Question: DESeq2 to analyse OTU differential abundance
gravatar for sarahmacdonald86
5.3 years ago by
United Kingdom
sarahmacdonald860 wrote:


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.


> library(DESeq2)




ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition)

colData(ddsHTSeq)$condition<-factor(colData(ddsHTSeq)$condition, levels=c("untreated","treated"))









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?

Thank you,



ADD COMMENTlink written 5.3 years ago by sarahmacdonald860

what you should use for statistical significance is the adjusted p-values (padj), last column, that accounts for multiple testing.

apart from this, I don't see any mistake in your approach. it would probably be useful to analyze all your data points together adding "time" as a variable in your linear model. but that depends on what your aim is.

ADD REPLYlink modified 15 months ago by Ram32k • written 5.3 years ago by Martombo2.7k
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