Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data
Abstract
Single-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, DR should preserve the biological information while discarding the noise. However, if the DR is to be used directly to gain biological insight it must also be interpretable—that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 18, 2022
- Source ID
- 10.1093/bioinformatics/btac136
Entities
People
- Dennis Kostka
- Maria Chikina
- Maziyar Baran Pouyan
- Weiguang Mao
Organizations
- Defense Advanced Research Projects Agency
- Defense Health Agency
- National Institutes of Health
- University of Pittsburgh