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Peer-reviewedCC BYBioinformatics (Oxford, England)

NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.

  • Chaoyi Long1
  • Jingxian Li2
  • Bingqiang Liu2
  • Duanchen Sun2,3
  • Guojun Li1
  1. 1Research Center for Mathematics and Interdisciplinary Sciences (Ministry of Education Frontiers Science Center for Nonlinear Expectations), Shandong University, Qingdao, China.
  2. 2School of Mathematics, Shandong University, Jinan, China.
  3. 3Shandong Key Laboratory of Cancer Digital Medicine, Jinan, China.
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Abstract

Motivation

High-throughput transcriptomic technologies generate large-scale gene expression datasets for functional module discovery. However, technical and biological noise can obscure coherent expression patterns and compromise module detection. Noise-tolerant methods are therefore needed to recover meaningful biclusters.Results

We developed NoiBic, a noise-tolerant biclustering algorithm for high-throughput transcriptomic data. NoiBic integrates a longest approximate common subsequence-based seed identification strategy with noise-aware column expansion to improve bicluster detection under noisy conditions. Across simulated datasets with controlled settings for bicluster coherency, matrix size, bicluster number, noise level, and overlap level, NoiBic accurately recovered planted biclusters, particularly under noisy and overlapping conditions. Evaluation on bulk and single-cell RNA-seq datasets demonstrated its ability to identify biologically meaningful gene modules and cell type-associated biclusters.Availability

NoiBic is implemented in C++17 and available at https://github.com/lcyi0208/NoiBic. The software version used in this study has been permanently archived on Zenodo at https://doi.org/10.5281/zenodo.22732377. Benchmark datasets are available at https://doi.org/10.5281/zenodo.21919392.

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Cite

BibTeXRIS
BibTeX
@article{long2026noibic,
  title = {{NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.}},
  author = {Chaoyi Long and Jingxian Li and Bingqiang Liu and Duanchen Sun and Guojun Li},
  journal = {Bioinformatics (Oxford, England)},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag743},
  url = {https://europepmc.org/article/MED/42841722},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.
AU  - Chaoyi Long
AU  - Jingxian Li
AU  - Bingqiang Liu
AU  - Duanchen Sun
AU  - Guojun Li
PY  - 2026
DA  - 2026/10/01
JO  - Bioinformatics (Oxford, England)
DO  - 10.1093/bioinformatics/btag743
UR  - https://europepmc.org/article/MED/42841722
AB  - Motivation  High-throughput transcriptomic technologies generate large-scale gene expression datasets for functional module discovery. However, technical and biological noise can obscure coherent expression patterns and compromise module detection. Noise-tolerant methods are therefore needed to recover meaningful biclusters.Results  We developed NoiBic, a noise-tolerant biclustering algorithm for high-throughput transcriptomic data. NoiBic integrates a longest approximate common subsequence-based seed identification strategy with noise-aware column expansion to improve bicluster detection under noisy conditions. Across simulated datasets with controlled settings for bicluster coherency, matrix size, bicluster number, noise level, and overlap level, NoiBic accurately recovered planted biclusters, particularly under noisy and overlapping conditions. Evaluation on bulk and single-cell RNA-seq datasets demonstrated its ability to identify biologically meaningful gene modules and cell type-associated biclusters.Availability  NoiBic is implemented in C++17 and available at https://github.com/lcyi0208/NoiBic. The software version used in this study has been permanently archived on Zenodo at https://doi.org/10.5281/zenodo.22732377. Benchmark datasets are available at https://doi.org/10.5281/zenodo.21919392.
SN  - 1367-4803
SN  - 1367-4811
ER  -