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aec
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@aec-9409
Last seen 3.9 years ago
Dear all,
I have some doubts about the differences of the following interaction models:
~ind+grp+cnd+grp:cnd
~grp+grp:ind+grp:cnd
~grp+cnd+grp:ind+grp:cnd
After reading the DESeq2 manual and forums I still did not understand the differences.
My experiment design is:
GROUP CONDITION ind.n
s1 CONTROL no_treat p1
s2 CONTROL no_treat p2
s3 CONTROL no_treat p3
s4 CONTROL treat p1
s5 CONTROL treat p2
s6 CONTROL treat p3
s7 KO no_treat p2
s8 KO no_treat p3
s9 KO no_treat p1
s10 KO treat p2
s11 KO treat p3
s12 KO treat p1
And I would like to know the differential response of the treatment between groups accounting for the patient effect.
@Michael, I followed the example described in the DESeq2 documentation Group-specific condition effects, individuals nested within groups with this model
~grp+grp:ind+grp:cndand obtained a bunch of DE genes for the interaction I am interested, the differential response to treatment between groups. However, applying the limma-voom duplicateCorr (patient as random effect) and the combinedgrp_cndfactor as is described in 9.7 section of the manual which should be an equivalent alternative I do not get any significant genes for the contrast(grp2cnd2-grp2cnd1)-(grp1cnd2-grp1cnd1), but the top DE genes with DESeq2 are also at the top positions of limma. Is the different calculation of the Pval affecting the results? Is limma more conservative than DESeq2?“Is the different calculation of the Pval affecting the results?”
These are different methods which model the data differently. I would stick with one method because when you try multiple on your data now you are in a position to choose which method having seen the results of both.