Question: DESeq2 4 group comparison
3
gravatar for arabi
13 months ago by
arabi40
arabi40 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 • 552 views
ADD COMMENTlink modified 13 months ago by ATpoint46k • written 13 months ago by arabi40
1
gravatar for ATpoint
13 months ago by
ATpoint46k
ATpoint46k 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 13 months ago • written 13 months ago by ATpoint46k
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