I have a question regarding contrasts in a multi-factorial design in Deseq2. I have a design that have 1 factor (Inoculation) with two levels (Inoc or No.Inoc) and another factor (Crop) with 4 levels (fallow - Fa, corn - Co, Mix 1 - M1, and mix 5 - M5). I set No.Inoc and Fa as the reference levels for their respective factors.

The design was the following:

design(degtreatMF) <- formula(~ Crop + Inoculation + Crop:Inoculation)
degtreatMF <- DESeq(degtreatMF, fitType="local")

And I got the following coeficients:

resultsNames(degtreatMF)

[1] "Intercept"                   "Crop_Co_vs_Fa"              
[3] "Crop_M1_vs_Fa"               "Crop_M5_vs_Fa"              
[5] "Inoculation_Inoc_vs_No.Inoc" "CropCo.InoculationInoc"     
[7] "CropM1.InoculationInoc"      "CropM5.InoculationInoc"

I need to find the following contrasts:

Co and Inoc vs Co and No.Inoc

M1 and Inoc vs M1 and No.Inoc

M5 and Inoc vs M5 and No.Inoc

Fa and Inoc vs Fa and No.Inoc

Now if I understood correctly, my intercept is Fa and No.Inoc and can be represented as c(1,0,0,0,0,0,0,0) while the others would be:

Fa and Inoc: (1,0,0,0,1,0,0,0)

Co and No.Inoc: (1,1,0,0,0,0,0,0)

Co and Inoc: (1,1,0,0,1,1,0,0)

M1 and No.Inoc: (1,0,1,0,0,0,0,0)

M1 and Inoc: (1,0,1,0,1,0,1,0)

M5 and No.Inoc: (1,0,0,1,0,0,0,0)

M5 and Inoc: (1,0,0,1,1,0,0,1)

Which means that I need to specifiy the following contrasts to get my expected results:

Co and Inoc vs Co and No.Inoc: c(0,0,0,0,1,1,0,0)

M1 and Inoc vs M1 and No.Inoc: c(0,0,0,0,1,0,1,0)

M5 and Inoc vs M5 and No.Inoc: c(0,0,0,0,1,0,0,1)

Fa and Inoc vs Fa and No.Inoc c(0,0,0,0,1,0,0,0)

Is that accurate?

I have to limit my time on the support site to issues relating to the software. For questions about experimental design, statistical analysis and interpretation, I recommend to work with a local statistician or someone familiar with linear models in R.