GSE: Graph similarity enhancement algorithm for single-cell RNA-seq data clustering
Pages 405 - 409
Abstract
RNA-seq contains rich information about individual even single cell, implies certain biology pattern vary in special time or space two dimensions, e.g. different life stage or environment. Byusing clustering and other computing methods, we can efficient analysis and decode those data applying to cancer diagnosis and treat, biological evolution and so on. However, RNA-seq data has features of super-high dimensions, less labeled samples and strong noise, which bring large challenges for clustering analysis. Therefore, we proposed a new clustering method GSE, which can efficient enhance the signal-to-noise ratio of input similarity matrix using diffusion process in weighted connection network to improve clustering performance. Comparing with latest clustering methods, our method has advantages in external clustering criterions NMI and ARI indicators. Meanwhile inadequacy and improved idea are given. Code can be downloaded from Git-hub.
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Index Terms
- GSE: Graph similarity enhancement algorithm for single-cell RNA-seq data clustering
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Published In
November 2019
528 pages
ISBN:9781450361910
DOI:10.1145/3378065
Copyright © 2019 ACM.
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In-Cooperation
- Guilin: Guilin University of Technology, Guilin, China
- Wuhan University of Technology: Wuhan University of Technology, Wuhan, China
- International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 April 2020
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ICIIP 2019
ICIIP 2019: 2019 4th International Conference on Intelligent Information Processing
November 16 - 17, 2019
China, China
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Overall Acceptance Rate 87 of 367 submissions, 24%
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