Benchmarking principal component analysis for large-scale single-cell RNA-sequencing
Keyword(s):
Rna Seq
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AbstractPrincipal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but large-scale scRNA-seq datasets require long computational times and a large memory capacity.In this work, we review 21 fast and memory-efficient PCA implementations (10 algorithms) and evaluate their application using 4 real and 18 synthetic datasets. Our benchmarking showed that some PCA algorithms are faster, more memory efficient, and more accurate than others. In consideration of the differences in the computational environments of users and developers, we have also developed guidelines to assist with selection of appropriate PCA implementations.
2018 ◽
Vol 25
(12)
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pp. 1365-1373
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Keyword(s):
2018 ◽
Vol 43
(2)
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pp. 814-828
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2015 ◽
Vol 14
(4)
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pp. 15981-15987
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Keyword(s):