Hi Mike and community,

I am recently doing DEG analysis by using Deseq2. I am trying to get differentially expressed genes in African American (AAM) and European American (EAM) prostate cancer populations by using the TCGA dataset. My target is to see upregulated and downregulated genes in the African American population. I used the below code to set the level of my population.
As I want to see upregulated genes in African American Males (AAM), is it the right way to set the level? or I have to set the level as ("EAM", "AAM")?

sampledata$Race <- factor(sampledata$Race, levels=c("AAM", "EAM"))```


deseq2Data <- DESeqDataSetFromMatrix(countData=rawCounts_me, colData=sampleData_me, design= ~ Race)

Hello,

Yes, by setting the levels of 'Race' in this way, AAM will be treated as the numerator and EAM as the denominator. Therefore, a gene with a positive fold-change will have higher expression in AAM compared to EAM.

Kind regards,

Kevin