xCell: digitally portraying the tissue cellular heterogeneity landscape

TLDR

Tissues are complex milieus of many cell types, yet existing transcriptome‑based methods use limited training data and provide only a partial portrayal of the cellular landscape. We present xCell, a novel gene signature‑based method designed to infer 64 immune and stromal cell types. xCell harmonizes 1,822 pure human cell‑type transcriptomes, applies a curve‑fitting approach for linear comparison, and introduces a spillover‑compensation technique to separate cell types. Extensive in silico analyses and comparison to cytometry immunophenotyping demonstrate that xCell outperforms other methods and is available at http://xCell.ucsf.edu/.

Abstract

Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/ .