DESeq2DataSet Multivariate Design?

[LillyChen]

Hello,

I'm currently working on a project with two tumor cell lines each from a different patient, and each one of the tumor cell lines has a control and treated group. I'm trying to look at the changes in gene expression that occur in each tumor cell line between untreated and those treated with Treatment A using DESeq2, but i'm having trouble and keep getting a warning message when running the DESEq pipeline

I'm running DESeq2 off of a Summarized Experiment:

se <- summarizeOverlaps(features=exons, reads=list,
mode="Union",
singleEnd=FALSE,
ignore.strand=FALSE,
fragments=TRUE, BPPARAM = SerialParam())

colData(se)=DataFrame(sample)

For the design I used: design=~Cell + Treat + Cell:Treat

This is the error message I get when I run the DEseq pipeline:

>dds<-DESeq(dds)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- standard model matrices are used for factors with two levels and an interaction,
where the main effects are for the reference level of other factors.
see the 'Interactions' section of the vignette for more details: vignette('DESeq2')

Is there another way to run the differential expression analysis to look at the change in expression level between the control and treated for each cell line ? I'm not sure why i keep getting this error. I've tried running DESeq2 with 1 cell line at a time (2 groups instead of 4) and I got very different results (only 5 genes instead of 81)

Thanks!

Lilly

[Michael Love]

We discourage simultaneously crossposting identical question on multiple sites.

This duplicates the effort of the answerers (they can't see that a question was answered).

And it also spreads out the answers, which makes it harder to other users to track the thread.

https://support.bioconductor.org/p/70497/