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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed a new software framework for parallel matrix factorization in Version 3 of the CoGAPS R\/Bioconductor package to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This parallelization framework provides asynchronous updates for sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Altogether our new software enhance the efficiency of the CoGAPS Bayesian matrix factorization algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-020-03796-9","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T04:07:12Z","timestamp":1602648432000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures"],"prefix":"10.1186","volume":"21","author":[{"given":"Thomas D.","family":"Sherman","sequence":"first","affiliation":[]},{"given":"Tiger","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3204-342X","authenticated-orcid":false,"given":"Elana J.","family":"Fertig","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"3796_CR1","doi-asserted-by":"crossref","unstructured":"Ahn S, et al. Large-scale distributed Bayesian matrix factorization using stochastic gradient MCMC. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining\u2014KDD\u201915. Sydney: ACM Press; 2015. p. 9\u201318.","DOI":"10.1145\/2783258.2783373"},{"key":"3796_CR2","unstructured":"Bo Li, et al. Census of immune cells. Broad Inst. Mass. Inst. Technol. Howard Hughes Med. Inst. https:\/\/data.humancellatlas.org\/explore\/projects\/cc95ff89-2e68-4a08-a234-480eca21ce79. Accessed 2019"},{"key":"3796_CR3","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1016\/j.neuron.2019.04.010","volume":"102","author":"BS Clark","year":"2019","unstructured":"Clark BS, et al. Single-cell RNA-seq analysis of retinal development identifies NFI factors as regulating mitotic exit and late-born cell specification. 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