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.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Miao Z,Moreno P,Huang N,Papatheodorou I,Brazma A,Teichmann SAdoi
10.1038/s41592-020-0825-9subject
Has Abstractpub_date
2020-06-01 00:00:00pages
621-628issue
6eissn
1548-7091issn
1548-7105pii
10.1038/s41592-020-0825-9journal_volume
17pub_type
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