Selecting single cell clustering parameter values using subsampling-based robustness metrics
Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflo...
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| Format: | Article |
| Language: | English |
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BMC
2021-02-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-03957-4 |
| _version_ | 1818611558369984512 |
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| author | Ryan B. Patterson-Cross Ariel J. Levine Vilas Menon |
| author_facet | Ryan B. Patterson-Cross Ariel J. Levine Vilas Menon |
| author_sort | Ryan B. Patterson-Cross |
| collection | DOAJ |
| description | Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness. |
| first_indexed | 2024-12-16T15:32:14Z |
| format | Article |
| id | doaj.art-b84d380938f047e4b749af13974c5eae |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-16T15:32:14Z |
| publishDate | 2021-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-b84d380938f047e4b749af13974c5eae2022-12-21T22:26:18ZengBMCBMC Bioinformatics1471-21052021-02-0122111310.1186/s12859-021-03957-4Selecting single cell clustering parameter values using subsampling-based robustness metricsRyan B. Patterson-Cross0Ariel J. Levine1Vilas Menon2Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, National Institutes of HealthSpinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, National Institutes of HealthDepartment of Neurology, Center for Translational and Computational Neuroimmunology, Columbia UniversityAbstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.https://doi.org/10.1186/s12859-021-03957-4Single cell RNAseqParameter selectionClusteringResolution |
| spellingShingle | Ryan B. Patterson-Cross Ariel J. Levine Vilas Menon Selecting single cell clustering parameter values using subsampling-based robustness metrics BMC Bioinformatics Single cell RNAseq Parameter selection Clustering Resolution |
| title | Selecting single cell clustering parameter values using subsampling-based robustness metrics |
| title_full | Selecting single cell clustering parameter values using subsampling-based robustness metrics |
| title_fullStr | Selecting single cell clustering parameter values using subsampling-based robustness metrics |
| title_full_unstemmed | Selecting single cell clustering parameter values using subsampling-based robustness metrics |
| title_short | Selecting single cell clustering parameter values using subsampling-based robustness metrics |
| title_sort | selecting single cell clustering parameter values using subsampling based robustness metrics |
| topic | Single cell RNAseq Parameter selection Clustering Resolution |
| url | https://doi.org/10.1186/s12859-021-03957-4 |
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