xCell 2.0: robust algorithm for cell type proportion estimation predicts response to immune checkpoint blockade
Almog Angel, Loai Naom, Shir Nabet-Levy, Dvir Aran
Abstract
BACKGROUND: Accurate estimation of cell type proportions from bulk gene expression data is essential for understanding the cellular heterogeneity underlying complex tissues and diseases. Here, we introduce xCell 2.0, an advanced version of the xCell algorithm, featuring a training function that permits the utilization of any reference dataset. xCell 2.0 generates cell type gene signatures using an improved methodology, including automated handling of cell type dependencies and more robust signature generation. RESULTS: We benchmark xCell 2.0 against eleven popular deconvolution methods using nine human and mouse reference sets and 26 validation datasets, encompassing 1711 samples and 67 cell types. Additionally, we validate xCell 2.0 using the independent Deconvolution DREAM Challenge dataset. xCell 2.0 outperforms all other tested methods across distinct reference datasets, demonstrating superior accuracy and consistency across diverse biological contexts. xCell 2.0 also shows the best performance in minimizing spillover effects between related cell types. In a test example of pan-cancer immune cell checkpoint blockage response prediction, xCell 2.0-derived TME features significantly improve prediction accuracy compared to models using only cancer type and treatment information, and outperformed other deconvolution methods and established prediction scores. CONCLUSIONS: xCell 2.0 is a versatile and robust tool for cell type deconvolution that maintains high performance across various reference types and biological contexts. It is available both via a locally hosted web application and as a Bioconductor-compatible package, equipped with a large collection of pre-trained cell type signatures for human and mouse research.