Question: DESeq2 4 group comparison
0
gravatar for arabi
10 weeks 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 • 144 views
ADD COMMENTlink modified 10 weeks ago by ATpoint31k • written 10 weeks ago by arabi10
1
gravatar for ATpoint
10 weeks ago by
ATpoint31k
Germany
ATpoint31k 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 10 weeks ago • written 10 weeks ago by ATpoint31k
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