Hi All,

I am wondering if this is the correct methodology to create Dseq results, particularly the code in regards to the utilization of apeglm Did I shrink l2fc again by using apeglm or instead just ignore that portion of the code, and instead just keep the Dseq(dds) portion?

Counts <- read.csv("1_raw_data.csv", header = TRUE, row.names = 1, sep = ",") 
Counts
condition <- factor (c("S","S","S","C","C","C")) 
coldata <- data.frame (row.names = colnames(Counts), condition)
coldata
counts2 <- round(Counts)
counts2
write.csv(counts2, file = "TEST.csv")
dds <- DESeqDataSetFromMatrix(countData = counts2, colData = coldata, design = ~condition)
dds <- DESeq(dds)
vsdata <- vst(dds, blind = FALSE)
res <- results(dds, name="condition_S_vs_C") 
res <- results(dds, contrast = c ("condition", "S", "C")) 
res
resLFC <- lfcShrink(dds, coef="condition_S_vs_C", type="apeglm") 
head(res)
deg <- subset(resLFC, padj<0.05)  
head(deg)
write.csv( deg,file = "TEST_dseq_results.csv")

The correct (that is recommended by the developer) way is summarized in the vignette, please have a look, or state more precisely what the problem is. https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

Please also spell the tool correctly. DESeq2.