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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
Biclustering of gene expression data by correlation-based scatter search.
Juan A Nepomuceno1, Alicia Troncoso, Jesús S Aguilar-Ruiz
1Dpt, Lenguajes y Sistemas Informáticos, ETSII, University of Seville, Avd, Reina Mercedes s/n, 41012, Seville, Spain. janepo@us.es.
Biodata Mining
|January 26, 2011
Summary
This study introduces a novel Scatter Search algorithm for biclustering gene expression data, effectively identifying shifting and scaling patterns missed by traditional methods. The approach enhances biological pattern discovery in microarray analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Microarray data analysis is crucial for understanding gene function.
- Biclustering identifies co-expressed genes under specific conditions.
- Existing metaheuristic methods often miss biologically relevant shifting and scaling patterns.
Purpose of the Study:
- To develop a novel biclustering algorithm for gene expression data.
- To address limitations of current methods in detecting shifting and scaling patterns.
- To improve the discovery of biologically significant gene expression patterns.
Main Methods:
- Utilized Scatter Search, an evolutionary optimization technique.
- Developed a new fitness function based on linear gene correlation.
- Incorporated an improvement method to select positively correlated genes.
Main Results:
- Successfully identified numerous biclusters exhibiting shifting and scaling patterns in real datasets (Yeast Cell Cycle, human B-cells lymphoma, Yeast Stress).
- Demonstrated the effectiveness of the proposed fitness function and algorithm.
- Compared performance against established biclustering algorithms (CC, OPSM, ISA, BiMax, xMotifs, Samba) using Gene Ontology.
Conclusions:
- The proposed Scatter Search algorithm with a correlation-based fitness function effectively discovers shifting and scaling biclusters.
- This method offers a significant advancement in identifying complex gene expression patterns.
- The findings contribute to a deeper understanding of gene regulation and function.
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