Time-series Differential Expression Analysis

Question

[indrik.wijaya]

Hi, I have a time-series data (7 time points & 2 replicates) and want to perform differential expression analysis. I am still new to this field and would like to find out more how to go about doing this. What I have thought of so far: 1) Finding differentially expressed genes between different time points (eg time 1 vs time 2, time 2 vs time 3, etc) 2) Finding differentially expressed genes across all the different time points

Are these 2 approaches correct or if not, how should I improve this?

And since DE tools such as edgeR and DESeq2 require design matrix as the input, may I know how to create the design matrix for the analysis I intend to do? Thank you!

[ATpoint]

Please read the DESeq2 manual about time series and visit/search the Bioconductor support page, e.g. here as there are plenty of questions on time series answered by the authors of the above-mentioned tools.

[indrik.wijaya]

Thank you for the help and link!

[arjundvas]

Hi could you please tell how you got around this? I have a similar dataset.

[ATpoint]

Please open a new question with sufficient details to answer your question.

Answer 1

[Kristoffer Vitting-Seerup]

From your options I would go with option 2 since that will allow you to controle the false discovery rate (FDR).

But there is actually a third option: Stagewise FDR correction where you use the option 2 as a "screeening state" and then use the option 1 in a "validation stage". Such functionality is described in this article and implemented in the bioconductor stageR r-package. Take a look at this section of the vignette.

[indrik.wijaya]

Thank you, I will take a look at this method!