I have paired rnaseq data from multiple samples, counted with
featureCounts, now planning to use DESeq2 and trying to design it. I have gone through DESEq2 comparison with mulitple cell types under 2 conditions. However, I would like to confirm if my design is correct or not?
Here is the sample coldata:
tissue condition sample1_WA1 WA1 Wild sample2_WA2 WA2 Wild sample3_WA3 WA3 Wild sample4_WB1 WB1 Wild sample5_WB2 WB2 Wild sample6_WB3 WB3 Wild sample7_WC1 WC1 Wild sample8_WC2 WC2 Wild sample9_WC3 WC3 Wild sample10_MA1 MA1 Mutant sample11_MA2 MA2 Mutant sample12_MA3 MA3 Mutant sample13_MB1 MB1 Mutant sample14_MB2 MB2 Mutant sample15_MB3 MB3 Mutant sample16_MC1 MC1 Mutant sample17_MC2 MC2 Mutant sample18_MC3 MC3 Mutant sample19_WE1 WE1 Wild sample20_WE2 WE2 Wild sample21_WE3 WE3 Wild sample22_WD1 WD1 Wild sample23_WD2 WD2 Wild sample24_WD3 WD3 Wild
where A,B,C,D,E are five tissue types and D and E are from wild condition only. 1,2 and 3 are biological replicates.
I want to perform:
(i) comparison of differentially expressed genes between all tissue types in wild
(ii) comparison of differentially expressed genes between all tissue types in mutant
(iii) comparison of differentially expressed genes between for all tissue types between wild versus mutant
(iv) comparison of differentially expressed genes between between D and E
How should I setup the design with replicates? Is this correct:
dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ tissue)
dds <- DESeq(dds) estimating size factors estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing Warning message: In checkForExperimentalReplicates(object, modelMatrix) : same number of samples and coefficients to fit, estimating dispersion by treating samples as replicates. read the ?DESeq section on 'Experiments without replicates'
Please guide Michael Love