I would like to do a standard differential expression analysis.
Hi, I have mirdeep2 output that looks like this.
#miRNA read_count precursor total seq seq(norm)
mmu-let-7a-5p 43271 mmu-let-7a-1 43271 43271 7658.26
mmu-let-7a-1-3p 784 mmu-let-7a-1 784 784 138.76
mmu-let-7a-5p 43224 mmu-let-7a-2 43224 43224 7649.94
I think using the seq(norm) would be appropriate, but I cannot find how the sequences have been normalised. I think it is something like TPM but am not sure. Does anyone know?
Best, Krutik
1 answer
https://github.com/rajewsky-lab/mirdeep2/blob/master/FAQ.md
How are miRNA expression values reported by the qunatification module normalized?
Expression values are normalized by library size and multiplied by a factor of 1E6 which corresponds technically to counts per million mapped miRNA reads (RPM). The library size is the total number of reads mapping to miRNA precursors.
So a pretty simple and suboptimal normalization. Better use something like edgeR or DESeq2 for a proper normalization, depending on your downstream analysis. What is the analysis goal?
Then I would simple make a count matrix, genes being rows, samples being columns, with the raw counts (read_count) the values, and feed it into e.g. DESeq2. This tool needs raw counts.
Hell ATpoint,
I found your replied is very helpful, thanks. I have another question, what's the criteria to select true-positive miRNAs from all predicted miRNAs? As I understand, it requests significant randfold p-value labeled as yes, high miRDEEP2 score. Do you know a more clear criteria? Thanks. Xu
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