Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data
Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to autom...
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| Vydáno v: | Genome Biology Ročník 23; číslo 1; s. 49 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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BioMed Central
08.02.2022
Springer Nature B.V BMC |
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| ISSN: | 1474-760X, 1474-7596, 1474-760X |
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| Abstract | Background
A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.
Results
We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.
Conclusions
We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from (
https://github.com/PYangLab/scCCESS
). |
|---|---|
| AbstractList | Abstract Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. Results We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. Conclusions We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. Results We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. Conclusions We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from (https://github.com/PYangLab/scCCESS). A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.BACKGROUNDA key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.RESULTSWe systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ).CONCLUSIONSWe identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. Results We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. Conclusions We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). |
| ArticleNumber | 49 |
| Author | Yang, Pengyi Yang, Jean Y. H. Yu, Lijia Cao, Yue |
| Author_xml | – sequence: 1 givenname: Lijia surname: Yu fullname: Yu, Lijia organization: School of Mathematics and Statistics, University of Sydney, Computational Systems Biology Group, Children’s Medical Research Institute, University of Sydney, Charles Perkins Centre, University of Sydney – sequence: 2 givenname: Yue surname: Cao fullname: Cao, Yue organization: School of Mathematics and Statistics, University of Sydney, Charles Perkins Centre, University of Sydney – sequence: 3 givenname: Jean Y. H. surname: Yang fullname: Yang, Jean Y. H. organization: School of Mathematics and Statistics, University of Sydney, Charles Perkins Centre, University of Sydney – sequence: 4 givenname: Pengyi orcidid: 0000-0003-1098-3138 surname: Yang fullname: Yang, Pengyi email: pengyi.yang@sydney.edu.au organization: School of Mathematics and Statistics, University of Sydney, Computational Systems Biology Group, Children’s Medical Research Institute, University of Sydney, Charles Perkins Centre, University of Sydney |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35135612$$D View this record in MEDLINE/PubMed |
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A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical... A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for... Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical... BACKGROUND: A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical... Abstract Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be... |
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| SubjectTerms | Algorithms Animal Genetics and Genomics Benchmarking Bioinformatics Biomedical and Life Sciences Cluster Analysis Clustering data collection Datasets Evolutionary Biology Gene expression Gene Expression Profiling - methods genome Heuristic Human Genetics Life Sciences memory Methods Microbial Genetics and Genomics Performance evaluation Plant Genetics and Genomics RNA sequence analysis Sequence Analysis, RNA - methods Single-Cell Analysis - methods |
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| Title | Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data |
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