Putative cell type discovery from single-cell gene expression data
We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells, we simultaneously identify distinct cell groups...
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| Veröffentlicht in: | Nature methods 2020-06, Vol.17 (6), p.621-628 |
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| container_title | Nature methods |
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| creator | Miao, Zhichao Moreno, Pablo Huang, Ni Papatheodorou, Irene Brazma, Alvis Teichmann, Sarah A. |
| description | We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells, we simultaneously identify distinct cell groups and a weighted list of feature genes for each group. The differentially expressed feature genes discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterized by the feature genes as markers. Benchmarking using expert-annotated scRNA-seq datasets shows that our method automatically identifies the ‘ground truth’ cell assignments with high accuracy.
SCCAF automates the discovery of putative cell types and their feature genes using scRNA-seq data. |
| doi_str_mv | 10.1038/s41592-020-0825-9 |
| format | Article |
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Academic</collection><jtitle>Nature methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Miao, Zhichao</au><au>Moreno, Pablo</au><au>Huang, Ni</au><au>Papatheodorou, Irene</au><au>Brazma, Alvis</au><au>Teichmann, Sarah A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Putative cell type discovery from single-cell gene expression data</atitle><jtitle>Nature methods</jtitle><stitle>Nat Methods</stitle><addtitle>Nat Methods</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>17</volume><issue>6</issue><spage>621</spage><epage>628</epage><pages>621-628</pages><issn>1548-7091</issn><eissn>1548-7105</eissn><abstract>We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells, we simultaneously identify distinct cell groups and a weighted list of feature genes for each group. The differentially expressed feature genes discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterized by the feature genes as markers. Benchmarking using expert-annotated scRNA-seq datasets shows that our method automatically identifies the ‘ground truth’ cell assignments with high accuracy.
SCCAF automates the discovery of putative cell types and their feature genes using scRNA-seq data.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>32424270</pmid><doi>10.1038/s41592-020-0825-9</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-5988-7409</orcidid><orcidid>https://orcid.org/0000-0002-6294-6366</orcidid><orcidid>https://orcid.org/0000-0002-5777-9815</orcidid></addata></record> |
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| subjects | 631/114 631/114/2397 631/114/2404 631/114/794 631/208/199 Animals Automation Bioinformatics Biological Microscopy Biological Techniques Biomedical and Life Sciences Biomedical Engineering/Biotechnology Cluster Analysis Clustering Datasets as Topic Gene Expression Gene sequencing Genes Genetic research Ground truth Humans Learning algorithms Life Sciences Machine Learning Proteomics Reproducibility of Results Ribonucleic acid RNA RNA sequencing RNA-Seq - methods Single-Cell Analysis - methods Software |
| title | Putative cell type discovery from single-cell gene expression data |
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