Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering
Abstract Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the structure for a population of cells is hierarchical, whic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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BMC
2020-05-01
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| Series: | Genome Biology |
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| Online Access: | http://link.springer.com/article/10.1186/s13059-020-02027-x |
| _version_ | 1818241383598653440 |
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| author | Zhijin Wu Hao Wu |
| author_facet | Zhijin Wu Hao Wu |
| author_sort | Zhijin Wu |
| collection | DOAJ |
| description | Abstract Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the structure for a population of cells is hierarchical, which could result in misleading evaluation results. In this work, we develop two new metrics that take into account the hierarchical structure of cell types. We illustrate the application of the new metrics in constructed examples as well as several real single cell datasets and show that they provide more biologically plausible results. |
| first_indexed | 2024-12-12T13:28:28Z |
| format | Article |
| id | doaj.art-49d765fbdc7244e898f1ba4a1131aa44 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-12T13:28:28Z |
| publishDate | 2020-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-49d765fbdc7244e898f1ba4a1131aa442022-12-22T00:23:08ZengBMCGenome Biology1474-760X2020-05-0121111410.1186/s13059-020-02027-xAccounting for cell type hierarchy in evaluating single cell RNA-seq clusteringZhijin Wu0Hao Wu1Department of Biostatistics, Brown UniversityDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory UniversityAbstract Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the structure for a population of cells is hierarchical, which could result in misleading evaluation results. In this work, we develop two new metrics that take into account the hierarchical structure of cell types. We illustrate the application of the new metrics in constructed examples as well as several real single cell datasets and show that they provide more biologically plausible results.http://link.springer.com/article/10.1186/s13059-020-02027-xGene expressionSingle cell RNA-seqClustering |
| spellingShingle | Zhijin Wu Hao Wu Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering Genome Biology Gene expression Single cell RNA-seq Clustering |
| title | Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering |
| title_full | Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering |
| title_fullStr | Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering |
| title_full_unstemmed | Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering |
| title_short | Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering |
| title_sort | accounting for cell type hierarchy in evaluating single cell rna seq clustering |
| topic | Gene expression Single cell RNA-seq Clustering |
| url | http://link.springer.com/article/10.1186/s13059-020-02027-x |
| work_keys_str_mv | AT zhijinwu accountingforcelltypehierarchyinevaluatingsinglecellrnaseqclustering AT haowu accountingforcelltypehierarchyinevaluatingsinglecellrnaseqclustering |