Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes

Zhao, Konghao; Grayson, Jason M.; Khuri, Natalia

doi:10.3390/jpm13020183

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…Undeniably, the incorporation of graph-based clustering algorithms has emerged as the prevailing trend for clustering large-scale single-cell data. However, these methods often exhibit inconsistent performance, with room for improvement in terms of clustering scalability and robustness in diverse application scenarios [ 3 , 24 ]. FlowGrid's clustering framework exhibits unstable performance concerning varying feature quantities of the data; phenograph encounter difficulties when dealing with highly imbalanced data, and although PARC exhibits high efficiency, uncertainties persist regarding clustering precision [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

CDSKNNXMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure

Ren,

Lyu,

Guo

et al. 2024

J Transl Med

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Background Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency. Methods We propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure. Results We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods. Conclusions The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.

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