Impact of similarity metrics on single-cell RNA-seq data clustering
Abstract
Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on individual cells from complex samples. A common goal in scRNA-seq data analysis is to discover and characterise cell types, typically through clustering methods. The quality of the clustering therefore plays a critical role in biological discovery. While numerous clustering algorithms have been proposed for scRNA-seq data, fundamentally they all rely on a similarity metric for categorising individual cells. Although several studies have compared the performance of various clustering algorithms for scRNA-seq data, currently there is no benchmark of different similarity metrics and their influence on scRNA-seq data clustering. Here, we compared a panel of similarity metrics on clustering a collection of annotated scRNA-seq datasets. Within each dataset, a stratified subsampling procedure was applied and an array of evaluation measures was employed to assess the similarity metrics. This produced a highly reliable and reproducible consensus on their performance assessment. Overall, we found that correlation-based metrics (e.g. Pearson’s correlation) outperformed distance-based metrics (e.g. Euclidean distance). To test if the use of correlation-based metrics can benefit the recently published clustering techniques for scRNA-seq data, we modified a state-of-the-art kernel-based clustering algorithm (SIMLR) using Pearson’s correlation as a similarity measure and found significant performance improvement over Euclidean distance on scRNA-seq data clustering. These findings demonstrate the importance of similarity metrics in clustering scRNA-seq data and highlight Pearson’s correlation as a favourable choice. Further comparison on different scRNA-seq library preparation protocols suggests that they may also affect clustering performance. Finally, the benchmarking framework is available at http://www.maths.usyd.edu.au/u/SMS/bioinformatics/software.html.
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A recent comparison has suggested that correlation-based distances may outperform other distance metrics when used with k-means or as the basis for Gaussian kernels.
“A recent comparison has suggested that correlation‐based distances may outperform other distance metrics when used with k‐means or as the basis for Gaussian kernels (Kim et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Kim et al, 2018
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- Authors
- Kim, T., Chen, I. R., Lin, Y., Wang, A. Y.-Y., Yang, J. Y. H., & Yang, P.
- Affiliations
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
- The University of Sydney (AU)
- Corresponding author
- Yang, Pengyi
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-08-22
- Volume
- 20
- Issue
- 6
- Pages
- 2316-2326
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2888562574
- Cited by
- 138
- References
- 61
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 16 · 2023: 25 · 2022: 25 · 2021: 24 · 2020: 12 · 2019: 12 · 2018: 1
- FWCI
- 4.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Australian Research Council Discovery Early Career Researcher Award (DE170100759)
- Australian Research Council Discovery Projects (DP170100654)
- National Health and Medical Research Council Career Development Fellowships (1111338)
- Judith and David Coffey Life Lab
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- single-cell RNA sequencing, cell clustering, similarity measures, Pearson correlation, Euclidean distance, kernel clustering, benchmarking, cell type discovery, library preparation protocols, gene expression profiling
- MeSH terms
- Algorithms, Humans, Cluster Analysis, Sequence Analysis, RNA
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