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ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

1
Department of Computer Science & Engineering, Aliah University, Newtown, WB-700156, India
2
Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
3
Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
*
Author to whom correspondence should be addressed.
Received: 2 August 2017 / Revised: 12 December 2017 / Accepted: 12 December 2017 / Published: 28 December 2017
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)
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Abstract

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures—weighted rank-based Jaccard and Cosine measures—and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm—RANWAR—was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data. View Full-Text
Keywords: gene co-expression modules; Limma; association rule mining; dynamic tree cut method; gene expression markers; lung squamous cell carcinoma gene co-expression modules; Limma; association rule mining; dynamic tree cut method; gene expression markers; lung squamous cell carcinoma
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Mallik, S.; Zhao, Z. ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis. Genes 2018, 9, 7.

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