REFCON: Reference-free and robust copy number inference in single-cell tumor transcriptomes
TL;DR
REFCON is introduced, a deep-learning model that enables reference-free copy number profiling from scRNA-seq data collected without matched normals, and profiles pure tumors, generalizes to unseen tissues and platforms, and stays robust to cohort composition.
Abstract
Single-cell RNA sequencing (scRNA-seq) is widely used to infer copy number profiles from tumor cells. Existing methods build on a reference-based normalization paradigm: normalizing each tumor cell against a reference of normal cells, whether supplied, in-sample, or synthesized. This makes them reference-dependent and as a result, sensitive to cohort composition, and prone to false positives. To address these limitations, we introduce REFCON, a deep-learning model that enables reference-free copy number profiling from scRNA-seq data. REFCON estimates local copy-number deviations and jointly optimizes them into a genome-wide per-cell profile. It profiles pure tumors, generalizes to unseen tissues and platforms, and stays robust to cohort composition. Predicted copy number profiles distinguish malignant cells with high specificity, producing far fewer false-positive calls, and improve clonal reconstruction. The model can also benefit from reference cells when available, turning a field requirement into an optional refinement. Hence, REFCON extends reliable per-cell copy number profiling to the scRNA-seq data collected without matched normals.