RPKM/FPKM, TPM and raw counts for RNA-seq

There was a hot discussion that RPKM should not be used in the RNA-seq analysis:
blahah404  Woohoo - death to FPKM and RPKM!t.co/CyR3ht1dqi

However @BioMickWatson has a different opinion, he thinks FPKM makes sense.
Why FPKM makes sense

what is RPKM or FPKM? see blog posts below:
update on 07/23/2015, see a youtube video  for great explanation of the three.
What is the FPKM? A review of RNA-Seq expression units
Counts vs. FPKMs in RNA-seq
Paper: RPKM measure is inconsistent among samples
FPKM/RPKM normalization caveat and upper quartile normalization
Finally a post from Lior Pachter
Estimating number of transcripts from RNA-Seq measurements (and why I believe in paywall)

I personally use raw counts and let DESeq2 to deal with normalization.

update on 07/08/2016.
Salmon and Kallisto outputs TPM, but if you want to convert counts to TPM, read this biostar post, and my post comparing salmon, kallisto and HTseq.

	#' 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 <- do.call(cbind, 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 <- do.call(cbind, 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)
	return(tpm)
	}

view raw counts_to_tpm.R hosted with ❤ by GitHub