Hi, I am new to RNASeq and DESeq2 and I am trying to do the analysis. My experiment has three groups (Control: X-overexpression= Disease: X+Y-overexpression= Treatment) with two samples in each. I did DESeq2 with RNASeq data to find the differential expression between two groups (pair-wise comparison: Disease vs. Control and Treatment vs. Disease). I have provided the code. However, I have been suggested to use a single model matrix (y ~ Disease+Treatment) to simultaneously evaluate the effect of X-overexpression and X+Y-overexpression using all samples. Genes with β(X) <> 0 are X-induced dysregulated genes. Genes with β(X+Y) <> 0 and has an opposite sign of β(X) are Y-rescued genes. I do not understand what is "single model matrix". I will be thankful if anyone directs me on this.
# setting the metadata for the samples sample <- c('C1', 'C2', 'D1','D2', 'T1', 'T2') condition <- c(rep('Control', 2), rep('Disease', 2), rep('Treatment', 2)) metadata <- data.frame(sample, condition) metadata <- column_to_rownames(metadata, 'sample') # sample order all(colnames(file) == rownames(metadata) # Create DESeqDataSet object dds <- DESeqDataSetFromMatrix(countData = file, colData = metadata, design = ~condition) # Differential Expression Analysis dds <- DESeq(dds) # Building the results table (res_A_W <- results(dds, contrast = c('condition', 'Disease', 'Control'), alpha = 0.05)) (res_AT_A <- results(dds, contrast = c('condition', 'Treatment', 'Disease'), alpha = 0.05))
I went through the "multi-factor designs" section of the DESeq2 vignette. It suggests two variables (~type+condition) where each level of variable 1 has every level of variable 2. However, my design has only one variable (Genotype and condition are the same, I have edited the code). My treatments, X (Disease) and X+Y (treatment) are the levels of condition variable but not as variables itself to use in the design formula. I will be thankful if you let me know if my understanding is correct. Regards Bhanu