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Dostęp zamkniętyTruncated Robust Principal Component Analysis and Noise Reduction for Single Cell RNA Sequencing Data
| dc.abstract.en | The development of single cell RNA sequencing (scRNA-seq) has enabled innovative approaches to investigating mRNA abundances. In our study, we are interested in extracting the systematic patterns of scRNA-seq data in an unsupervised manner; thus, we have developed two extensions of robust principal component analysis (RPCA). First, we present a truncated version of RPCA (tRPCA), which is much faster and memory efficient. Second, we introduce a noise reduction in tRPCA with L2 regularization. Unlike RPCA that only considers a low-rank L and sparse S matrices, the proposed method can also extract a noise E matrix inherent in modern genomic data. We demonstrate its usefulness by applying our methods on the peripheral blood mononuclear cell scRNA-seq data. Particularly, the clustering of a low-rank L matrix showcases better classification of unlabeled single cells. Overall, the proposed variants are well suited for high-dimensional and noisy data that are routinely generated in genomics. |
| dc.affiliation | Uniwersytet Warszawski |
| dc.contributor.author | Gogolewski, Krzysztof |
| dc.contributor.author | Gambin, Anna |
| dc.contributor.author | Sykulski, Maciej |
| dc.contributor.author | Chung, Neo Christopher |
| dc.date.accessioned | 2024-01-26T11:07:06Z |
| dc.date.available | 2024-01-26T11:07:06Z |
| dc.date.issued | 2019 |
| dc.description.finance | Nie dotyczy |
| dc.description.number | 8 |
| dc.description.volume | 26 |
| dc.identifier.doi | 10.1089/CMB.2018.0255 |
| dc.identifier.issn | 1066-5277 |
| dc.identifier.uri | https://repozytorium.uw.edu.pl//handle/item/123838 |
| dc.identifier.weblink | https://www.liebertpub.com/doi/full-xml/10.1089/cmb.2018.0255 |
| dc.language | eng |
| dc.pbn.affiliation | computer and information sciences |
| dc.relation.ispartof | Journal of Computational Biology |
| dc.relation.pages | 782-793 |
| dc.rights | ClosedAccess |
| dc.sciencecloud | nosend |
| dc.subject.en | matrix decomposition |
| dc.subject.en | principal component analysis |
| dc.subject.en | robust PCA |
| dc.subject.en | single cell RNA-seq |
| dc.subject.en | unsupervised learning |
| dc.title | Truncated Robust Principal Component Analysis and Noise Reduction for Single Cell RNA Sequencing Data |
| dc.type | JournalArticle |
| dspace.entity.type | Publication |