Hello, I'm using DESeq2 to model a multi-factorial experiment with two predictor variables: Genotype (binary) and Passage (integer):

```
# Make passage variable numeric
coldata_exp <- coldata
coldata_exp$Passage <- (as.numeric(coldata_exp$Passage))
dds_exp <- DESeqDataSetFromMatrix(countData = cts,
colData = coldata_exp,
design= ~Genotype*Passage)
keep <- rowSums(counts(dds_exp)) >= 10
dds_exp <- dds_exp[keep,]
# Make the model, perform likelihood ratio test
dds_exp <- DESeq(dds_exp, test="LRT",reduced = ~ Genotype + Passage)
```

We would like to derive some measure of 'effect size' of the interaction which is that is comparable between genes; or, how much does v1 affect gene expression in a v2-dependent manner? We have arrived at the standardized Beta coefficient (Wikipedia) as an appropriate measure of effect size.

My few questions are: Is it appropriate to compare standardized Beta values across genes? Does the DESeq2 coef() function output raw regression coefficients, or are they standardized Betas?

Thank you! Owen

Hi owen.chapman1, could you share your method on how to compute the effect size of multiple groups comparison? Thanks.