身份(音乐)
细胞
计算生物学
电池类型
鉴定(生物学)
生物
基因
理论(学习稳定性)
计算机科学
推论
遗传学
人工智能
机器学习
声学
植物
物理
作者
Hani Jieun Kim,Kevin Wang,Carissa Chen,Yingxin Lin,Patrick Tam,David Lin,Jean Yang,Pengyi Yang
标识
DOI:10.1038/s43588-021-00172-2
摘要
The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells. Defining cell identity is a fundamental task in dissecting the cellular heterogeneity in single-cell data. Here the authors developed Cepo, a method to uncover cell identity genes and enhance the retrieval of cellular identities from scRNA-seq data.
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