LCA robustly reveals subtle diversity in large-scale single-cell RNA-seq data

Abstract

Single-cell RNA sequencing has emerged as a powerful tool for characterizing the cell-to-cell variation and dynamics. We present Latent Cellular Analysis (LCA), a machine learning-based analytical pipeline that features a dual-space model search with inference of latent cellular states, control of technical variations, cosine similarity measurement, and spectral clustering. LCA has proved to be robust, accurate, scalable, and powerful in revealing subtle diversity in cell populations.