multifactor desing with interaction LRT or Wald test?
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jnaviapelaez ▴ 10
@jnaviapelaez-23060
Last seen 18 months ago

Hi I am analyzing a RNAseq experiment with 2 factors,

I have been reading vignettes and other questions but I am still confused on what to do

donor(genotype) with 2 levels "WT" and "imKO", condition with 3 levels "naive" "cisplatin" and "cispAIBP",

My design = ~donor +condition + donor:condition

and my ref levels, dds$condition <- relevel( dds$condition, ref="naive" ) dds$donor <- relevel(dds$donor, ref="WT"),

ddsCollapsed <- DESeq(ddsCollapsed)

1. I want to know if there is an effect of conditions in each genotype (main effect)?
res_cisp <- results(ddsCollapsed, test= "Wald", contrast=c("condition", "cisplatin", "naive"), alpha=.05)

res_AIBP <- results(ddsCollapsed, test="Wald", contrast=c("condition", "cispAIBP", "cisplatin"), alpha=.05)



However, If I do this how can I extract or compare the DEGs in only one genotype, for instance, WT?, I don't want to know the effect of each condition regardless of genotype but most the effect inside each genotype level. will the idea below work? I understand rescisp and resAIBP are comparisons of my first level (WT) and then for other genotype:

res_cisp_KO <- results(ddscollapsed, test="Wald", contrast=list( c("condition_cisplatin_vs_naive","donor_imKO.condition_cisplatin") ))
res_AIBP_KO <- results(ddscollapsed, test="Wald", contrast=list( c("condition_cispAIBP_vs_cisplatin","donor_imKO.condition_cispAIBP") ))



however, my results are not computing "condition_cispAIBP_vs_cisplatin" should i relevel my condition factor? in which case, should I create a second dds "ddsCollapsed2" with the new levels using cisplatin as reference and run DESeq to extract resAIBPKO ?

> resultsNames(ddsCollapsed)
[1] "Intercept"                              "donor_imKO_vs_WT"
[3] "condition_cisplatin.it.AIBP_vs_naive"   "condition_cisplatin.it.saline_vs_naive"
[5] "donorimKO.conditioncisplatin.it.AIBP"   "donorimKO.conditioncisplatin.it.saline"


I think using a grouping variable might be an easier way to do it. but then the other question that I have is to know if there is a difference in condition effect across genotype, and that is why I included the interaction in the design. , it seems like my DESeq function results without arguments res <- results(ddsCollapsed) is doing it

but again, I don't know how to extract "donorimKO.conditioncisplatin.it.AIBP" "donorimKO.conditioncisplatin.it.saline" DEGs., based on ?results I think the following might work. but I don't really understand why. i don't think I understand resultsName output either Bytheway

res_cisp_ko_vs_wt <- results(ddsCollapsed, name="donor_imKO.condition_cisplatin")
res_AIBP_ko_vs_wt <- results(ddsCollapsed, name="donor_imKO.condition_cispAIBP")


resAIBPkovswt in this case is comparing AIBP effect but taking the first level "naive" as reference right? should I relevel?

and I also want to compare "donorimKO _vsWT" but at condition "naive" which is the reference level. is this the comparison in resultsNames(ddsCollapsed) ? If not I don't know how to do it. and I think the grouping variable will help here too.

can I use 2 different designs so I can do the grouping for my comparison and then including the interaction for the genotype effect on the main condition effect? OR I should do an LRT instead and in that case which should be my full and reduced design?

I appreciate any help,

deseq2 LRT wald • 153 views
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Entering edit mode
@kevin
Last seen 8 hours ago
Republic of Ireland

I answered a similar question on Biostars, recently: https://www.biostars.org/p/426021/#426048

There is also information in the DESeq2 vignette and via ?DESeq2::results (scroll to the bottom when the manual entry page comes up to see the examples, 1 to 3), which should help you to arrive at a decision. There are also many questions already answered on this topic on both Biostars and here on Bioconductor.

Trust that it helps.

Kevin