Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data - Galindo-Hernández, Raúl; Rodríguez-Vázquez, Katya; Galán-Vásquez, Edgardo; Hernández Castellanos, Carlos Ignacio (Briefings in bioinformatics, 2024)
Analyzing gene expression data helps the identification of significant biological relationships in genes. With a growing number of open biological datasets available, it is paramount to use reliable and innovative methods to perform in-depth analyses of biological data and ensure that informed decisions are made based on accurate information. Evolutionary algorithms have been successful in the analysis of biological datasets. However, there is still room for improvement, and further analysis should be conducted. In this work, we propose Online-Adjusted EVOlutionary Biclustering algorithm (OAEVOB), a novel evolutionary-based biclustering algorithm that efficiently handles vast gene expression data. OAEVOB incorporates an online-adjustment feature that efficiently identifies significant groups by updating the mutation probability and crossover parameters. We utilize measurements such as Pearson correlation, distance correlation, biweight midcorrelation, and mutual information to assess the similarity of genes in the biclusters. Algorithms in the specialized literature do not address generalization to diverse gene expression sources. Therefore, to evaluate OAEVOB’s performance, we analyzed six gene expression datasets obtained from diverse sequencing data sources, specifically Deoxyribonucleic Acid microarray, Ribonucleic Acid (RNA) sequencing, and single-cell RNA sequencing, which are subject to a thorough examination. OAEVOB identified significant broad gene expression biclusters with correlations greater than $0.5$ across all similarity measurements employed. Additionally, when biclusters are evaluated by functional enrichment analysis, they exhibit biological functions, suggesting that OAEVOB effectively identifies biclusters with specific cancer and tissue-related genes in the analyzed datasets. We compared the OAEVOB’s performance with state-of-the-art methods and outperformed them showing robustness to noise, overlapping, sequencing data sources, and gene coverage.