Iterative Refinement of Cellular Identity from Single-Cell Data Using Online Learning

Chao Gao, Joshua D. Welch

Abstract: Recent experimental advances have enabled high-throughput single-cell measurement of gene expression, chromatin accessibility and DNA methylation. We previously employed integrative non-negative matrix factorization (iNMF) to jointly align multiple single-cell datasets (\(X_i\)) and learn interpretable low-dimensional representations using dataset-specific (\(V_i)\) and shared metagene factors (W) and cell factor loadings (\(H_i\)). We developed an alternating nonnegative least squares (ANLS) algorithm to solve the iNMF optimization problem [2]: