Dear List, I have a couple of covariates in conjunction with my gene expression values. I want to know, whether some of the covariates have an influence on the expression values. The GlobalAncova package seems to be able to answer this question. "data" is a matrix (genes x samples) with gene expression values. "pData" is a data frame with several covariates (factors as well as numeric values). One factor is "Hyb" and describes the day of hybridization. I tried the following: > ga.Hyb <- GlobalAncova(xx=data, formula.full=~Class+Hyb, formula.red=~Class, model.dat=pData, perm=perm, test.genes=NULL); The call above leads to the error message "Error in solve.default(t(D) %*% D) : Lapack routine dgesv: system is exactly singular". Is it possible to use a pseudoinverse within GlobalAncova to get a parameter estimation in spite of the singularity? I read that GlobalAncova uses ordinary least squares like lm() does. lm() can handle such situations. Regards, Hans-Ulrich