Are there any limitations of Edge R or DEseq while doing differential gene expression analysis?
Can you elaborate on why you're asking this? Which problem do you have? It's a quite broad question and it's unclear what you are looking for.
Hi, answer to your question - check out these articles,
A comparison of methods for differential expression analysis of RNA-seq data
Evaluation of methods for differential expression analysis on multi-group RNA-seq count data
RNA-Seq differential expression analysis: An extended review and a software tool
DESeq is not suitable if you don't have biological replicates but EdgeR can calculate differential gene expression even you don't have biological replicates.
Differential expression without replicates is never valid, regardless of the method you use.
In case you want to see DE among large number of samples and you don't have replicates you can use EdgeR. Please see the section 2.11 What to do if you have no replicates
That is true waqas; however, it is bad practice. Results from single replicate experiments would suffer a lack of reproducibility. Not your fault, of course, but the EdgeR authors should probably reflect on the leadership position that they hold and be aware that the things they say/write can be interpreted in different ways.
Not only is it bad practice it is misleading - the whole reason we do DE is to have an idea of whether a result will extrapolate to other samples analyzed. Without independent biological replicates no such claims can be made. The 2.11 section of the edgeR vignette should only be used for pilot studies and never published!
From the link:
Be satisfied with a descriptive analysis, that might include an MDS plot and an analysis of fold changes. Do not attempt a significance
analysis. This may be the best advice.
" to see DE among large number of samples". Sounds like you have replicates to me. See my answer here: A: Replicates for RNA-seq from 1 cell line undergoing different treatments
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