As stated, which one is better for differential expression analysis? When I say normal pipeline I mean limma-voom, edgeR and DESeq2 pipeline for standard analysis. Kal's z test is mentioned in this paper and I know some pre-made tools like the one on QIAgen uses this as well for single vs single comparison

The situation I have is that I have RNA-Seq data for some patients with rare translocations in leukaemia. There are two of these translocation with almost 10 or even more patients so I have no problem with them. And then there are these translocation which mostly I have only 1, some with 2 or 3 patients, for 7 different translocations.

Our hypothesis is that the two larger group can certainly form their own subtypes, and the others may fall into one of the three cases: (1) belongs to one of the two groups despite of the difference in translocation; (2) belongs to a separate group on their own i.e. complete outlier; (3) belongs to a separate group with other members in the "others" i.e. forming a third subtype. And this can be tested with clustering.

For that, I need to do supervised feature selection to filter out useless genes. I can do that with those with multiple samples, but not those isolated cases. So I thought I can get some DGE analysis and use a certain adj.p and fold change as cut off, or adj.p as ranking matrix, and select those genes for clustering.

So my main questions are: (1) when using limma/edgeR/DESeq2, should the model design include all the translocation labels in one go or should I just add the one contrast I want to make and build a new model everytime? (2) Is Kal's z test useful for one vs all DGE analysis and is it better? (3) Are there other outlier analysis working better under this situation?

Many thanks!


1 Answer 1

  1. You'll have a bit more power if you use all of the samples at once.
  2. Nothing is useful for this, don't waste your time with it.
  3. You're not looking for outliers, you're looking to see how samples cluster. You can just use correlation for that.

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