Hi I need help the analysis of my RNA-seq samples using DESeq2. I uploaded cancer evidence for 100 samples divided into two parts, each section contains 50, the first group for a dead patient and the second for a living patient, all samples are cancerous. Data from the site GDC , HTSeq - Counts. I want to extract the expressed genes.

> -- replacing outliers and refitting for 3827 genes

> -- DESeq argument 'minReplicatesForReplace' = 7 

> -- original counts are preserved in counts(dds)

and running : minReplicatesForReplace = Inf and cooksCutoff = FALSE ,Get only 200

Code :

    #groups <- data.frame(type = c(rep("Dead", 50), rep("Alive", 50)))
    #rownames(groups) <- colnames(counts)

    #dds <- DESeqDataSetFromMatrix(countData = counts, colData = groups, design = ~ type)
    #dds <- DESeq(dds)

This is a lot of outliers. It sounds like the data are not a match for the model you are trying to fit. I would suggest trying RUV or sva to model unwanted variation (see the rnaseqGene workflow).