英国威康桑格研究所Sarah A. Teichmann、Alvis Brazma等研究人员,合作开发出利用单细胞基因表达数据发现潜在细胞类型的方法。该研究于2020年5月18日在线发表于《自然—方法学》。
Title: Putative cell type discovery from single-cell gene expression data
Author: Zhichao Miao, Pablo Moreno, Ni Huang, Irene Papatheodorou, Alvis Brazma, Sarah A. Teichmann
Issue&Volume: 2020-05-18
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.
DOI: 10.1038/s41592-020-0825-9
Source: https://www.nature.com/articles/s41592-020-0825-9
Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex