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Putative cell type discovery from single-cell gene expression data
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DOI：10.1038/s41592-020-0825-9.png)
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摘要
En 中文
SCCAF automates the discovery of putative cell types and their feature genes using scRNA-seq 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 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.
Keyword:
CLASSIFICATION
STATES
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32.1
论文数:
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被引数:
12.7W
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