Volume 7, Issue 2, December 2019, Page: 11-21
Discovering Gene Co-Expression Modules Using Fuzzified Adjusted Rand Index
Taiwo Adigun, Department of Computer Science, University of Ibadan, Ibadan, Nigeria
Angela Makolo, Department of Computer Science, University of Ibadan, Ibadan, Nigeria
Received: May 20, 2019;       Accepted: Jun. 24, 2019;       Published: Aug. 6, 2019
DOI: 10.11648/j.cbb.20190702.11      View  158      Downloads  37
Abstract
Understanding the interrelationship among genes in a cellular system is fundamental to the investigation of cellular activities, because the interrelated genes are either functionally related, controlled by the same transcriptional regulatory process or generally take part in a common biological process, and most importantly are known to be co-expressed genes. Most latent Mtb genes have been discovered but their functions, interrelationship and correlations that will help to develop protocol (s) to tame the menace of tuberculosis disease at latency have not been fully uncovered. We have developed a computational technique called Fuzzified Adjusted Rand Index (FARI) to effectively discover the co-expressed genes from identified latent Mtb genes and perform functional analysis of the gene sets using an annotation database. FARI, a modification of Adjusted Rand index used to compare clustering results, is designed to analyze, establish and quantify the expression trend of two genes with different sample points. Rank matrix of all the genes in consideration is produced after each gene has been analyzed with others, and the rank matrix serves as the basis of the co-expression discovery. A synthetic gene expression dataset, the biological benchmark dataset (E. coli), and different set of genes containing latent Mtb genes from an experiment result were fed into the computational tool, and different gene sets (modules) representing co-expressed genes were discovered. The discovered gene modules from latent Mtb genes are used to uncover the hub genes and their molecular functions. We have been able to identify different co-expression network from this analysis and assign biological functional meanings to some of the important Mtb genes that emerge from the experiment. Also, discovering gene co-expression module births gene co-expression network, which is a preliminary step towards gene regulatory network discovery.
Keywords
Co-expression, Modules, Latent Mtb, Rank Matrix, Adjusted Rank Index
To cite this article
Taiwo Adigun, Angela Makolo, Discovering Gene Co-Expression Modules Using Fuzzified Adjusted Rand Index, Computational Biology and Bioinformatics. Vol. 7, No. 2, 2019, pp. 11-21. doi: 10.11648/j.cbb.20190702.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Luo F., Yang Y., Zhong J., Gao H., Khan L., Thompson D. K. and Zhou J.(2007). Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinformatics, 8: 299 doi: 10.1186/1471-2105-8-299.
[2]
Li J., Zhou D., Qiu W., Shi Y., Yang J., Chen S., Wang Q. and Pan H.(2018). Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design. Scientific Reports | (2018) 8: 622 | DOI: 10.1038/s41598-017-18705-z.
[3]
Jiang J., Sun X., Wu W., Li L., Wu H., Zhang L., Yu G. and Li Y. (2016). Construction and application of a co-expression network in Mycobacterium tuberculosis. Scientific Reports | 6: 28422 | DOI: 10.1038/srep28422.
[4]
Ruan J., Dean A. K., and Zhang W.(2010). A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Systems Biology 2010, 4: 8.
[5]
Roy S., Bhattacharyya D. K., and Kalita J. K. (2014). Reconstruction of gene co-expression network from microarray data using local expression patterns. BMC Bioinformatics 2014, 15 (Suppl 7): S10.
[6]
Ballouz S., Verleyen W. and Gillis J. (2015). Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics, 31 (13), 2015, 2123–2130 doi: 10.1093/bioinformatics/btv118.
[7]
Gibson S. M., Ficklin S. P., Isaacson S., Luo F., Feltus F. A. and Smith M. C. (2013). Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory. PLoS ONE 8 (2): e55871. doi: 10.1371/journal.pone 0055871.
[8]
Song L., Langfelder P. and Horvath S. (2012). Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 2012, 13: 328.
[9]
Villa-Vialaneix N., Liaubet L., Laurent T., Chere P., Gamot A. and SanCristobal m. (2013). The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs. PLoS ONE 8 (4): e60045. doi: 10.1371/journal.pone.0060045.
[10]
Grimaldi, M., Visintainer, R. and Jurman, G. (2011). RegnANN: Reverse Engineering Gene Networks Using Artificial Neural Networks. PLoS ONE, Vol. 6, Issue 12, e28646.
[11]
Mandal S., Khan A., Saha G., and Pal R. K. (2016) Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm. Advances in Bioinformatics Volume 2016, Article ID 5283937, 9 pages.
[12]
Raza K. and Alam M. (2016) Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network. Computational Biology and Chemistry, 64: 322-334.
[13]
Noman N., Palafox L., and Iba H., (2013) “Reconstruction of gene regulatory networks from gene expression data using decoupled recurrent neural network model,” in Natural Computing and Beyond: Winter School Hakodate 2011, Hakodate, Japan, March 2011 and 6th International Workshop on Natural Computing, Tokyo, Japan, March 2012, Proceedings, vol. 6 of Proceedings in Information and Communications Technology, pp. 93–103, Springer, Berlin, Germany, 2013.
[14]
Reimand, J., Arak, T., Adler, P., Kolberg, L., Reisberg, S., Peterson, H., Vilo, J. g: Profiler - a web server for functional interpretation of gene lists (2016 update) Nucleic Acids Research 2016; doi: 10.1093/nar/gkw199.
[15]
Thuong NTT, Dunstan SJ, Chau TTH, Thorsson V, Simmons CP, et al. (2008) Identification of Tuberculosis Susceptibility Genes with Human Macrophage Gene Expression Profiles. PLoS Pathog 4 (12): e1000229. doi: 10.1371/journal.ppat.1000229.
[16]
Farahbod F. and Eftekhari M. (2013) A New Clustering-Based Approach for Modeling Fuzzy Rule-Based Classification Systems. IJST, Transactions of Electrical Engineering, Vol. 37, No. E1, pp 67-77.
[17]
Priyono A., Ridwan M., Alias A. J., Rahmat R. A. O. K., Hassan A. and Ali M. A. M. (2005). Generation of Fuzzy Rules with Subtractive Clustering. Jurnal Teknologi, 43 (D) Dis. 2005: 143–153.
[18]
Jones R (2010) There Goes the (Gene Expression) Neighbourhood Theory. PLoS Biol 8 (11): e1001002. doi: 10.1371/journal.pbio.1001002.
[19]
Oliver B., Parisi M. and Clark D. (2002). Gene expression neighborhoods. Journal of Biology 2002, Volume 1, Issue 1, Article 4. http://jbiol.com/content/1/1/4.
[20]
Yuan, L., Chen, L., Qian, K., Qian, G., Wu, C., Wang, X. and Xiao Y. (2017). Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC). Genomics Data 14 (2017) 132–140.
Browse journals by subject