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TransBic: bucket trend-preserving biclustering for finding local and interpretable expression patterns.

Jing Li1,2, Qinglin Mei3, Chaoxia Yang4

  • 1Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, No. 72 Binhai Road, Jimo Distinct, Qingdao 266237, Shandong, China.

Briefings in Bioinformatics
|February 5, 2025
PubMed
Summary

TransBic introduces a novel bucket trend-preserving (BTP) pattern for analyzing gene expression data. This method enhances biclustering by preserving specific patterns, improving accuracy in noisy conditions and identifying disease-related pathways.

Keywords:
acyclic tournament digraphbiclusteringlocal pattern identification

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biclustering analyzes high-dimensional expression data to find co-expression patterns.
  • Current methods lose specific pattern information and struggle with noisy data.
  • Identifying specific patterns is crucial for disease subtype and progression research.

Purpose of the Study:

  • To address limitations of existing biclustering methods.
  • To introduce a novel pattern generalization called bucket trend-preserving (BTP) patterns.
  • To develop an algorithm, TransBic, for extracting BTP-patterns.

Main Methods:

  • TransBic transforms bicluster extraction into finding common subgraphs in tournament digraphs.
  • The method derives acyclic tournament digraphs from expression matrices.
  • It identifies shared multipartite acyclic tournament subdigraphs.

Main Results:

  • TransBic outperforms existing tools in identifying biclusters, especially non-row-constant patterns under noise.
  • The algorithm successfully identifies disease-related pathways for type 2 diabetes, colorectal cancer, hepatocellular carcinoma, and breast cancer.
  • BTP-patterns capture specific gene up-regulation and down-regulation dynamics, unlike previous methods.

Conclusions:

  • TransBic offers a robust approach to biclustering, preserving crucial pattern information.
  • The method improves the analysis of complex diseases by accurately identifying disease-related pathways.
  • TransBic extends trend-preserving biclustering applications, revealing biological processes affected by disease risk factors.