ' -r ', cfg$hsa.housekeeping.bed, ' -o ', cfg$rseqcDir)
rseqc.cmd = paste0(rseqc.cmd, " \"")
rseqc.cmd
exe.jobs(rseqc.cmd, logger)
## fastqc
fastqc.jobname = paste0("fastqc")
fastq = paste(c(target$R1, target$R2), collapse=" ")
fastqc.cmd = bsub.head(fastqc.jobname, mem=10, cpu=20, postdone=paste(target$rsem.jobname, collapse=' '))
fastqc.cmd = paste0(fastqc.cmd, " \"", cfg$fastqc, " -o ", cfg$fastqcDir, " -j ", cfg$java, " -f fastq -t 20 ", fastq, " \"")
fastqc.cmd
exe.jobs(fastqc.cmd, logger)
##
target[, countfile := paste0(cfg$initDir, '/', base, '.genes.results')]
file.exists(target$countfile)
##
target[, countfile := paste0(input, '/', base, '.genes.results')]
file.exists(target$countfile)
## import reads
rsem = tximport(target$countfile, type = "rsem")
rsem$counts -> rsem
mode(rsem) = 'integer'
colnames(rsem) = target$base
head(rsem)
design = data.frame(
row.names = colnames(target$ID),
condition = target$condition,
libType = rep("PE", nrow(target)));
ddsmat = DESeqDataSetFromMatrix(countData = rsem,
colData = design,
design = ~ condition);
dds.ds <- estimateSizeFactors(ddsmat);
dds <- DESeq(dds.ds, parallel=T);
resultName(dds)
res = results(dds, contrast = c('condition', 'cond1', 'cond2'))
## export data
source("~/program/fun/write_rnk.r")
rnkfile = ''
write_rnk(data = x, filename=rnkfile, pvalue.cutoff=1)
source("~/program/fun/run_gsea.R")
run_gsea(rnkfile )
## gsea summary
system('find gsea -name "*gsea_report_for*.xls" > gsea.list')
scan('gsea.list', character()) -> fl
fl[grep('h.all', fl)]
fl
lapply(fl, fread) -> fl.list
names(fl.list) = fl
as.data.table(rbindlist(fl.list, idcol = TRUE)) -> fl.dt
fl.dt[grep('rt4', .id), cell := 'RT4']
fl.dt[grep('mghu3', .id), cell := 'MGHU3']
fl.dt[grep('gata3', .id), shrna := 'GATA3']
fl.dt[grep('foxa1', .id), shrna := 'FOXA1']
fl.dt[grep('_neg_', .id), updn := 'DN']
fl.dt[grep('_pos_', .id), updn := 'UP']
fl.dt[, geneset := sub(".*rnk.(.*).v5.0.symbols.gmt.G.*", "\\1", .id)]
fl.dt[grep('h.all', .id), geneset := 'Hallmark']
fl.dt[, qval := `FDR q-val`]
fl.dt[, cell := factor(cell, levels=c('RT4', 'MGHU3'))]
fl.dt[, name := NAME]
fl.d[, name := sub("HALLMARK_", "", NAME)]
fl.dt[, name := gsub("_", " ", name)]
fl.dt[, cellshrna := paste0(cell, ', shRNA ', shrna)]
fl.dt
for(gs.name in unique(fl.dt$geneset)){
tmp = fl.dt[grep(gs.name, geneset),][qval < 0.001 & abs(NES) > 2,]
gg = ggplot(tmp, aes(x=reorder(name, NES), y=NES, group = name)) + geom_jitter(width=0.2, height=.05) +
theme(axis.text.x = element_text(angle=-90, vjust = 0.5, hjust=0)) +
ylab("NES") + xlab("Gene set names") + coord_flip()
ggsave(gg, file=paste0('res/gsea_', gs.name, '.pdf'), width=7, height=8)
}
sync('res')
source('~/program/fun/sync.r')
sync('gsea')
mtxfile = ''
write.table(x, file = mtxfile, sep="\t", quote=F)
res.cc = counts(dds, normalized=T);
anno.col = data.frame()
anno.row = data.frame()
row.sel = anno.row[]
col.sel = anno.col[]
breaks = c( seq(from = -2, to = 2, length.out = 30))
breaks = unique(breaks)
col = greenred(length(breaks + 1))
rld = rlog(dds, blind = F)
res.cc[row.sel, col.sel] -> mtx
cn = colnames(mtx)
mtx = t(apply(mtx, 1, scale))
colnames(mtx) = cn
mtx[mtx > 2] = 2
mtx[mtx < -2] = -2
heatmapfile = paste0()
pdf(heatmapfile, width=10, height=6)
hp = pheatmap(mtx, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = F, fontsize_row = 6,
annotation_col = anno.row, annotation_row = anno.col,
dev.off()
## venn diagram
source("~/program/vennDia.R")
venfile = paste0()
pdf(venfile, width=6, height=6)
venndiagram(x = xx, y = yy, type= '2', labels = c("", ""))
dev.off()
## export counts table
## geo submission need this
write.table(res.cc, file='res/counts_table.tsv', sep="\t", quote=F)
## tcga RNA-seq
load("../bcg/tcga_blca.RData");
blca.rows$id = paste(blca.rows$ensembl_gene_id, blca.rows$external_gene_name, sep = "_")
blca_count_symbol = blca.assay
row.names(blca_count_symbol) = blca.rows[row.names(blca_count_symbol), "id"]
blca_count_symbol[1:10, 1:2]
## pca analysis
pcafile = paste0()
pdf(pcafile)
rd = plotPCA(rld, intgroup = 'condition', returnData = T)
dev.off()
pcafile = paste0()
pdf(pcafile, width=8, height=6)
ggplot(rd, aes(PC1, PC2, label = samp_class, color=samp_class)) + geom_point() + theme(legend.position="right")
dev.off()
pcafile = paste0()
percentvar = round(100 * attr(rd, 'percentVar'))
pdf('res/pca_2.pdf')
ggplot(rd, aes(x = PC1, y = PC2, color = condition, shape = condition)) +
geom_point(size=3) +
xlab(paste0("PC1: ", percentvar[1], "% variance")) +
ylab(paste0("PC2: ", percentvar[2], "% variance")) +
coord_fixed()
dev.off()
# PCA by using top 100 genes in bladder from https://www.gtexportal.org/home/
tmp = prcomp(t(res.cc))
tmp = tmp$x
tmp = as.data.frame(tmp)
pcafile = paste0()
pdf(pcafile, width=8, height=6)
p = ggplot(tmp, aes(PC1, PC2, color=samp_class)) + geom_point() + theme(legend.position="right"); print(p)
dev.off()
##
load("../bcg/uc_basal_luminal_marks.RData")
setdiff(base47, c(base47.luminal, base47.basal))
colnames(tcga_shen_cc_log)
anno_col = data.frame(row.names = colnames(tcga_shen_cc_log),
Tumor_Stage = c(blca.data$tumor_stage, rep("organoids", ncol(rsem_shen$counts))),
Definition = c(blca.data$definition, rep("organoids", ncol(rsem_shen$counts))),
stringsAsFactors = F)
dim(anno_col)
anno_col$samp_class= "TCGA BLCA"
anno_col$samp_class[grep("Org", row.names(anno_col))] = "organoid"
anno_col$samp_class[grep("tumor", row.names(anno_col))] = "tumor"
tail(anno_col)
tcga_sc_id = c('TCGA-BT-A0YX-01A', 'TCGA-BT-A20U-01A', 'TCGA-BT-A2LD-01A', 'TCGA-C4-A0F1-01A', 'TCGA-C4-A0F7-01A', 'TCGA-CU-A0YN-01A',
'TCGA-DK-A2I2-01A', 'TCGA-FD-A3B5-01A', 'TCGA-FD-A3N5-01A', 'TCGA-G2-A2ES-01A', 'TCGA-G2-A3IB-01A', 'TCGA-GC-A3I6-01A',
'TCGA-GD-A3OS-01A', 'TCGA-BT-A42E', 'TCGA-GU-A42Q', 'TCGA-FD-A43Y', 'TCGA-FD-A5BU', 'TCGA-K4-A4AC', 'TCGA-FD-A5BY', 'TCGA-PQ-A6FI',
'TCGA-GU-A766', 'TCGA-CU-A72E', 'TCGA-E7-A7XN', 'TCGA-XF-A8HE', 'TCGA-YC-A89H', 'TCGA-E7-A97P', 'TCGA-XF-A8HH', 'TCGA-ZF-A9RE',
'TCGA-ZF-AA4W', 'TCGA-4Z-AA80', 'TCGA-4Z-AA82', 'TCGA-4Z-AA89', 'TCGA-XF-A9SJ', 'TCGA-XF-A9T4', 'TCGA-XF-A9T8', 'TCGA-ZF-AA53',
'TCGA-XF-AAME', 'TCGA-XF-AAMH', 'TCGA-XF-AAMT', 'TCGA-XF-AAN2', 'TCGA-XF-AAN5', 'TCGA-ZF-A9RD', 'TCGA-ZF-A9RG', 'TCGA-BT-A20X-01A',
'TCGA-FD-A3B4-01A', 'TCGA-FD-A3N6-01A')
tcga_sc_id = sub("-01A", "", tcga_sc_id)
tcga_sc_id
tcga_basal_id = c('TCGA-DK-A3WW', 'TCGA-SY-A9G5', 'TCGA-DK-A2I4', 'TCGA-E7-A7XN', 'TCGA-XF-A9T8', 'TCGA-XF-A9T5', 'TCGA-ZF-AA4V', 'TCGA-BT-A0YX', 'TCGA-DK-AA6S',
'TCGA-UY-A9PB', 'TCGA-4Z-AA7W', 'TCGA-4Z-AA84', 'TCGA-BT-A3PJ', 'TCGA-XF-A8HD', 'TCGA-ZF-AA4W', 'TCGA-BT-A20J', 'TCGA-FD-A3SS', 'TCGA-XF-AAN2',
'TCGA-XF-A9SY', 'TCGA-DK-A3IU', 'TCGA-G2-A2ES', 'TCGA-XF-A8HE', 'TCGA-XF-A9T3', 'TCGA-DK-A3IN', 'TCGA-DK-AA6L', 'TCGA-FD-A3SN', 'TCGA-FD-A3SO',
'TCGA-FD-A5BX', 'TCGA-GC-A3RC', 'TCGA-XF-A9SI', 'TCGA-HQ-A5NE', 'TCGA-K4-A5RJ', 'TCGA-K4-A6FZ', 'TCGA-BT-A3PK', 'TCGA-DK-AA6R', 'TCGA-FD-A3N5',
'TCGA-E7-A7DV', 'TCGA-UY-A8OB', 'TCGA-XF-AAN5', 'TCGA-XF-A9T6', 'TCGA-XF-A9SJ', 'TCGA-UY-A78P', 'TCGA-G2-A2EJ', 'TCGA-DK-A2I6', 'TCGA-FD-A3B6',
'TCGA-G2-A2EF', 'TCGA-XF-A9SM', 'TCGA-GC-A3YS', 'TCGA-ZF-A9RF', 'TCGA-CF-A1HS', 'TCGA-G2-AA3C', 'TCGA-4Z-AA7Q', 'TCGA-CU-A3KJ', 'TCGA-GC-A6I1',
'TCGA-DK-AA74', 'TCGA-E5-A2PC', 'TCGA-4Z-AA86', 'TCGA-4Z-AA81', 'TCGA-ZF-AA58', 'TCGA-DK-A1AB', 'TCGA-UY-A78L', 'TCGA-E7-A97P', 'TCGA-GU-A766',
'TCGA-ZF-AA54', 'TCGA-K4-A5RH', 'TCGA-GU-A42Q', 'TCGA-ZF-A9RN', 'TCGA-BT-A42F', 'TCGA-GC-A3I6', 'TCGA-BT-A20O', 'TCGA-ZF-AA53', 'TCGA-C4-A0F1',
'TCGA-BT-A42E', 'TCGA-ZF-A9RD', 'TCGA-GU-AATQ', 'TCGA-FD-A3NA', 'TCGA-FT-A61P', 'TCGA-UY-A9PA', 'TCGA-FD-A3B3', 'TCGA-XF-AAN3', 'TCGA-ZF-AA4N',
'TCGA-CU-A0YN', 'TCGA-FD-A6TH', 'TCGA-BL-A5ZZ', 'TCGA-GV-A40E', 'TCGA-GC-A3WC', 'TCGA-K4-A4AC', 'TCGA-PQ-A6FI', 'TCGA-FD-A3B5', 'TCGA-FD-A5C1',
'TCGA-FD-A6TD', 'TCGA-LC-A66R', 'TCGA-K4-A5RI', 'TCGA-FD-A3SP', 'TCGA-GU-A764', 'TCGA-E7-A3X6', 'TCGA-FT-A3EE', 'TCGA-DK-A6B5', 'TCGA-DK-A2I2',
'TCGA-FD-A6TK', 'TCGA-DK-A1AE', 'TCGA-FD-A3N6', 'TCGA-FD-A5BT', 'TCGA-DK-AA6Q', 'TCGA-4Z-AA82', 'TCGA-BT-A2LD', 'TCGA-GD-A3OQ', 'TCGA-GU-A762',
'TCGA-CU-A72E', 'TCGA-ZF-AA56', 'TCGA-BL-A13I', 'TCGA-UY-A8OC', 'TCGA-XF-A9SX', 'TCGA-XF-AAMT', 'TCGA-BT-A20V', 'TCGA-G2-A3IB', 'TCGA-FD-A62N',
'TCGA-HQ-A5ND', 'TCGA-C4-A0F0', 'TCGA-GV-A3QG', 'TCGA-DK-A3IM', 'TCGA-BT-A20U', 'TCGA-BT-A20X', 'TCGA-PQ-A6FN', 'TCGA-FD-A3B7', 'TCGA-FD-A5BU',
'TCGA-FD-A62S', 'TCGA-DK-AA6M', 'TCGA-FD-A3B4', 'TCGA-ZF-AA5H', 'TCGA-FD-A43Y', 'TCGA-ZF-AA5N', 'TCGA-FD-A5BY', 'TCGA-DK-A3WX', 'TCGA-XF-A9T4',
'TCGA-XF-AAN4', 'TCGA-XF-AAMW', 'TCGA-FD-A3B8', 'TCGA-FD-A5BS', 'TCGA-XF-AAME', 'TCGA-DK-A3WY', 'TCGA-XF-AAN8');
anno_col$SC = "NOT"
anno_col$SC[434:474] = "organoids"
anno_col$SC[substr(row.names(anno_col), 1, 12) %in% tcga_sc_id] = "SC"
anno_col$SC[substr(row.names(anno_col), 1, 12) %in% tcga_basal_id] = "Basal"
anno_col$SC[substr(row.names(anno_col), 1, 12) %in% intersect(tcga_sc_id, tcga_basal_id)] = "Basal & SC"
table(anno_col$SC)
unique(anno_col$Definition)
load("../../genelist/early117.RData")
read.table("../../genelist/early_117_subset.txt", head=F) -> early.subset
colnames(early.subset) = c('class', 'genesymbol')
early.subset
gs = row.names(tcga_shen_cc_log)
gs = sub(".*_", "", gs)
anno_row = data.frame(row.names=row.names(tcga_shen_cc_log),
BASE47 = rep("NOT", nrow(tcga_shen_cc_log)), stringsAsFactors = F);
anno_row$BASE47[gs %in% base47.luminal] = "Luminal";
anno_row$BASE47[gs %in% base47.basal] = "Basal";
anno_row$McConkey = "NOT"
anno_row$McConkey[gs %in% mcconkey.luminal] = "Luminal"
anno_row$McConkey[gs %in% mcconkey.basal] = "Basal"
anno_row$Early117 = "NOT"
anno_row$Early117[gs %in% early.subset$genesymbol[early.subset$class == 'Class12']] = "Class12"
anno_row$Early117[gs %in% early.subset$genesymbol[early.subset$class == 'Class13']] = "Class13"
anno_row$Early117[gs %in% early.subset$genesymbol[early.subset$class == 'Class2']] = "Class2"
anno_row$sd = apply(tcga_shen_cc_log, MARGIN = 1, FUN = function(x) sd(x) != 0)
head(anno_row$sd)
breaks = c( seq(from = -2, to = 2, length.out = 30))
breaks = unique(breaks)
col = greenred(length(breaks + 1))
## immune cell infiltration
## ssGSEA
immuno_sig =fread("~/program/immuno_sig.txt")
immuno_sig
gg = unique(immuno_sig$CellType)
geneset = list(immuno_sig[CellType == gg[1], Symbol])
for(i in 2:length(gg)){
geneset = c(geneset, list(immuno_sig[CellType == gg[i], Symbol]))
}
names(geneset) = gg
length(geneset)
names(geneset)
geneset
adaptive = c( "Bcells", "Tcells", "Thelpercells", "Tcm", "Th1cells", "Th2cells", "TFH", "Th17cells", "TReg", "CD8Tcells", "Tgd", "Cytotoxiccells")
innate = c( "NKcells", "NKCD45dimencells", "NKCD56brightcells", "DC", "iDC", "aDC", "pDC", "Eosinophils", "Macrophages", "Mastcells", "Neutrophils")
anno_row = data.frame(row.names = names(geneset), ImmuneType = rep("Adaptive", 28), stringsAsFactors = F)
anno_row$ImmuneType[row.names(anno_row) %in% innate] = "Innate"
names(geneset)
head(geneset)
gsva.score = gsva(res.cc, geneset, method="ssgsea")
gsva.score
## significance test of the GSVA score by using limma
library(limma)
cn = colnames(gsva_score)
condition = rep(1, length(cn))
condition[grep('primary', cn)] = 0
condition
design <- model.matrix(~ condition)
design
colnames(design) <- c("ALL", "PDXvsPrimary")
fit <- lmFit(gsva.score, design)
fit <- eBayes(fit)
gsva.res <- topTable(fit, coef="PDXvsPrimary", number=Inf)
gsva.res
summary(decideTests(fit, p.value=0.05))
summary(gsva.res)
gg = ggplot(conv.ssgsea, aes(x=reorder(pop, mean_diff), y=mean_diff, color = Significance, size=-log(pvalue))) + geom_point() +
theme(axis.text.x = element_text(angle=-90, vjust = 0.5, hjust=0)) + ggtitle('Immune cell infilteration \nby ssGSEA (T2 vs T1) ') +
ylab("Mean difference") + xlab("Immune cells") + coord_flip() +
geom_hline(yintercept=0, color=adjustcolor(2, .7))
ggsave(gg, file='rnaseq/res/immune_cell_deconv_ssGSEA.pdf', width=4, height=5)
sync('rnaseq/res')
cn = colnames(gsva_score)
matrix_tmp = t(apply(gsva_score, 1, scale))
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
colnames(matrix_tmp) = cn
breaks = c( seq(from = -2, to = 2, length.out = 30))
pdf("res/heatmap_gsva.pdf")
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 9, show_rownames = T,
annotation_row = anno_row)
dev.off()
sync('res')
gsva_hp = hp
cutree(hp$tree_col, 2) -> gsva_cluster
source("~/program/fun/topn_pheatmap.R")
source("~/program/fun/my_pheatmap.R")
source("~/program/fun/my_scale.R")
source("~/program/fun/top_rows.R")
source("~/program/fun/remove_null_row.R")
source("~/program/fun/consensus_cluster.R")
## heatmap of the immuno sig genes from ssGSEA
gs = sub(".*_", "", row.names(res.cc.log))
res.cc.log[gs %in% immuno_sig$Symbol, ] -> tmp
#my_scale(tmp) -> tmp
cn = colnames(tmp)
matrix_tmp = t(apply(tmp, 1, scale))
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
colnames(matrix_tmp) = cn
breaks = c( seq(from = -2, to = 2, length.out = 30))
remove_null_row(matrix_tmp) -> matrix_tmp
pdf("res/heatmap_immuno_sig.pdf")
hp = pheatmap(tmp, color=greenred(length(breaks)+1), scale='none', clustering_method = 'ward.D2',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 9, show_rownames = F)
dev.off()
## cibersort
tmp = as.data.frame(res.cc)
head(tmp)
gs = sub(".*_", "", row.names(tmp))
gs1 = gs[!duplicated(gs)]
tmp = tmp[!duplicated(gs),]
row.names(tmp) = gs1
head(tmp)
dim(tmp)
rm(gs1, gs)
write.table(tmp, file='tmp.tsv', sep="\t", quote=F)
## add one more column names
##library(Rserve)
##Rserve(args="--no-save")
##cmd = paste0(cfg$java, ' -Xmx5g -Xms3g -jar ', cfg$cibersort, '/CIBERSORT.jar ')
##cmd = paste0(cmd, ' -M tmp.tsv -B ', cfg$cibersort, '/LM22.txt > cibersort.tsv')
##cmd
##exe.jobs(cmd, logger)
source('~/program/cibersort/CIBERSORT.R')
ciber = CIBERSORT(paste0(cfg$cibersort, '/LM22.txt'), 'tmp.tsv', perm=1000, QN=TRUE, absolute=T)
ciber.t = t(ciber)
ciber.t
ciber.t = ciber.t[1:22, ]
tail(ciber.t)
dim(ciber.t)
ciber.t[-1,]
library(limma)
cn = colnames(ciber.t)
condition = rep(1, length(cn))
condition[grep('primary', cn)] = 0
condition
design <- model.matrix(~ condition)
design
colnames(design) <- c("ALL", "PDXvsPrimary")
fit <- lmFit(ciber.t, design)
fit <- eBayes(fit)
summary(decideTests(fit, p.value=0.05))
ciber.res <- topTable(fit, coef="PDXvsPrimary", number=Inf)
ciber.res = as.data.table(ciber.res, keep.rownames=T)
ciber.res[, Significance := 'Not']
ciber.res[P.Value < 0.05, Significance := 'Sig']
ciber.res
save(ciber.res, ciber.t, file='cibersort_score_39_samples.RData')
gg = ggplot(ciber.res, aes(x=reorder(rn, P.Value), y=logFC, color = Significance, size=-log(adj.P.Val))) + geom_point() +
theme(axis.text.x = element_text(angle=-90, vjust = 0.5, hjust=0)) + ggtitle('Immune cell infilteration \nby cibersort (PDX vs Primary) ') +
ylab("logFC") + xlab("Immune cells") + coord_flip() +
geom_hline(yintercept=0, color=adjustcolor(2, .3))
ggsave(gg, file='res/immune_cell_deconv_39_samples_cibersort.pdf', width=4, height=5)
sync('res')
cn = colnames(ciber.t)
matrix_tmp = t(apply(ciber.t, 1, scale))
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
colnames(matrix_tmp) = cn
breaks = c( seq(from = -2, to = 2, length.out = 30))
pdf("res/heatmap_cibersort_score.pdf")
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 9, show_rownames = T,
annotation_row = anno_row)
dev.off()
sync('res')
## heatmap of the immuno sig genes from cibersort
ciber.gs = fread(paste0(cfg$cibersort, '/LM22.txt'))
ciber.gs = ciber.gs$`Gene symbol`
ciber.gs
gs = sub(".*_", "", row.names(res.cc.log))
res.cc.log[gs %in% ciber.gs, ] -> tmp
#my_scale(tmp) -> tmp
cn = colnames(tmp)
matrix_tmp = t(apply(tmp, 1, scale))
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
colnames(matrix_tmp) = cn
breaks = c( seq(from = -2, to = 2, length.out = 30))
remove_null_row(matrix_tmp) -> matrix_tmp
pdf("res/heatmap_immuno_sig_cibersort.pdf")
hp = pheatmap(tmp, color=greenred(length(breaks)+1), scale='none', clustering_method = 'ward.D2',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 9, show_rownames = F)
dev.off()
#blca.cc[c(base47.basal.ensembl, base47.luminal.ensembl),] -> blca_base47
#dim(blca_base47)
#row.names(blca_base47) = blca.rows[row.names(blca_base47), "external_gene_name"]
#anno_row = data.frame(row.names = row.names(blca_base47), BASE47 = rep("luminal", nrow(blca_base47)), stringsAsFactors = F)
#anno_row$BASE47[row.names(anno_row) %in% base47.basal] = "basal"
# add annotation of bladder cluster of 129 samples
anno_col$nat_cl = "NOT"
anno_col$nat_cl[grep("MS0", row.names(anno_col))] = "organoids"
anno_col[row.names(nat2014_cluster), "nat_cl"] = nat2014_cluster[, "cluster"]
table(anno_col$nat_cl)
dim(anno_col)
head(nat2014_cluster)
colnames(anno_col)
unique(anno_col$Tumor_Stage)
unique(anno_col$Definition)
unique(anno_col$SC)
unique(anno_col$nat_cl)
colnames(anno_col)
unique(anno_col$Tumor_Stage)
unique(anno_col$Definition)
unique(anno_col$SC)
unique(anno_col$nat_cl)
colnames(anno_row)
unique(anno_row$BASE47)
unique(anno_row$McConkey)
unique(anno_row$Early117)
unique(anno_col$passage)
del = c("MS020_SuB9_9", "MS029_SuB38_2", 'xx', 'yy')
anno_col$del = 0
anno_col$del[row.names(anno_col) %in% del] = 1
#anno_color
ann_colors = list(
mark = c(basal = "#1B9E77", luminal = "#D95F02"),
#type = c(primary = "#7570B3", secondary = "#E7298A", Shen = "#02427D", BLCA= "#66A61E"),
type = c(Shen = "#02427D", BLCA= "#66A61E"),
BASE47 = c(NOT="#cde6ec", Luminal="#E7298A", Basal="#02427D"),
McConkey = c(NOT="#cde6ec", Luminal="#E7298A", Basal="#02427D"),
Early117 = c(NOT="#cde6ec", Class2="#E7298A", Class12="#02427D", Class13="#299d02"),
#Early117 = c(NOT="#cde6ec", Uromol="#E7298A"),
SC = c(Basal="#5351c7", NOT="#cde6ec", SC="#e81b1b", "Basal & SC" ="#f20bda", organoids="#9d7802"),
nat_cl = c(c1="#eec8c2", c2="#9190d7", c3="#f05840", c4 ="#0a07ab", NOT="#cde6ec", organoids="#9d7802"),
Tumor_Stage = c("not reported"="#cde6ec", "stage i" = "#adb6ec", "stage ii" = "#9190d7", "stage iii" = "#5c72eb", "stage iv" = "#0a07ab", organoids="#9d7802" ),
BASE47_129 = c("NOT"="#cde6ec", "C1" = "#eec8c2", "C2" = "#9190d7", "C3" = "#f05840", "C4" = "#0a07ab" ),
Definition= c('Primary solid Tumor' ="#dedae8", 'Solid Tissue Normal' = "#fdd4c1", organoids="#9d7802"),
samp_class = c("TCGA BLCA"='#cde6ec', 'organoid'=adjustcolor('#9d7802', alpha.f=.3), 'tumor'='#9d7802'),
passage = c(TCGA="#ffffff", "P14"=adjustcolor("#2a027d", alpha.f = .06*2),"P13"=adjustcolor("#2a027d", alpha.f = .06*3),
"P12"=adjustcolor("#2a027d", alpha.f = .06*3.5), "P11"=adjustcolor("#2a027d", alpha.f = .06*4),
"P10"=adjustcolor("#2a027d", alpha.f = .06*4.5), "P9"=adjustcolor("#2a027d", alpha.f = .06*5),
"P8"=adjustcolor("#2a027d", alpha.f = .06*5.5), "P7"=adjustcolor("#2a027d", alpha.f = .06*6),
"P6"=adjustcolor("#2a027d", alpha.f = .06*7), "P5"=adjustcolor("#2a027d", alpha.f = .06*8),
"P4"=adjustcolor("#2a027d", alpha.f = .06*9), "P3"=adjustcolor("#2a027d", alpha.f = .06*10),
"P2"=adjustcolor("#2a027d", alpha.f = .06*11), "P1"=adjustcolor("#2a027d", alpha.f = .06*12),
"P0"=adjustcolor("#2a027d", alpha.f = .06*13), "tumor"=adjustcolor("#2a027d", alpha.f = .06*14)
)
)
#BASE47_129 four clusters by BASE47 genes on 129 samples
## BASE47 classifiers 129 samples
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("nat_cl", "Tumor_Stage", "SC")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
pdf("results/heatmap_tcga129_base47v2.pdf", width=10, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='TCGA BLCA RNA-Seq 129 samples, BASE47 genes', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = F, fontsize_row = 6,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
tail(anno_col)
table(anno_col_tmp$nat_cl, anno_col_tmp$SC)
# assign the clusters of the 129 patient samples based on the BASE47 genes
anno_col$BASE47_129 = rep("NOT", nrow(anno_col))
row.names(anno_col_tmp) -> tmp
tmp = tmp[hp$tree_col$order]
anno_col$BASE47_129[row.names(anno_col) %in% tmp[1:51]] = "C1"
anno_col$BASE47_129[row.names(anno_col) %in% tmp[52:68]] = "C2"
anno_col$BASE47_129[row.names(anno_col) %in% tmp[69:94]] = "C3"
anno_col$BASE47_129[row.names(anno_col) %in% tmp[95:129]] = "C4"
## 51, 35, 25, 18
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("nat_cl", "Tumor_Stage", "SC", "BASE47_129")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
pdf("results/heatmap_tcga129_base47_cluster_v2.pdf", width=10, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='TCGA BLCA RNA-Seq 129 samples, BASE47 genes', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = F, fontsize_row = 6,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
# survive curve for these nat_cl clusters
library(survival)
death = blca.data$days_to_death
death = death[anno_col$nat_cl != "NOT"]
followup = blca.data$days_to_last_follow_up
followup = followup[anno_col$nat_cl != "NOT"]
event = rep(0, length(death))
event[death > 0] = 1
time = death
time[is.na(time)] = followup[is.na(time)]
tmp = anno_col[anno_col$nat_cl != "NOT", "nat_cl"]
sf = survfit(Surv(time, event) ~ tmp)
pdf("results/surv_tcga_4cluster.pdf")
plot(sf, col=2:5, mark.time=T, cex=.5, xpd=T, xlab="days", ylab="% survived")
legend(4000, 1, col=2:5, legend=c("Basal", "Basal & SC", "NOT", "SC"), lty=1, cex=.5, bty="n", y.intersp=3)
text(4000, .5, "P value 0.187", cex=.7)
dev.off()
#data.frame(death = death, followup = followup, time=time)
sf$n
survdiff(Surv(time, event) ~ tmp)
# survive curve for these SC clusters
death = blca.data$days_to_death
death = death[anno_col$nat_cl != "NOT"]
followup = blca.data$days_to_last_follow_up
followup = followup[anno_col$nat_cl != "NOT"]
event = rep(0, length(death))
event[death > 0] = 1
time = death
time[is.na(time)] = followup[is.na(time)]
tmp = anno_col[anno_col$nat_cl != "NOT", "SC"]
tmp
tmp2 = tmp
tmp2[tmp == "C1" | tmp == "C2"] = "C1/2"
tmp2[tmp == "C3" | tmp == "C4"] = "C3/4"
sf = survfit(Surv(time, event) ~ tmp)
pdf("results/surv_tcga_pathology.pdf")
plot(sf, col=2:5, mark.time=T, cex=.5, xpd=T, xlab="days", ylab="% survived")
legend(4000, 1, col=2:5, legend=c("Basal", "Basal & SC", "NOT", "SC"), lty=1, cex=.5, bty="n", y.intersp=3)
text(4000, .5, "P value 0.0173", cex=.7)
dev.off()
sf$n
survdiff(Surv(time, event) ~ tmp)
# survive curve for these BASE47_129 clusters
death = blca.data$days_to_death
death = death[anno_col$nat_cl != "NOT"]
followup = blca.data$days_to_last_follow_up
followup = followup[anno_col$nat_cl != "NOT"]
event = rep(0, length(death))
event[death > 0] = 1
time = death
time[is.na(time)] = followup[is.na(time)]
tmp = anno_col[anno_col$nat_cl != "NOT", "BASE47_129"]
tmp2 = tmp
tmp2[tmp == "C1" | tmp == "C3"] = "C1/3"
tmp2[tmp == "C2" | tmp == "C4"] = "C2/4"
sf = survfit(Surv(time, event) ~ tmp2)
pdf("results/surv_tcga_base47_base47cluster.pdf")
plot(sf, col=2:3, mark.time=T, cex=.5, xpd=T, xlab="days", ylab="% survived")
legend(4000, 1, col=2:3, legend=c("C1/C3 n=75", "C2/C4 n=53"), lty=1, cex=.5, bty="n", y.intersp=3)
text(4000, .4, "P value 0.02", cex=.7)
dev.off()
sf$n
survdiff(Surv(time, event) ~ tmp2)
#genes from nature 2014
tmp = c("FGFR3", "KRT5", "KRT6A", "KRT14", "EGFR", "GATA3", "FOXA1", "UPK3A", "ERBB2", "ESR2", "CDH1")
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT"
row_sel = gs %in% tmp & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("nat_cl", "Tumor_Stage", "SC")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
pdf("results/heatmap_tcga129_natureSig.pdf", width=10, height=6)
hp = pheatmap(matrix_tmp, color=col, scale='none', show_rownames = T, breaks = breaks, border_color = NA, main='TCGA BLCA RNA-Seq 129 samples, Nature 2014 gene list', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = F, fontsize_row = 6,
annotation_col = anno_col_tmp, #annotation_row = anno_row_tmp,
annotation_colors = ann_colors)
dev.off()
#BASE47 genes, all samples
col_sel = anno_col$Definition == "Primary solid Tumor"
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("nat_cl", "Tumor_Stage", "SC", "BASE47_129")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = 2
pdf("results/heatmap_tcga414_base47.pdf", width=10, height=7)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='TCGA BLCA RNA-Seq 414 samples, BASE47 genes', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = F, fontsize_row = 8,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
#BASE47 genes, blca plus SHEN samples
tcga_shen_cc_log[1:10, 404:414]
unique(anno_col$samp_class)
head(anno_col)
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT"| anno_col$Definition == "organoids" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("nat_cl", "Tumor_Stage", "SC", "samp_class")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
ll = 2
breaks = c( seq(from = -ll, to = ll, length.out = 30));breaks = unique(breaks)
matrix_tmp[matrix_tmp > ll] = ll
matrix_tmp[matrix_tmp < -ll] = -ll
pdf("results2/heatmap_tcga414_organoids_base47.pdf", width=10, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='414 samples plus Organoid samples', clustering_method = 'ward.D2',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = F, fontsize_row = 6,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
#BASE47 genes, SHEN samples
col_sel = anno_col$Definition == "organoids" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("samp_class", "passage")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
dim(matrix_tmp)
row.names(matrix_tmp) = sub(".*_", "", row.names(matrix_tmp))
row.names(anno_row_tmp) = sub(".*_", "", row.names(anno_row_tmp))
pdf("results/heatmap_organoids_42samples_base47.pdf", width=7, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='Organoid/tumor samples, BASE47 genes', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 6, fontsize_col=6,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
## cutree
## 1 basal, 2 luminal, 3 mix
cutree(hp$tree_col, 3) -> tree
tree[tree == 1] = 'basal'
tree[tree == 2] = 'luminal'
tree[tree == 3] = 'mix'
length(tree)
rsem_shen_counts = rsem_shen$counts
dim(rsem_shen_counts)
mode(rsem_shen_counts) <- "integer"
condition = tree
design = data.frame(
row.names = colnames(rsem_shen_counts),
condition = condition,
libType = rep("PE", ncol(rsem_shen_counts)));
ddsmat = DESeqDataSetFromMatrix(countData = rsem_shen_counts,
colData = design,
design = ~ condition);
dds.ds <- estimateSizeFactors(ddsmat);
dds <- DESeq(dds.ds, parallel=T);
rsem_shen_cc = counts(dds, normalized=T)
rsem_shen_cc[rsem_shen_cc == 0] = 1
rsem_shen_cc_log = log2(rsem_shen_cc)
res_basal_luminal = results(dds, contrast = c('condition', 'basal', 'luminal'))
res_mix_luminal = results(dds, contrast = c('condition', 'mix', 'luminal'))
#source("~/program/fun/post_deseq2_results.R")
vv = c('res_basal_luminal', 'res_mix_luminal')
vv_sig = paste0(vv, "_sig")
padj = 0.01; log2FC = 2;
for(i in 1:length(vv)){
v = vv[i]
v_sig = vv_sig[i]
x = get(v)
x$symbol = sub(".*_", "", row.names(x))
x[order(x$pvalue),] -> x
x.sig = x[!is.na(x$padj) & x$padj < padj & !is.na(x$log2FoldChange) & abs(x$log2FoldChange) > log2FC,]
source("~/program/fun/write_rnk.r")
write_rnk(data = x, filename=paste0(v, ".rnk"), pvalue.cutoff=1)
write.table(x, file = paste0(v, ".csv"), sep="\t", quote=F)
assign(v, x);
assign(v_sig, x.sig)
}
source("~/program/fun/run_gsea.R")
run_gsea("res_basal_luminal.rnk" )
run_gsea("res_mix_luminal.rnk")
source("~/program/vennDia.R")
pdf("results/ven_basal_mix_vs_lumina.pdf", width=6, height=6)
venndiagram(x = res_basal_luminal_sig$symbol, y = res_mix_luminal_sig$symbol, type= '2', labels = c("basal vs luminal", "mixed type vs luminal"))
dev.off()
dim(res_basal_luminal_sig)
dim(res_mix_luminal_sig)
## heatmap
id = union(row.names(res_basal_luminal_sig), row.names(res_mix_luminal_sig))
rsem_shen_cc_log[id, ] -> matrix_tmp
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = names(tree)
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
ann = data.frame(row.names = names(tree), type = tree)
pdf("results/heatmap_organoids_42sample_subtype.pdf", width=10, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = F, breaks = breaks, border_color = NA,
main='Heatmap of differentially expressed genes in tumor/organoids subtypes', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, fontsize_row = 6,
annotation_col = ann)
dev.off()
#BASE47 genes, SHEN samples organoids, not primary
col_sel = anno_col$samp_class == "organoid" & anno_col$del == 0
col_sel
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("samp_class", "passage")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
dim(matrix_tmp)
row.names(matrix_tmp) = sub(".*_", "", row.names(matrix_tmp))
row.names(anno_row_tmp) = sub(".*_", "", row.names(anno_row_tmp))
pdf("results/heatmap_organoidsonly_base47.pdf", width=7, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='Organoid/tumor samples, BASE47 genes', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 6, fontsize_col=6,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
## cutree
## 1 basal, 2 luminal
cutree(hp$tree_col, 2) -> tree_orgonly
tree_orgonly[tree_orgonly == 1] = 'basal'
tree_orgonly[tree_orgonly == 2] = 'luminal'
length(tree_orgonly)
tree_orgonly
rsem_shen_counts = rsem_shen$counts
rsem_shen_counts = rsem_shen_counts[, grep("tumor", target$id, invert = T)]
dim(rsem_shen_counts)
mode(rsem_shen_counts) <- "integer"
condition = tree
design = data.frame(
row.names = colnames(rsem_shen_counts),
condition = condition,
libType = rep("PE", ncol(rsem_shen_counts)));
ddsmat = DESeqDataSetFromMatrix(countData = rsem_shen_counts,
colData = design,
design = ~ condition);
dds.ds <- estimateSizeFactors(ddsmat);
dds <- DESeq(dds.ds, parallel=T);
rsem_shen_cc = counts(dds, normalized=T)
rsem_shen_cc[rsem_shen_cc == 0] = 1
rsem_shen_cc_log = log2(rsem_shen_cc)
res_basal_luminal_orgonly = results(dds, contrast = c('condition', 'basal', 'luminal'))
vv = c('res_basal_luminal_orgonly')
vv_sig = paste0(vv, "_sig")
padj = 0.01; log2FC = 2;
for(i in 1:length(vv)){
v = vv[i]
v_sig = vv_sig[i]
x = get(v)
x$symbol = sub(".*_", "", row.names(x))
x[order(x$pvalue),] -> x
x.sig = x[!is.na(x$padj) & x$padj < padj & !is.na(x$log2FoldChange) & abs(x$log2FoldChange) > log2FC,]
source("~/program/fun/write_rnk.r")
write_rnk(data = x, filename=paste0(v, ".rnk"), pvalue.cutoff=1)
write.table(x, file = paste0(v, ".csv"), sep="\t", quote=F)
assign(v, x);
assign(v_sig, x.sig)
}
dim(res_basal_luminal_orgonly_sig)
source("~/program/fun/run_gsea.R")
run_gsea("res_basal_luminal_orgonly.rnk" )
## heatmap
dim(res_basal_luminal_orgonly_sig)
table(sign(res_basal_luminal_orgonly_sig$log2FoldChange))
head(res_basal_luminal_orgonly_sig)
id = row.names(res_basal_luminal_orgonly_sig)
rsem_shen_cc_log[id, target$passage != 'p' ] -> matrix_tmp
dim(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = names(tree_orgonly)
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
ann = data.frame(row.names = names(tree_orgonly), type = tree_orgonly)
dim(ann)
pdf("results/heatmap_organoids_34sample_subtype.pdf", width=7, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = F, breaks = breaks, border_color = NA,
main='Heatmap of differentially expressed genes in tumor/organoids subtypes', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, fontsize_row = 6,
annotation_col = ann)
dev.off()
#BASE47 genes, blca plus SHEN samples , less header
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT"| anno_col$Definition == "organoids" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("samp_class"), drop = F]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
pdf("results2/heatmap_tcga129_organoids_base47_lesshead.pdf", width=10, height=6)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='129 samples plus Organoid samples', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = F, fontsize_row = 6,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
#BASE47 genes, blca plus SHEN samples, with col names
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT"| anno_col$Definition == "organoids" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("nat_cl", "SC", "samp_class")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
pdf("results2/heatmap_tcga129_organoids_base47_colnames.pdf", width=24, height=8)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks, border_color = NA,
main='129 samples plus Organoid samples', clustering_method = 'complete',
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 6, fontsize_col = 7,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
head(newid)
anno_col$patients = "TCGA"
anno_col$patients[434:475] = row.names(anno_col)[434:475]
anno_col$passage = "TCGA"
target$passage2 = paste0("P", target$passage)
target$passage2[target$passage2 == 'Pp'] = 'tumor'
anno_col$passage[434:475] = target$passage2
tail(anno_col)
#BASE47 genes, blca plus SHEN samples, colnames and passage
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT"| anno_col$Definition == "organoids" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c("nat_cl", "SC", "samp_class", "passage")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
pdf("results/heatmap_tcga129_organoids_base47_colname_passage.pdf", width=20, height=10)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks,
main='129 samples plus Organoid samples', clustering_method = 'complete', border_color = NA,
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 8, fontsize_col = 8,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
## patient wise
tail(matrix_tmp)
tmp = row.names(anno_col)
tmp
tmp = sub("MS..._", "", tmp)
tmp = sub("_.*", "", tmp)
tmp
anno_col$pat="TCGA BLCA"
anno_col$pat[grep("MS0", row.names(anno_col))]= tmp[grep("MS0", row.names(anno_col))]
anno_col
col_sel = anno_col$Definition == "Primary solid Tumor" & anno_col$nat_cl != "NOT"| anno_col$Definition == "organoids" & anno_col$del == 0
row_sel = anno_row$BASE47 != "NOT" & anno_row$sd
anno_col_tmp = anno_col[col_sel, c( "SC", "samp_class", "passage", "patients")]
anno_row_tmp = anno_row[row_sel, "BASE47", drop=F]
matrix_tmp = tcga_shen_cc_log[row_sel, col_sel]
cn = colnames(matrix_tmp)
matrix_tmp = t(apply(matrix_tmp, 1, scale))
colnames(matrix_tmp) = cn
matrix_tmp[matrix_tmp > 2] = 2
matrix_tmp[matrix_tmp < -2] = -2
pdf("results/heatmap_tcga129_organoids_base47_colname_passage_pat.pdf", width=20, height=10)
hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', show_rownames = T, breaks = breaks,
main='129 samples plus Organoid samples', clustering_method = 'complete', border_color = NA,
cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 8, fontsize_col = 8,
annotation_col = anno_col_tmp, annotation_row = anno_row_tmp, annotation_colors = ann_colors)
dev.off()
tmp = cutree(hp$tree_col, 2)
tmp[tmp == 2] = "basal"
tmp[tmp == 1] = "luminal"
tmp = tmp[grep("MS0", names(tmp))]
base47_org_cluster = tmp
head(base47_org_cluster)
base47_org_cluster
#library(xlsx)
## see http://wlab.ethz.ch/cspa/#highlights
#hsa_cd = read.xlsx("../../genelist/Surface_protein.xlsx", 1)