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Convert read counts to transcripts per million (TPM).
#' Convert counts to transcripts per million (TPM).
#' Convert a numeric matrix of features (rows) and conditions (columns) with
#' raw feature counts to transcripts per million.
#' Lior Pachter. Models for transcript quantification from RNA-Seq.
#' arXiv:1104.3889v2
#' Wagner, et al. Measurement of mRNA abundance using RNA-seq data:
#' RPKM measure is inconsistent among samples. Theory Biosci. 24 July 2012.
#' doi:10.1007/s12064-012-0162-3
#' @param counts A numeric matrix of raw feature counts i.e.
#' fragments assigned to each gene.
#' @param featureLength A numeric vector with feature lengths.
#' @param meanFragmentLength A numeric vector with mean fragment lengths.
#' @return tpm A numeric matrix normalized by library size and feature length.
counts_to_tpm <- function(counts, featureLength, meanFragmentLength) {
# Ensure valid arguments.
stopifnot(length(featureLength) == nrow(counts))
stopifnot(length(meanFragmentLength) == ncol(counts))
# Compute effective lengths of features in each library.
effLen <-, lapply(1:ncol(counts), function(i) {
featureLength - meanFragmentLength[i] + 1
# Exclude genes with length less than the mean fragment length.
idx <- apply(effLen, 1, function(x) min(x) > 1)
counts <- counts[idx,]
effLen <- effLen[idx,]
featureLength <- featureLength[idx]
# Process one column at a time.
tpm <-, lapply(1:ncol(counts), function(i) {
rate = log(counts[,i]) - log(effLen[,i])
denom = log(sum(exp(rate)))
exp(rate - denom + log(1e6))
# Copy the row and column names from the original matrix.
colnames(tpm) <- colnames(counts)
rownames(tpm) <- rownames(counts)
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siya-kk commented Jan 26, 2021

The sad thing is i was using your "tpm_rpkm_from_featureCount.R" code years ago, and could not find the place how to cite it. cry......

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myh9811 commented Feb 1, 2021

hi @slowkow just wonder if I can use expected (estimated) counts (RSEM result) for this script?
(or is its use restricted to raw counts only?)

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