library(TrenaMultiScore)
library(EndophenotypeExplorer)
#----------------------------------------------------------------------------------------------------
targetGene <- "B4GALT3"
#----------------------------------------------------------------------------------------------------
runTMS <- function(goi)
{
tbl.fimo <- get(load(sprintf("tbl.fimo.%s.RData", targetGene)))
dim(tbl.fimo) # 1230563 9
tbl.oc <- human.brain.mayo.oc()
trenaProject <- TrenaProjectAD()
mtx.rna <- get.rna.matrix("old-mayo-tcx")
corf <- function(gene){
cor(mtx.rna["NDUFS2",], mtx.rna[gene,], method="spearman", use="pairwise.complete")
}
tbl.tfs <- get(load("~/github/MotifDb/inst/extdata/tfs-1683-lambert.RData"))
head(tbl.tfs)
tfs.with.rna <- unique(intersect(rownames(mtx.rna), tbl.tfs$Gene))
length(tfs.with.rna) # 1436
x <- lapply(tfs.with.rna, corf)
names(x) <- tfs.with.rna
fivenum(as.numeric(x))
dim(mtx.rna)
tms <- TMS$new(trenaProject, targetGene, tbl.fimo, tbl.oc, quiet=FALSE)
tms$scoreFimoTFBS() # chip, conservation, genehancer, genic annotations, distance to tss
tms$add.tf.mrna.correlations(mtx.rna, featureName="cor.all")
tbl.tms <- tms$getTfTable()
fivenum(tbl.tms$cor.all)
checkEquals(nrow(tbl.tms), nrow(tbl.fimo))
dim(subset(tbl.tms, fimo_pvalue <= 1e-6 & chip))
dim(subset(tbl.tms, fimo_pvalue < 1e-5 & abs(cor.all) > 0.8 & chip & gh > 600))
dim(subset(tbl.tms, fimo_pvalue < 1e-3 & abs(cor.all) > 0.4 & oc))
tfs <- subset(tbl.tms, fimo_pvalue < 1e-3 & abs(cor.all) > 0.3 & oc)$tf
tfs <- unique(tfs)
tfs <- tfs.with.rna
printf("running trena with candidate tfs: %d", length(unique(tfs)))
tms$addRBP()
tms$add.rbp.mrna.correlations(mtx.rna.asinh, featureName="cor.all") # added to tbl.rbp
tbl.rbp <- tms$getRbpTable()
dim(tbl.rbp)
rbps <- unique(subset(tbl.rbp, abs(cor.all) > 0.3)$gene)
printf("candidate rbps: %d", length(rbps))
tbl.trena.tf <- tms$build.trena.model(tfs, list(), mtx.rna.asinh)
tbl.trena.both <- tms$build.trena.model(tfs, rbps, mtx.rna)
tbl.trena.tf$target <- targetGene
tbl.trena.both$target <- targetGene
save(tbl.trena.tf, tbl.trena.both, file=sprintf("%s/trena.%s.RData", targetGene, targetGene))
} # runTMS
#------------------------------------------------------------------------------------------------------------------------
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