I am working with micro_RNA data and wonder if we can include fold changes (as they are every large, since expression in one cell type may be 0 while high in others) along with p values. I believe these large FC does not look good, Is there any other alternative way to give FC or we just give p values. Thanks
What if you filter for miRNA with Count-Per-Million abundances are greater than 1 in at least 50% of samples (or use CPM for counts, if you have > 1 million aligned reads per sample)?
Also, as a QC metric, is the percent of reads assigned to miRNAs (versus rRNA, snoRNA, intergenic, etc.) consistent between samples?
The challenge I have is in two group analysis of 6 samples each if in one group read count is 0 in three samples while in second group the read count is there so the FC will be something like 200 or so. If i exclude those microRNAs then perhaps i am loosing important information. Is there a reasonable way to present such kind of large FC?
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