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I was wondering if we can perform differential expression on samples from different library and read lengths.

What we intend to do is,

  1. n = 6 tumors (recently sequenced using Hiseq 2500 read length 150, paired end)
  2. n = 5 normals (previously sequenced using Hiseq read length 100, paired end, these are not matched normal)
  3. n = 100 ( normals, from tcga read length 100, paired)

What we would like to do is compare n=6 Vs 100 + 5 normals. What steps should we be considering before we move ahead with this analysis?

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  • $\begingroup$ It might be nice to add what steps you have considered already. For instance: how will you be able to tell if the observed differences you find are due to cancer/normal instead of due to different sequencing machines, different labs that produced the data or different people extracting the RNA? $\endgroup$
    – holmrenser
    Commented Nov 30, 2017 at 7:46
  • $\begingroup$ I have no clue we still at design stage, what you have asked is a good point that we need to consider and look an answer for. $\endgroup$ Commented Nov 30, 2017 at 14:45

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Adding experiments 1 & 2 should be fine without modification on the computer side of things, as long as your sample preparation is consistent. To be ultra-cautious, you could add in the experiment as an additional covariate for the differential expression model.

I'm less confident about adding in TCGA data; it's probably going to look better than your own samples, which might cause problems in calculating differential expression from raw reads. You can do a separate normalisation/correction of the TCGA data, but that will limit the inferences that can be made about differential expression -- without raw count information, the shot noise associated with read sampling cannot be included in the statistical model. I notice that TCGA has already done their own filtering / expression, and that would be preferable to use (mRNA sequencing, data subtype 'Gene'):

https://tcga-data.nci.nih.gov/docs/publications/tcga/datatype.html

However, even with data that has been properly normalised and filtered to exclude batch effects, there will still be population differences that influence results. My recommendation would be to do some subsampling to reduce the population effects, something like at least the following comparisons:

  • 6 Vs 100 + 5 normals
  • 6 Vs 100 normals
  • 6 Vs 50 + 3 normals [randomly sampled from the two normal groups]

And only consider differential expression that is consistently observed in all three of these comparisons. Note that the third "subsampled" dataset can be generated more than once, taking different subgroups each time.

If you are going to use separately normalised data, then DESeq2 may not be appropiate (because it expects count-level data). Something like Limma could work for DE analysis, see chapter 15 of the user's guide for RNASeq analysis. Unfortunately I'm not very familiar with using Limma, so can't provide any advice on things like what to watch out for in the input data and/or results.

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  • $\begingroup$ since I have not analyzed such data what sort of normalization would you reckon, before we push this to DESeq2 $\endgroup$ Commented Nov 28, 2017 at 13:46
  • $\begingroup$ any suggestions how can i compare this? $\endgroup$ Commented Nov 28, 2017 at 22:31

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