Description Usage Arguments Details Value Author(s) References See Also Examples
For some analyses you might be interested in transforming the counts into TPMs which you can do with this function. This function uses the gene-level RPKMs to derive TPM values (see Details).
1 |
rse |
A RangedSummarizedExperiment-class object as downloaded with download_study. |
length_var |
A length 1 character vector with the column name from
|
mapped_var |
A length 1 character vector with the column name from
|
For gene RSE objects, you will want to specify the length_var
because otherwise you will be adjusting for the total gene length instead
of the total exonic sequence length of the gene.
As noted in https://support.bioconductor.org/p/124265/, Sonali Arora et al computed TPMs in https://www.biorxiv.org/content/10.1101/445601v2 using the formula: TPM = FPKM / (sum of FPKM over all genes/transcripts) * 10^6
Arora et al mention in their code that the formula comes from
https://doi.org/10.1093/bioinformatics/btp692; specifically
1.1.1 Comparison to RPKM estimation
where they mention an important
assumption: Under the assumption of uniformly distributed reads, we note
that RPKM measures are estimates of ...
There's also a blog post by Harold Pimentel explaining the relationship between FPKM and TPM: https://haroldpimentel.wordpress.com/2014/05/08/what-the-fpkm-a-review-rna-seq-expression-units/.
A matrix with the TPM values.
Sonali Arora, Leonardo Collado-Torres
https://www.biorxiv.org/content/10.1101/445601v2 https://arxiv.org/abs/1104.3889
getRPKM
1 2 | ## Compute the TPM matrix from the raw gene-level base-pair counts.
tpm <- getTPM(rse_gene_SRP009615)
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