QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data | Zendy

Juan Xie | Zendy; Anjun Ma | Zendy; Yu Zhang | Zendy; Bingqiang Liu | Zendy; Sha Cao | Zendy; Cankun Wang | Zendy; Jennifer Xu | Zendy; Chi Zhang | Zendy; Qin Ma | Zendy

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QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data

Author(s) -

Juan Xie,

Anjun Ma,

Yu Zhang,

Bingqiang Liu,

Sha Cao,

Cankun Wang,

Jennifer Xu,

Chi Zhang,

Qin Ma

Publication year - 2019

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/btz692

Subject(s) - biclustering , benchmark (surveying) , computer science , algorithm , data mining , rna seq , expression (computer science) , gaussian , mixture model , scale (ratio) , computational biology , cluster analysis , gene expression , artificial intelligence , gene , biology , transcriptome , genetics , cure data clustering algorithm , physics , correlation clustering , geodesy , quantum mechanics , programming language , geography

The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed.

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