Dear list, I have a question about removing the effects of a small number of technical variables on an expression matrix. I have found that these technical variables, related to various aspects of the array and experiment, strongly correlate to the first few principal components of the expression matrix. Now I have already succesfully removed batch effects using the ComBat function and was wondering if one could also remove confounding continous technical variables by using ComBat: - Is it a valid approach to use ComBat for this? - If so, how can ComBat be used for this? - Or am I alternatively better off using linear models? My approach, was like this: #Call ComBat #technical.var is a numerical vector containing numeric measures of the technical variable for each sample #mod is the model matrix containing outcome and covariates of interest adjusted.exprs.matrix <- ComBat(exprs.matrix, batch=technical.var, mod = mod) However ComBat treats technical.var as a factor. Furthermore the function crashes: Error in solve.default(t(design) %*% design) : Lapack routine dgesv: system is exactly singular If ComBat isnt a good method for this am I better off fitting a linear model including the technical variables and variables of interest and remove the components due to technical variables, like this is done in the removeBatchEffect() function in limma? And possibly altering this function as to also handle continous variables? Or other alternatives? As the removal of batch effects will be incorporated in some sort of pipeline, I'm interested in returning a cleaned up expression matrix. So when further analysis is done on the dataset, for example linear regression, the technical variables don't have to be included anymore as covariates. I'd appreciate any input! Kind regards, Djie Thung [[alternative HTML version deleted]]