Hello Dears, I have questions and I want your kind help?

I did gene filtering as below in edgeR which is exactly the same with my filtered genes in DESeq2 which I did before and I like that because I had a thought I may get different numbers of genes. Maybe you could comment to me here.

# edgeR 
y2 <- DGEList(counts = count, group = factor(Sample_data[,2]))
keep <- rowSums(y2$counts >= 50) >= 59
y2 <- y2[keep, , keep.lib.sizes=FALSE]
nrow(y2) # 10685 

# DESeq2
dds <- DESeqDataSetFromMatrix(countData = countMatrix, colData = colData, design = ~gender)
keep <- rowSums(counts(dds) >= 50) >= 59
dds <- dds[keep,]
nrow(dds) # 10685

My question here are two:

1. how to do normalization in edgeR? I mean, Is it enough using the function (y2 <- calcNormFactors(y2, method = "TMM") ) or still, I have to convert this to CPM? or Just using CPM is enough without prior use of calcNormFactor ()?

2. How to export significant genes as a CSV file plus how to extract significant and normalized genes for heatmap visualization?

Thank you so much?

What don't you just follow the edgeR documentation? There are heaps of case studies. For example, try typing

edgeRUsersGuide()

at the R prompt to get the pdf manual. The "Quick start" on page 10 would probably already answer your questions.

If you prefer reading in a browser, try the edgeR workflow.