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DESeq: GLMs for RNA-Seq with interaction termsI have a reasonably large RNA-Seq dataset for a non-model plant, and want to fit a GLM with interaction terms:
expression ~ treatment + time + treatment:time In theory, I should be able to test the significance of each term by comparing a reduced model (dropping that term) with the full model above. It seems DESeq can handle this for main effect terms: it produces a vector of p-values with a reasonable distribution. See this example, using data from the pasilla package: data(pasillaGenes) design <- pData(pasillaGenes)[, c("condition", "type")] fullCountsTable <- counts(pasillaGenes) cdsFull <- newCountDataSet(fullCountsTable[1:1000,], design) cdsFull <- estimateSizeFactors(cdsFull) cdsFull <- estimateDispersions(cdsFull, method="pooled") fit1 <- fitNbinomGLMs(cdsFull, count ~ type + condition) fit0 <- fitNbinomGLMs(cdsFull, count ~ type) pvalsGLM <- nbinomGLMTest(fit1, fit0) However, when I try this with an interaction term included it gives clearly wrong results: every p value is either 0 or 1. fit1 <- fitNbinomGLMs(cdsFull, count ~ type + condition + type:condition) fit0 <- fitNbinomGLMs(cdsFull, count ~ type + type:condition) pvalsGLM <- nbinomGLMTest(fit1, fit0) So my questions: (a) Is this even possible in DESeq? (b) If so, can anyone spot the error in how I coded this? Thanks for any advice! |

Hi
you test for a rather odd contrast: Quote:
Code:
`fit1 <- fitNbinomGLMs(cdsFull, count ~ type + condition + type:condition)` |

Hi Simon !
Usually each coeficient in a GLM is associated with a p value, do you know how to get these p value from the GLM of DEseq ? |

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