Question: DESeq2 test over all timepoints?
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gravatar for Charles Determan Jr
6.0 years ago by
United States
Charles Determan Jr140 wrote:
Greetings, I have used the DESeq package previously and have been recently using DESeq2. I am particularly interested in repeated measures designs and was wondering about applications with DESeq2. I have read through the manual and tried searching the archives but couldn't find too much direction for testing over all timepoints instead of just one at a time (ANOVA-like approach). Reading the edgeR manual, it provides an example in section 3.3.4 that tests whether a treatment has an effect at any time by taking multiple coefficients (i.e. lrt <- glmLRT(fit, coef=5:6)). I attempted something similar with DESeq2: res <- results(dds, name=resultsNames(dds)[5:6] but I got the warning message saying only the first element used: Warning message:In if (paste0("WaldPvalue_", name) %in% names(mcols(object))) { : the condition has length > 1 and only the first element will be used Is there functionality with DESeq for looking over all timepoints or should I stick to using edgeR for these types of experimental designs? For context, I prefer to use multiple techniques instead of just one for further support given that no standard exists. Many thanks, -- Charles Determan Integrated Biosciences PhD Candidate University of Minnesota [[alternative HTML version deleted]]
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ADD COMMENTlink modified 6.0 years ago by Michael Love24k • written 6.0 years ago by Charles Determan Jr140
Answer: DESeq2 test over all timepoints?
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gravatar for Michael Love
6.0 years ago by
Michael Love24k
United States
Michael Love24k wrote:
hi Charles, On Tue, Jul 9, 2013 at 3:59 PM, Charles Determan Jr <deter088 at="" umn.edu=""> wrote: > Greetings, > > I have used the DESeq package previously and have been recently using > DESeq2. I am particularly interested in repeated measures designs and was > wondering about applications with DESeq2. I have read through the manual > and tried searching the archives but couldn't find too much direction for > testing over all timepoints instead of just one at a time (ANOVA- like > approach). Reading the edgeR manual, it provides an example in section > 3.3.4 that tests whether a treatment has an effect at any time by taking > multiple coefficients (i.e. lrt <- glmLRT(fit, coef=5:6)). I attempted > something similar with DESeq2: > > res <- results(dds, name=resultsNames(dds)[5:6] > > but I got the warning message saying only the first element used: > > Warning message:In if (paste0("WaldPvalue_", name) %in% > names(mcols(object))) { : > the condition has length > 1 and only the first element will be used > I should clean up the code to provide a warning here, as the results() function should only accept a character vector of length 1 for the argument 'name'. The proper way to test for the significance of multiple coefficients at once is to use the nbinomLRT() function in DESeq2 and specify a reduced formula. To test whether the treatment effect at all times is different than at the baseline time, the reduced formula would remove the interaction term between treatment and time, so: design(dds) <- formula(~ time + treatment + treatment:time) dds <- estimateSizeFactors(dds) dds <- estimateDispersions(dds) dds <- nbinomLRT(dds, reduced = formula(~ time + treatment)) res <- results(dds) If you presume that the treatment effect is the same at all times, you can test whether the treatment effect is equal to 0 with: # using the Wald test and coefficient shrinkage design(dds) <- formula(~ time + treatment) dds <- DESeq(dds) res <- results(dds) # or using the likelihood ratio test as in the previous example design(dds) <- formula(~ time + treatment) dds <- estimateSizeFactors(dds) dds <- estimateDispersions(dds) dds <- nbinomLRT(dds, reduced = formula(~ time)) res <- results(dds) The main difference here between the Wald and LRT tests is the shrinkage of estimated log2 fold changes to 0 using the default DESeq() function/Wald test. I will add more examples to the vignette to better explain these cases of testing multiple coefficients. Mike
ADD COMMENTlink written 6.0 years ago by Michael Love24k
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