I have a very noisy data set that I am having trouble thresholding to get useful genes, so I am resorting to using missing values. (Yes, I could impute, but my n is not huge and consists of four non-distinct groups). My question is: How does limma handle missing data? It doesn't mind NA's coming in, but does it correct for the different df at each gene given the number of NA's? Thanks, Sean

Hi Sean, >I have a very noisy data set that I am having trouble thresholding to get >useful genes, so I am resorting to using missing values. (Yes, I could >impute, but my n is not huge and consists of four non-distinct groups). My >question is: > >How does limma handle missing data? > When fitting gene-wise linear models in limma using lmFit(), missing values are removed from the response vector of log-ratios, and the corresponding row/s of the design matrix are removed before the linear model is fitted to each gene. >It doesn't mind NA's coming in, but >does it correct for the different df at each gene given the number of NA's? > So yes, the degrees of freedom will depend on the number of missing values for each gene. Best wishes, Matt Ritchie >Thanks, >Sean >