DESeq2 accounting for two confounders with interaction in the design matrix
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@mohamedrefaat11992-22243
Last seen 17 months ago
Austria

Hi all!

I am analyzing RNA-seq data with DESeq2, and I have two confounding variables. One is the batch number and the other is whether cells were washed or not.

The data is comprised of samples of two different cell-lines that are un/treated with dox and un/modified with Luc/pax5/pax5-ita gene. These samples come from two batches, where the first contain only one type of cell line(NAM6) and the second batch contains two(NAM6/MHHCAL_2). This an abridged version of the metadata table

cohort cell_line mod treatment
2 MHHCALL2 Luc ctr
2 MHHCALL2 Luc dox
2 MHHCALL2 P5 ctr
2 MHHCALL2 P5 dox
2 MHHCALL2 P5X ctr
2 MHHCALL2 P5X dox
2 NALM6 Luc ctr
2 NALM6 Luc dox
2 NALM6 P5 ctr
2 NALM6 P5 dox
2 NALM6 P5X ctr
2 NALM6 P5X dox
1 NALM6 Luc ctr
1 NALM6 P5 ctr
1 NALM6 P5X ctr
1 NALM6 Luc dox
1 NALM6 P5 dox
1 NALM6 P5X dox

To study the effect of different combinations of modifications and treatments in the cell lines, I modified the table as follows

cohort cell_line celllinecohort mod treatment samplegroupsimple
2 MHHCALL2 MHHCALL2_2 Luc ctr Luc_ctr
2 MHHCALL2 MHHCALL2_2 Luc dox Luc_dox
2 MHHCALL2 MHHCALL2_2 P5 ctr P5_ctr
2 MHHCALL2 MHHCALL2_2 P5 dox P5_dox
2 MHHCALL2 MHHCALL2_2 P5X ctr P5X_ctr
2 MHHCALL2 MHHCALL2_2 P5X dox P5X_dox
2 NALM6 NALM6_2 Luc ctr Luc_ctr
2 NALM6 NALM6_2 Luc dox Luc_dox
2 NALM6 NALM6_2 P5 ctr P5_ctr
2 NALM6 NALM6_2 P5 dox P5_dox
2 NALM6 NALM6_2 P5X ctr P5X_ctr
2 NALM6 NALM6_2 P5X dox P5X_dox
1 NALM6 NALM6_1 Luc ctr Luc_ctr
1 NALM6 NALM6_1 P5 ctr P5_ctr
1 NALM6 NALM6_1 P5X ctr P5X_ctr
1 NALM6 NALM6_1 Luc dox Luc_dox
1 NALM6 NALM6_1 P5 dox P5_dox
1 NALM6 NALM6_1 P5X dox P5X_dox

As you can see, I have combined the mod and treatment columns into one column called sample_group_simple. As well as combining the cohort and cell_line columns into cell_line_cohort column. Finally, the following design for the analysis.

~ cell_line_cohort + cell_line_cohort:sample_group_simple

Unfortunately, the complex enough situation got more complicated when we found out that only a subset of samples have been washed by PBS. This variable is confounding the batch variable since all samples of the first batch have been washed, unlike the second one. As a result, any attempt to account for it in the analysis design leads to a not-full-rank model matrix. The final table looks like this

PBS cohort cell_line celllinecohort mod treatment samplegroupsimple
wash 2 MHHCALL2 MHHCALL2_2 Luc ctr Luc_ctr
wash 2 MHHCALL2 MHHCALL2_2 Luc dox Luc_dox
wash 2 MHHCALL2 MHHCALL2_2 P5 ctr P5_ctr
wash 2 MHHCALL2 MHHCALL2_2 P5 dox P5_dox
wash 2 MHHCALL2 MHHCALL2_2 P5X ctr P5X_ctr
wash 2 MHHCALL2 MHHCALL2_2 P5X dox P5X_dox
no_wash 2 NALM6 NALM6_2 Luc ctr Luc_ctr
no_wash 2 NALM6 NALM6_2 Luc dox Luc_dox
no_wash 2 NALM6 NALM6_2 P5 ctr P5_ctr
no_wash 2 NALM6 NALM6_2 P5 dox P5_dox
no_wash 2 NALM6 NALM6_2 P5X ctr P5X_ctr
no_wash 2 NALM6 NALM6_2 P5X dox P5X_dox
wash 1 NALM6 NALM6_1 Luc ctr Luc_ctr
wash 1 NALM6 NALM6_1 P5 ctr P5_ctr
wash 1 NALM6 NALM6_1 P5X ctr P5X_ctr
wash 1 NALM6 NALM6_1 Luc dox Luc_dox
wash 1 NALM6 NALM6_1 P5 dox P5_dox
wash 1 NALM6 NALM6_1 P5X dox P5X_dox

My question is how can I test for the effect of different combinations of treatment and modifications on different cell lines, while accounting for the two confounders, namely PBS and cell_line_cohort?

Thanks in advance, Mohamed

deseq2 design matrix multi-confounders linear models rna-seq • 1.1k views
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@mikelove
Last seen 4 days ago
United States

If a nuisance variable is confounded with another nuisance variable, you can just combine the two:

nuisance <- factor(paste(nuisance1, nuisance2))

And then just use this one variable.

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Thanks for the prompt reply, Micheal!

This means that I should do the following: 1) Combine cell_line, cohort, and PBS into one variable cell_line_cohort_PBS <- cell_line + cohort + PBS 2) Use the created variable without modifying the inital design formula. cell_line_cohort_PBS + cell_line_cohort_PBS :sample_group_simple Am I right?

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Sorry, I missed the fact that the confounding is with a condition of interest not another nuisance variable. In that case you can't really control for the nuisance variables.

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