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

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ID: 331590
2026
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Abstract
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. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7212433309 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chaoyi Long, Jingxian Li, Bingqiang Liu, Duanchen Sun, Guojun Li
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag743
URL
https://doi.org/10.1093/bioinformatics/btag743
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