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 (Xi) and learn interpretable low-dimensional representations using dataset-specific (Vi> and shared metagene factors (W) and cell factor loadings (Hi). We developed an alternating nonnegative least squares (ANLS) algorithm to solve the iNMF optimization problem [2]: (Formula presented).
Cite
CITATION STYLE
Gao, C., & Welch, J. D. (2020). Iterative refinement of cellular identity from single-cell data using online learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12074 LNBI, pp. 248–250). Springer. https://doi.org/10.1007/978-3-030-45257-5_24
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
