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

Hussain Ahmed Chowdhury

doi:10.1016/j.compbiomed.2021.104820

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

2021-10-01

7

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AI

H

Hussain Ahmed Chowdhury

D

Dhruba K. Bhattacharyya *

J

Jugal Kalita

DOI：10.1016/j.compbiomed.2021.104820

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Abstract

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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. We 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. Our method does not require user input. However, it requires settings of threshold, which are benchmarked after performing extensive experiments. We 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. UICPC is available as an R package and can be downloaded by clicking the link https://sites.google.com/view/hussinch owdhury/software.

Keywords:

Clustering

scRNA-seq

NGS

Bioinformatics

Computational biology

Machine learning

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

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