fpkm to tpm conversion

Question

[sayamsmruti]

how to convert the fpkm value generated from cufflink to tpm value using r programming????

[lucianalpt]

Actually, you can convert in R by the function I got from another forum and used.

fpkmToTpm <- function(fpkm) {

exp(log(fpkm) - log(sum(fpkm)) + log(1e6))

}

where fpkm is the values you got from TCGA for example.

Luciana

[Michael]

How do you want to cite that in a paper? In general we do not recommend to convert directly between normalized counts because they could been based on whatever non-linear transformation.

[Satyajeet Khare]

For a small dataset (raw counts) I tested, it did work fine. I did not expect the formula to be so simple :). Thanks for this input. Looking forward to learn more from this discussion.

[EpiExplorer]

Hi

Which package do I need to install for this code?

[Kevin Blighe]

Why do you want to use either FPKM or TPM?

Look:

You should abandon RPKM / FPKM. They are not ideal where cross-sample differential expression analysis is your aim; indeed, they render samples incomparable via differential expression analysis:

Please read this: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

The Total Count and RPKM [FPKM] normalization methods, both of which are still widely in use, are ineffective and should be definitively abandoned in the context of differential analysis.

Also, by Harold Pimental: What the FPKM? A review of RNA-Seq expression units

The first thing one should remember is that without between sample normalization (a topic for a later post), NONE of these units are comparable across experiments. This is a result of RNA-Seq being a relative measurement, not an absolute one.

[lucianalpt]

https://haroldpimentel.wordpress.com/2014/05/08/what-the-fpkm-a-review-rna-seq-expression-units/

[Renesh]

http://bioinfogeek.over-blog.com/2017/09/gene-expression-units-explained-rpm-rpkm-fpkm-and-tpm.html

[Renesh]

Please, read this article for basic calculation,

http://bioinfogeek.over-blog.com/2017/09/gene-expression-units-explained-rpm-rpkm-fpkm-and-tpm.html

[sayamsmruti]

actually after doing cufflink i got the genes.fpkm_tracking as output file so i am clueless what to do next for further data analysis, and how can i convert the generated fpkm values to tpm values...plzz can sum1 help out

[Michael]

Hi, I highly recommend to leave the cufflinks fpkm output alone and use a more simple and state-of-the-art approach such as featureCounts or HTseq-count directly from BAM files and then generate TPM or CPM from the counts directly using RSEM. In addition I recommend to provide more information, your question is pretty unspecific, and please avoid chat jargon like "plzz sum1". The R-programming portion should be ignored unless there are multiple alternative ways to do this.

Answer 1

[Satyajeet Khare]

It is going to be difficult to calculate TPM from FPKM values in Cuffdiff unless you have raw count values or gene length vector. I would suggest moving to count based methods since the old Tuxedo protocol is deprecated.

[Renesh]

You can still calculate TPM from RPKM/FPKM values.You need to have information about a total number of transcripts sampled from your read data and avg. a number of nucleotides mapped to each gene.

[Satyajeet Khare]

I think the issue is RPKM to TPM conversion in cuffdiff. RPKM values in cuffdiff are internally normalized. When calculated from raw counts, it should not be an issue.

Best

[sayamsmruti]

no, actually it is the fpkm value generated from cufflink, i am unable to convert the fpkm values to tpm

Answer 2

[DareDevil]

TPM(i) = ( FPKM(i) / sum ( FPKM all transcripts ) ) * 10^6

TPM = (((mean transcript length in kilobases) x RPKM) / sum(RPKM all genes)) * 10^6

To convert fpkm to tpm first generate dummy FPKM data

num_genes <- 1000
num_samples <- 5

fpkm_matrix <- matrix(rexp(num_genes * num_samples, rate = 0.1), nrow = num_genes)
colnames(fpkm_matrix) <- paste0("Sample_", 1:num_samples)
rownames(fpkm_matrix) <- paste0("Gene_", 1:num_genes)

Create a function for tpm based on above formula

sum_fpkm_per_sample <- colSums(fpkm_matrix)
scaling_factors <- sum_fpkm_per_sample / 1e6
tpm_matrix <- t(t(fpkm_matrix) / scaling_factors * 1e6)