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  • DESeq2 multi-factor design

    Hi all,

    I have just started using DESeq2 using a multi-factor approach. As I'm new to this (and to statistics), I was hoping to get feedback / corrections on what I think the analysis should look like:

    I have the following colData:
    Code:
    sample	genotype	treatment	library_batch
    S1	WT	vehicle	B1
    S2	WT	vehicle	B2
    S3	WT	vehicle	B1
    S4	WT	vehicle	B2
    S5	WT	Drug	B2
    S6	WT	Drug	B1
    S7	WT	Drug	B2
    S8	KO	vehicle	B1
    S9	KO	vehicle	B2
    S10	KO	vehicle	B1
    S11	KO	vehicle	B2
    S12	KO	Drug	B1
    S13	KO	Drug	B2
    S14	KO	Drug	B1
    S15	KO	Drug	B2
    Blocking for library batch effects, I am interested in finding the following DE genes:
    Q1) DE genes between vehicle-treated "WT" and "KO" (i.e. the 'baseline' difference);
    Q2) Genes that are DE following treatment within each genotype ("WT" and "KO");
    Q3) Genes that show a genotype-dependent response to treatment.

    Looking through the DESeq2 tutorials and form posts, Q1 and Q2 would probably be easiest addressed by created a 'genotype_treatment' factor and working with contrast:

    Code:
    colData$condition <- paste(colData$genotype, colData$treatment, sep="_")
    and using a design
    Code:
    design = ~ library_batch + condition
    to extract results as:
    Q1:
    Code:
    res1 <- results(dds, contrast=c("condition", "KO_vehicle", "WT_vehicle"))
    Q2.1:
    Code:
    res2wt <- results(dds, contrast=c("condition", "WT_Drug", "WT_vehicle"))
    Q2.2:
    Code:
    res2ko <- results(dds, contrast=c("condition", "KO_Drug", "KO_vehicle"))
    As for Q3, I would then repeat the analysis including an interaction term:

    Code:
    design = ~ library_batch + genotype*treatment
    to extract results as:

    Code:
    res3 <- results(dds, name="genotypeKO.treatmentvehicle")
    Is this correct? I was wondering if there is a way to do this without running 2 separeate analysis designs...?

    On a more general note, can anyone recommend a resource to get more familiar with these design formulas and what they 'mean'?
    Last edited by PMRoberts; 11-16-2015, 03:48 PM.

  • #2
    Q1/2 looks very similar to what we've done with our RNASeq experiments. Mike Love seemed to think the approach was correct:

    Comment

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