UICPC – Centrality-based clustering for scRNA-seq data analysis without user input

scRNA-seq data analysis enables new possibilities for identification of novel cells, specific characterization of known cells and study of cell heterogeneity. The performance of most clustering methods especially developed for scRNA-seq is greatly influenced by user input. Researchers at the University of Colorado and Tezpur University propose a centrality-clustering method named UICPC and compare its performance with 9 state-of-the-art clustering methods on 11 real-world scRNA-seq datasets to demonstrate its effectiveness and usefulness in discovering cell groups. This method does not require user input. However, it requires settings of threshold, which are benchmarked after performing extensive experiments. The researchers observe that most compared approaches show poor performance due to high heterogeneity and large dataset dimensions. However, UICPC shows excellent performance in terms of NMI, Purity and ARI, respectively.

Availability – UICPC is available as an R package and can be downloaded at: https://sites.google.com/view/hussinchowdhury/software.

Chowdhury HA, Bhattacharyya DK, Kalita JK. (2021) UICPC: Centrality-based clustering for scRNA-seq data analysis without user input. Comput Biol Med [Epub ahead of print]. [abstract]

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