DEseq2 time-series design
Entering edit mode
Last seen 8 weeks ago

Hi All,

I have time series study, where colData files look like

      cellLine   dex timeStep batch
      200142521     TF-1  CTRL       24     A
      200142571     TF-1 IFN-g       24     A
      200142621     TF-1  CTRL       48     A
      200142661     TF-1 IFN-g       48     A
      200142701     TF-1  CTRL       72     A
      200142741     TF-1 IFN-g       72     A
      700347671     TF-1  CTRL       24     B
      700347681     TF-1 IFN-g       24     B
      700347691     TF-1  CTRL       48     B
      700347701     TF-1 IFN-g       48     B
      700347711     TF-1  CTRL       72     B
      700347721     TF-1 IFN-g       72     B

The aim of the study is to find Significantly differential Expression gene across Time series 24, 48 and 72 hours. I have two replicates for each Time steps.

I have used below design for analysis. dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ dex + timeStep + dex:timeStep) and then dds <- DESeq(dds ,test="LRT", reduced = ~ dex + timeStep)

My questions/concerns are: 1. Am I am using correct design Matrix for such time series analysis? 2. Since I am having two replicates as time Period so if the Number of replicates is good for such study? ( I don't have any possibility of adding more replicates.

deseq2 • 403 views
Entering edit mode

Thanks Michael,

For the same experiment, I have run for 4 different Cell line. Out of these 4 Cell line, I am able to get gene with padj cutoff for 2 cellline while other 2 cell line, I didn't get any gene to lower then padj cutoff. For this two celline, I got gene with pval criteria but not padj. So what would your suggestion for such cases?

Regards, Najeeb

Entering edit mode

I have no further suggestions. It is underpowered.

Entering edit mode
Last seen 7 hours ago
United States

Yes, that's correct. You could also throw in batch into both full and reduced as an additive effect with no interactions with other coefficients. Having two replicates is not desirable for a number of reasons, including bare practicality that one replicate may be of poor quality. But you can see if you can find large effects with this study nevertheless.


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