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 prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis.

Results

We developed a new software framework for parallel matrix factorization in Version 3 of the CoGAPS R/Bioconductor package to over

SUBMITTER: Sherman TD 

PROVIDER: S-EPMC7556974 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature