I am currently trying to use DeSeq2 to look at differential abundance in my OTU data. My problem is that I have a small data set (18 samples on total) with only two biological replicates per group (3 groups, on 3 different days-example shown below for day 3);
S19= E.ten (Infected), Day3, S20=E.ten (Infected), day3, 21= E.max (Infected), Day3, S22=max (Infected), day3, S23= Control, Day7, S24=Control, day3,
In order to analyse differences between, for example, the E.ten infected group and the Control I used the following code: Taking only day3 samples and comparing one of the infected groups to the controls.
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition)
The CSV file returns the wald test p-value, untreated vs treated.
It is this P value that I use to indicate if there is statistical significance here?
Or is there a better way to do this?