DESeq2 multiple conditions, time points and sample types
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deseq2man • 0
@895d686a
Last seen 13 days ago
Belgium

I know there are a lot of questions asking similar things here on this forum and I checked the vingette and did a lot of other research but I still have a hard time wrapping my head around it.

I have multiple cell lines, multiple time points and multiple treatments:

Cell lines: CL1, CL2, CL3 Time points: 6h, 24h Treatments: T1, T2, T3, Control

For each cell line and each time point, there are 3 different treatments plus a control. 3 replicates for each sample -> 72 samples I want to measure control vs each treatment at each time point in each cell line. No testing across time points or across cell lines. My first thought was to separate the data into 6 different data sets (CL1, 6h | CL1, 24h | CL2, 6h | CL2 24h | CL3, 6h | CL3, 24h) and simply do the DE analysis separately, but I read that this is not the way to go. In the end it would also be nice to get a normalized count matrix with all data normalized together, for PCAs and similar.

I hope I made it understandable. How do I design the DESeqDataSet?

DESeq2 • 179 views
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I would find your question more useful if it came from a real person rather than an anonymous account, and if it reflected a basic effort of asking a scientific question and relating it to the experimental design. This forum is intended to serve a scientific community, and with that comes the inevitable tension to what extent knowledgeable people such as @mikelove should invest their limited time answering an individual request, versus more generally on maintenance and further development of their packages and methods. I am afraid that in this particular case you made the answer easy.

Of course this issue is at least as old as the internet, see e.g. fortunes::fortune(122)

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@mikelove
Last seen 1 minute ago
United States

Sorry, I just don't have time right now to do this kind of statistical consultation. For a complex experiment with 72 samples, you should work with someone familiar with linear models.

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