Abstract
The microRNAs and single-cell expression data embed relevant and unrelevant information about the genes. Finding the hidden gene expression pattern helps us to understand the functionality of a gene at molecular level. Particularly, gene expression pattern varies with the effect of external stimuli. Therefore, in this situation, soft clustering technique helps to detect multiple patterns of a gene at one time. In this work, a form of soft clustering approach is presented in assimilation with the fuzzy and cat swarm optimization (CSO) algorithm to find an optimal cluster center for clustering. Here, the task of clustering is formulated as the optimization of multiple cluster validity indexes. The proposed clustering algorithm (multiobjective fuzzy-based CSO) is then compared with some of state-of-the-art clustering technique on different gene expression dataset. A case study is conducted to identify the transcriptome or marker gene in each cell types of human urinary bladder single-cell RNA sequencing data. Experimental analysis of multiobjective fuzzy-based CSO clustering algorithm shows its effectiveness over other state-of-the-art clustering algorithm such as AL, K-means, GA-FCM, DE-FCM, PSO-FCM, and GWO-FCM.
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Acknowledgements
Principal Investigator (PI) acknowledges Sunrise Project with Ref: NECBH/ 2019-20/178 under North East Centre for Biological Sciences and Healthcare Engineering (NECBH) Twinning Outreach Programme hosted by Indian Institute of Technology Guwahati (IITG), Guwahati, Assam, funded by Department of Biotechnology (DBT), Ministry of Science and Technology, Govt. of India, with number BT/COE/34/SP28408/2018 for providing necessary financial support.
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Achom, A., Das, R., Gond, P., Pakray, P. (2022). A Fuzzy-Based Multiobjective Cat Swarm Optimization Algorithm: A Case Study on Single-Cell Data. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_21
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DOI: https://doi.org/10.1007/978-981-16-6624-7_21
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