Do I right understand that scRNA seq data in RPKM format are normalized in the expression within one sample, but do not normalized between the samples? And, therefore, they can contain all the copies of one gene due to PCR amplification step?
Do you know any good recommendations or articles which can help with RPKM data normalization and downstream analysis?
Thank you in advance!
Yes, the RPKM metrics is used to normalize for sequencing depth and transcript length within one sample. You cannot use it to compare expression profiles in different samples. For intra-sample comparisons better use TPM (Transcripts Per Kilobase Million) metrics. Here is a link to a short article on how TPM and RPKM are related (https://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/). And here is a useful guide on how to calculate these metrics using scater from Bioconductor package, see part 7.8.8 "Normalization for gene/transcript length" (https://hemberg-lab.github.io/scRNA.seq.course/cleaning-the-expression-matrix.html).
I am not sure if I understood your question, but I giving it a try.
You can perform RPKM normalization in your single-cell data, however this method sometimes is not the most accurate due to some complications such as dropouts in scRNA seq. The most used method for normalization that comes from bulk-RNA seq and it is used for scRNA seq is “counts per million” or CPM, but there new methods that have been developed specifically for single-cell. Finally, I think you correct among your groups with batch correction and data integration.
I suggest this paper: Article Current best practices in single‐cell RNA‐seq analysis: a tutorial
It has some explanations and clarifications on how to perform scRNA seq analyses.
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