| Autor: |
Koki Tsuyuzaki, Hiroyuki Sato, Kenta Sato, Itoshi Nikaido |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Genome Biology, Vol 21, Iss 1, Pp 1-17 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1474-760X |
| DOI: |
10.1186/s13059-019-1900-3 |
| Popis: |
Abstract Background Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation time is long and consumes large amounts of memory. Results In this work, we review the existing fast and memory-efficient PCA algorithms and implementations and evaluate their practical application to large-scale scRNA-seq datasets. Our benchmark shows that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, memory-efficient, and more accurate than the other algorithms. Conclusion We develop a guideline to select an appropriate PCA implementation based on the differences in the computational environment of users and developers. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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