Hello, I am running Differential gene analysis. I am filter the counts before running differential gene analysis, because so many of my genes have low mapped reads and that is affecting it. My issue is that I take out my transcript column to run DeSeq2 but the issue is that then since I filter the sample I am unsure what gene goes with what count, so then when I make a volcano plot I can see genes are upregulated and down regulated but I do not know what genes are. Please see attached code
cts <- read_excel("~/DS.xlsx", sheet="fibro_IFN100_DS_CT")
metadata <- (read_excel("~/DS.xlsx", sheet="fibro_IFN100_DS_CT_coldata"))
rownames(cts) <- cts$transcript
temp_cts_with_transcriptNames <- cts
#Adding NULL info to roName
cts$transcript <- NULL
cts_no_transcript<- cts[,-1]
dds <- DESeqDataSetFromMatrix(countData = cts_no_transcript,
colData = metadata,
design= ~ factor(condition))
smallestGroupSize <- 2
keep <- rowSums(counts(dds) >= 50) >= smallestGroupSize
dds <- dds[keep,]
dds2 <- DESeq(dds)
resultsNames(dds2)
res <- results(dds2, name="factor.condition.DS")
res
res