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.

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

I have no further suggestions. It is underpowered.