ArticleBioinformatics (Oxford, England)2020
QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.PLoS computational biology · 2026Article
- A novel biclustering algorithm for mining m6A co-methylation patterns based on beta-binomial distribution and data screening strategy.PLoS computational biology · 2026Article
- scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis.Statistics and computing · 2025Article
- TransBic: bucket trend-preserving biclustering for finding local and interpretable expression patterns.Briefings in bioinformatics · 2024Article
- Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.Briefings in bioinformatics · 2024Article
- Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers.Nature communications · 2024Article
- A parameter free relative density based biclustering method for identifying non-linear feature relations.Heliyon · 2024Article
- CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics.Nucleic acids research · 2024Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans.BMC bioinformatics · 2024Article
- Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.Cell reports methods · 2024Article
- Article
- Biclustering for Epi-Transcriptomic Co-functional Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- G-bic: generating synthetic benchmarks for biclustering.BMC bioinformatics · 2023Article
- RUBic: rapid unsupervised biclustering.BMC bioinformatics · 2023Article
- ARBic: an all-round biclustering algorithm for analyzing gene expression data.NAR genomics and bioinformatics · 2023Article
- Robust integrative biclustering for multi-view data.Statistical methods in medical research · 2022Article
- A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis.BMC bioinformatics · 2022Article
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9 authors.
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Abstract
motivationThe 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.
resultsWe 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. AVAILABILITY AND IMPLEMENTATION: The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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