Exploring high-dimensional biological data with sparse contrastive principal component analysis | Zendy

Philippe Boileau | Zendy; Nima S. Hejazi | Zendy; Sandrine Dudoit | Zendy

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Exploring high-dimensional biological data with sparse contrastive principal component analysis

Author(s) -

Philippe Boileau,

Nima S. Hejazi,

Sandrine Dudoit

Publication year - 2020

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/btaa176

Subject(s) - principal component analysis , computer science , dimensionality reduction , bioconductor , software , data mining , source code , component (thermodynamics) , curse of dimensionality , r package , biological data , artificial intelligence , pattern recognition (psychology) , machine learning , bioinformatics , biology , biochemistry , physics , thermodynamics , gene , programming language , operating system , computational science

Statistical analyses of high-throughput sequencing data have re-shaped the biological sciences. In spite of myriad advances, recovering interpretable biological signal from data corrupted by technical noise remains a prevalent open problem. Several classes of procedures, among them classical dimensionality reduction techniques and others incorporating subject-matter knowledge, have provided effective advances. However, no procedure currently satisfies the dual objectives of recovering stable and relevant features simultaneously.

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