Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions

DOI10.1515/SAGMB-2019-0004zbMath1445.92228OpenAlexW2969201309WikidataQ90917641 ScholiaQ90917641MaRDI QIDQ2195267

Aslı Suner

Publication date: 8 September 2020

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/sagmb-2019-0004

zbMATH Keywords

clusteringperformance evaluationsingle cellRNA sequencing

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Uses Software

• Unnamed Item
• ggplot2
• WGCNA
• AS 136
• t-SNE
• SC3
• CIDR
• scater

Cites Work

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• Unnamed Item
• Unnamed Item
• Algorithm AS 136: A K-Means Clustering Algorithm
• The adjusted rand statistic: A SAS macro
• Support-vector networks
• A Method for Comparing Two Hierarchical Clusterings

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