getKMerTFEnrichment: Get TF motif enrichment for given gkm-PCs

In Carldeboer/BrockmanR: K-mer-based analysis of chromatin state.

Description

Applies the minimum hypergeometric test to all PC*motif combinations, for highly-weighted k-mers and lowly-weighted k mers.

Usage

getKMerTFEnrichment(rotated, binaryKMerMatchesToTFs, n_max = 3000,
  verbose = F)

Arguments

rotated	A numeric matrix containing the k-mer loadings for however many PCs you want analyzed (rows are k-mers, columns are PCs).
binaryKMerMatchesToTFs	A matrix of k-mers by TF motifs, where each row is a k-mer and each column is a TF motif. Each entry in the matrix is 1 if the k-mer matches the TF's motif, and 0 otherwise.
n_max	The maximum number of k-mers to consider for each minimum hypergeometric test. Defaults to 3000. Should be no more than about 10% of the number of k-mers.
verbose	Print verbose output, describing progression.

Value

Returns a data.frame containing the following columns: Motif_ID (the motif ID from binaryKMerMatchesToTFs), PC (the PC from rotated), p (minimum hyper geometric ln(p-values)), k (the number of top k-mers that yielded maximal enrichment), log2OR (the log2 odds ratio (observed/expected) of k-mers for the point of maximal enrichment), i (a unique integer for each PC-motif-direction combination).

Examples

kmerMat = inputKMerFreqs(sprintf("kMerFiles/%s.freq.gz",sampleDesc$id), IDs = sampleDesc$id)
myPCA = doKMerPCA(kmerMat, nPCs = "jackstraw")
treatmentPCs = findDistinguishingPCs(myPCA$rotation[,1:myPCA$nPCs], sampleDesc[c("id","treated")])
tfEnrichmentsPBM = getKMerTFEnrichment(myPCA$rotation[,1:myPCA$nPCs], cisbp$binaryPBMZScores);
tfEnrichmentsPBM = tfEnrichmentsPBM[order(tfEnrichmentsPBM$p),]
tfEnrichmentsPBM = merge(tfEnrichmentsPBM2, cisbp$TFTable[c("Motif_ID","TF_Name")], by="Motif_ID") # add TF names
head(tfEnrichmentsPBM[tfEnrichmentsPBM$PC==treatmentPCs$PC[1],], n=20) # show top 20 motifs for the first treatment-distinguishing PC

Carldeboer/BrockmanR documentation built on May 31, 2019, 2:19 p.m.