CanSig: Discovering de novo shared transcriptional programs in single cancer cells

Abstract

Single-cell RNA sequencing (scRNA-seq) facilitates the discovery of gene signatures that define cell states across patients, which could be used in patient stratification and drug discovery. However, the lack of standardization in computational methodologies to analyse these data impedes the reproducibility of signature detection. To address this, we developed CanSig, a comprehensive benchmarking tool that evaluates methods for identifying transcriptional signatures in cancer. CanSig integrates metrics for batch correction and biological signal conservation with a gene signature correlation metric to score according to rediscovery, cross-dataset reproducibility, and clinical relevance. We applied CanSig to ten methods and to ten scRNA-seq datasets from four human cancer types--glioblastoma, breast cancer, lung adenocarcinoma, and cutaneous squamous cell carcinoma-- representing 116 patients and 105,000 malignant cells. Our results identify BBKNN as a leading method. We showed that the signatures identified with these methods correlate with clinically relevant outcomes, including patient survival and lymph node metastasis. Thus, CanSig establishes a standardized framework for reproducible cancer transcriptomics analysis.