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

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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 expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq.

Original languageEnglish (US)
Pages (from-to)1143-1149
Number of pages7
JournalBioinformatics
Volume36
Issue number4
DOIs
StatePublished - Feb 15 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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