Hello, very new to RNA-Sequencing, R and DESeq2 so the question might be too simple or repeated so I apologize in advance.
I am working with 24 RNA-Sequencing samples, 8 conditions with 3 biological replicates for each. A part of my data matrix is shown below.
Each sample represents one of three phenotypes. A wild-type phenotype, and two altered phenotypes. I would like to compare the wild-type phenotype with each of the other phenotypes, and the other two phenotypes with each other. I am confused as to what design formula I should use to generate my DESeq object. I have read the vignettes and beginner's guide but I lack the background to fully understand how the design formulas work. I have included part of my coldata below.
I would like to compare NB and CW, and NB and CCW. I aim to find genes that are differentially expressed as phenotype changes from NB to CW and from NB to CCW. Then I would like to compare CW and CCW. I would like to do this pair-wise for each sample pair and retain that information in the output. So far, I have been doing this by splitting my matrix into pairs of samples and running each pair individually but that is incredibly tedious and was wondering if I could accomplish the same results by using the complete expression matrix and coldata. I have tried only using chirality in the design formula (design = ~ Chirality) and set NB as the reference lever using the relevel() function, but upon using the resultsNames(dds) function I get,
"Intercept" "Chirality_CW_vs_CCW" "Chirality_NB_vs_CCW"
However, I believe I am looking for NB vs CW and NB vs CCW. Also, I have not included the Cell.Type in the design formula but I assume it should be included since I want to compare the samples pair-wise. Any help regarding design formulae for this particular case, understanding design formulae in R, in general, or other comments are greatly appreciated.
Thanks,
Tasnif Rahman
Please note the cross post on both sites:
https://support.bioconductor.org/p/115065/