Question: Deseq2: 3 factors design
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gravatar for marburg2107
21 months ago by
marburg21070 wrote:

Many people were confused to handle three-factors with DESeq2 though many posts on this website.

Three factors are temperature(A, B); water content (C, D); co2 concentration (E, F).

I want to know the following effects: (1)temperature, (2)water, (3)co2, (4)temperatureco2, (5)waterco2, (6)temperaturewaterco2, (7)temperature*water.

DESeqDataSetFromMatrix(countData = countData,colData = colData,design = ~co2+warm+drought+co2:warm+co2:drought+warm:drought+co2:warm:drought) --------------Error in checkFullRank(modelMatrix)

My R version is 3.4.2 (2017-09-28), Deseq2 version is 1.18.1

ADD COMMENTlink modified 21 months ago by Michael Love25k • written 21 months ago by marburg21070
Answer: Deseq2: 3 factors design
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gravatar for Michael Love
21 months ago by
Michael Love25k
United States
Michael Love25k wrote:

I notice you posted the same question simultaneously to Biostars, this is discouraged as it duplicates the effort or volunteers.

There are many valid formula for when you have three factors with potential interactions. There is not a specific software question or reproducible example here. I recommend that you meet with and collaborate with a statistician who can help you setup an appropriate design and set of statistical tests. You can use any linear model formula with DESeq2.

ADD COMMENTlink written 21 months ago by Michael Love25k

Hi, Michael. Thanks for your time!

However, it's really difficult to find a statistician around us.

Total 72 samples, 6 treatment,12 replicates.

6 treatments were: co2-400ppm, co2-400ppm+drought, co2-400ppm+drought+warm, co2-600ppm, co2-600ppm+drought,co2-600ppm+drought+warm

Maybe you can tell me more about the statistical tests.

ADD REPLYlink written 21 months ago by marburg21070

I’m unfortunately too busy right now to give personal feedback on this analysis. For a design where second order interactions are possible,  figuring out what kind of modeling choices to make, which interactions to include, how to construct and interpret the results is not trivial, and the analyst should have a background in linear modeling in R.

ADD REPLYlink written 21 months ago by Michael Love25k
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