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

ABSTRACT:

Motivation

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.

Results

We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving

SUBMITTER: Xie J 

PROVIDER: S-EPMC8215922 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature