# xena rsem fpkm setup
reportedScores <- read.table('~/Work/PanCancer_Data/five_signature_mclust_ensemble_results.tsv.gz', sep='\t', header=T, stringsAsFactors = F)
rownames(reportedScores) <- str_replace_all(reportedScores$AliquotBarcode, pattern = '\\.', replacement = '-')
reportedScores$SampleBarcodeShort <- str_sub(reportedScores$SampleBarcode, 1, 15)
dat <- read.table('tcga_RSEM_gene_fpkm.gz', stringsAsFactors=F, sep='\t', header=T)
load('~/Code/iatlas/ImmuneSubtypeClassifier/data/ebpp_gene.rda')
library(stringr)
reportedScores <- read.table('~/Work/PanCancer_Data/five_signature_mclust_ensemble_results.tsv.gz', sep='\t', header=T, stringsAsFactors = F)
rownames(reportedScores) <- str_replace_all(reportedScores$AliquotBarcode, pattern = '\\.', replacement = '-')
reportedScores$SampleBarcodeShort <- str_sub(reportedScores$SampleBarcode, 1, 15)
genes <- str_split(dat$sample, pattern='\\.')
genes2 <- unlist(lapply(genes, function(a) a[1]))
dat2 <- dat[genes2 %in% ebpp_genes_sig$Ensembl,]
reportedScores$ShortBarcode <- str_sub(reportedScores$AliquotBarcode, start=1, end=15)
ids <- intersect(reportedScores$ShortBarcode, colnames(dat2))
dat3 <- dat2[,ids]
rep1 <- reportedScores[reportedScores$ShortBarcode %in% ids,]
rep2 <- rep2[colnames(dat3),]
rownames(dat3) <- dat2$sample
fdat <- dat3
frep <- rep2
all(colnames(fdat) == frep$ShortBarcode)
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