xCell: Digitally portraying the tissue cellular heterogeneity landscape

Abstract

Tissues are complex milieu consisting of numerous cell-types. Numerous recent methods attempt to enumerate cell subsets from transcriptomes. However, available method used limited source for training and displayed only partial portrayal of the full cellular landscape. Here we present xCell, a novel gene-signature based method for inferring 64 immune and stroma cell-types. We harmonized 1,822 pure human cell-types transcriptomes from various sources, employed curve fitting approach for linear comparison of cell-types, and introduced a novel spillover compensation technique for separating between cell-types. Using extensive in silico analyses and comparison to cytometry immunophenotyping we show that xCell outperforms other methods: http://xCell.ucsf.edu/.