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We only provide automatic adjustment in the gene dimension. The decideTests function in limma does provide functionality for comparing across contrasts. when I see what appears to me to be opportunistic pair-wise comparisons, I just try to discourage it in favor of EDA: e.g. progammatically testing hundreds of pairs instead of having a preset hypothesis.
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DESeq2 Multiple Contrast Correction
Should an additional p-value correction be made for running multiple contrasts? Say for example I have 12 samples and with one factor that has 4 levels, A, B, C, and D.
No code has to be inserted here.If I run DESeq using Wald tests to determine the differentially expressed genes and then compare each level to the others using contrasts like this:
Code:dds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~ condition) dds <- DESeq(dds) results(dds, contrast=c("condition","A","B")) results(dds, contrast=c("condition","A","C")) results(dds, contrast=c("condition","A","D")) results(dds, contrast=c("condition","B","C")) results(dds, contrast=c("condition","B","D")) results(dds, contrast=c("condition","C","D"))
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The complexity of cancer is clearly demonstrated in the diverse ecosystem of the tumor microenvironment (TME). The TME is made up of numerous cell types and its development begins with the changes that happen during oncogenesis. “Genomic mutations, copy number changes, epigenetic alterations, and alternative gene expression occur to varying degrees within the affected tumor cells,” explained Andrea O’Hara, Ph.D., Strategic Technical Specialist at Azenta. “As...-
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