DESeq2 for another layer of differential analysis
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Ming • 0
@2dddf277
Last seen 6 days ago
United States

Hi, I'm trying to use DESeq2 to do the differential analysis, but I didn't find the most suitable conference for what I should do.

I would like to find out whether the differential expressed gene from two group is significant different.

Here I have two conditions, 1) whether have treatment A, 2)female and male.

I've already analyzed the DEGs in female_treatmentA vs female_NotreatmentA, male_treatmentA vs male_NotreatmentA. Next, I want to compare whether the difference between DEGs of female and male is significant.

I know how to do this in EdgeR, but I dont know how to do this by DESeq2.

female_contrast <- makeContrasts(
female.ex <- groupA.F-groupNoA.F,
levels = design
)

male_contrast <- makeContrasts(
male.ex <- groupA.M-groupNoA.M,
levels = design
)

all_contrast <- makeContrasts(
female.ex <- groupA.F-groupNoA.F,
male.ex <- groupA.M-groupNoA.M,
sex.ex <- (female.ex)-(male.ex),
levels = design
)


I would really appreciate if someone can show me an explicit example, tailored for my case.

Thank you! Ming

DESeq2 • 128 views
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Please show your design and see the section on contrasts in the vignette.

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Hi,

Thank you for the relpy. Sorry that I didnt make it clear.

Here is my coldata of edgeR object: ( A is the control, B is the treatment)

treatement. gender. group

A female. A_female

A female. A_female

A female. A_female

B female B_female

B female. B_female

B female. B_female

A. male. A_male

A. male. A_male

A male. A_male

B male. B_male

B male. B_male

B. male. B_male

First, I have got two logFC from two contrasts:

#x is the object from edgeR
y=DEFormats::as.DESeqDataSet(x)
dds=DESeq(y)
results(dds, contrast=c("group", A_female, B_female),independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)
results(dds, contrast=c("group", A_male, B_male),independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)


Nest, I want to comapre two logFC from above two comparison.

I have read other questions in Bioconducter and vignette, if I understand it right, my next try would be:

 results(dds, contrast=c("group", B_male, B_female),independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)


or maybe this way:

dds<-DESeqDataSetFromMatrix(countData=countData,colData=colData,design = ~treatment+gender+treatment:gender)
dds<-DESeq(dds)
res<-results(dds,name="treatmentB.genderfemale")


Could you tell me which one make senses? Thank you so much!

Best,

Ming

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Entering edit mode
@mikelove
Last seen 1 day ago
United States

Sorry I don't have sufficient time for answering non-software related questions on the support site. I recommend consulting with a local statistician.