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