Abstract
Gene expression of blood cells is a mixture of different cell types. Each cell type has its own specific profile, and different cell types might be correlated at the same time. Hence, decomposing the mixed expression profiles into cell typespecific expression profiles and their respective cellular proportions is a difficult problem. Previous studies usually build models on reference data that provide cellspecific profiles. We propose a Linear Mixed Model for Deconvolution (LMMD) to estimate the cell-specific expression level by modeling the reference profile and the mixture together in the same construction. We can also obtain the unknown cellular proportions at the same time. We establish the signature gene selection criteria for our LMMD model and compare it with four other models. LMMD has better performance when the reference data and mixture data are from different experiments.