Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets

Abstract

BackgroundRobust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. ResultsWe established a novel gene expression similarity-based workflow for the discovery of immune cell type signatures that leverages multiple datasets, here four single cell expression datasets from three different cancer types. We used our immune cell signatures to train random forest classifiers for immune cell type assignment of single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in two independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in our random forest approach. Discussion and conclusionWe demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for 90% of all genes whose expression profiles have not been used for cell type classification.