CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

Abstract Background Bayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohib...

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Bibliographic Details
Main Authors: Thomas D. Sherman, Tiger Gao, Elana J. Fertig
Format: Article
Language:English
Published: BMC 2020-10-01
Series:BMC Bioinformatics
Subjects:
Single cell
Matrix factorization
Pattern detection
Unsupervised learning
Online Access:http://link.springer.com/article/10.1186/s12859-020-03796-9

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http://link.springer.com/article/10.1186/s12859-020-03796-9

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