Question: DESeq2 4 group comparison
0
gravatar for arabi
6 months ago by
arabi10
arabi10 wrote:

I want to compare the DEG expression levels in WT(Wild Type) and three mutants(mutantA, mutantB, mutantC ; lacked a certain genes). I used Salmon, tximport, and DESeq2(shown below).

group <- data.frame(con = (c(rep("WT", 3), rep("A", 3), rep("B", 3), rep("C", 3)))

# compare WT with A
dds <- DESeqDataSetFromTximport(txi = gene.exp, colData = group, design = ~ con)
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)
res <- results(dds, contrast = c("con", "WT", "A"))
head(res[order(res$pvalue), ])
write.table(res, file = "result.txt", row.names = T, col.names = T, sep = "\t")

# compare WT with B
dds <- DESeqDataSetFromTximport(txi = gene.exp, colData = group, design = ~ con)
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)
res <- results(dds, contrast = c("con", "WT", "B"))
head(res[order(res$pvalue), ])
write.table(res, file = "result.txt", row.names = T, col.names = T, sep = "\t")

# compare WT with C
dds <- DESeqDataSetFromTximport(txi = gene.exp, colData = group, design = ~ con)
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)
res <- results(dds, contrast = c("con", "WT", "C"))
head(res[order(res$pvalue), ])
write.table(res, file = "result.txt", row.names = T, col.names = T, sep = "\t")

dds <- DESeqDataSetFromTximport(txi = gene.exp, colData = group, design = ~ con)
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomLRT(dds, full = ~ con, reduced = ~ 1)
res <- results(dds)
head(res[order(res$pvalue), ])
write.table(res, file = "result.txt", row.names = T, col.names = T, sep = "\t")

Is this correct? Should I do something for WT?

Would you tell me the workflow?

heatmap deg rna-seq deseq2 • 322 views
ADD COMMENTlink modified 6 months ago by ATpoint36k • written 6 months ago by arabi10
1
gravatar for ATpoint
6 months ago by
ATpoint36k
Germany
ATpoint36k wrote:

You don't need to run the same workflow multiple times, so making the dataset, the normalization step and estimating dispersion. You can extract multiple contrasts from the results object, please see http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#contrasts

ADD COMMENTlink modified 6 months ago • written 6 months ago by ATpoint36k
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