how to convert the fpkm value generated from cufflink to tpm value using r programming????
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written
by
sayamsmruti •
2 answers
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.
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
no, actually it is the fpkm value generated from cufflink, i am unable to convert the fpkm values to tpm
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)
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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
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.
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.
Hi
Which package do I need to install for this code?
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
Also, by Harold Pimental: What the FPKM? A review of RNA-Seq expression units
Hi
Which package do I need to install for this code?
https://haroldpimentel.wordpress.com/2014/05/08/what-the-fpkm-a-review-rna-seq-expression-units/
http://bioinfogeek.over-blog.com/2017/09/gene-expression-units-explained-rpm-rpkm-fpkm-and-tpm.html
Please, read this article for basic calculation,
http://bioinfogeek.over-blog.com/2017/09/gene-expression-units-explained-rpm-rpkm-fpkm-and-tpm.html
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
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.