Robust classification of single-cell transcriptome data by nonnegative matrix factorization | Zendy

Chunxuan Shao | Zendy; Thomas Höfer | Zendy

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Robust classification of single-cell transcriptome data by nonnegative matrix factorization

Author(s) -

Chunxuan Shao,

Thomas Höfer

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw607

Subject(s) - non negative matrix factorization , computer science , matrix decomposition , pattern recognition (psychology) , artificial intelligence , transcriptome , principal component analysis , feature (linguistics) , computational biology , data mining , biology , gene , gene expression , genetics , linguistics , eigenvalues and eigenvectors , physics , philosophy , quantum mechanics

Single-cell transcriptome data provide unprecedented resolution to study heterogeneity in cell populations and present a challenge for unsupervised classification. Popular methods, like principal component analysis (PCA), often suffer from the high level of noise in the data.

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