DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data

Cell-type-specific genes are often strongly correlated in expression - an informative yet underexplored property of single-cell data. Here, the authors leverage gene expression correlations to develop DUBStepR, a feature selection method for accurately clustering single-cell data.

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Detalles Bibliográficos
Autores principales:	Bobby Ranjan, Wenjie Sun, Jinyu Park, Kunal Mishra, Florian Schmidt, Ronald Xie, Fatemeh Alipour, Vipul Singhal, Ignasius Joanito, Mohammad Amin Honardoost, Jacy Mei Yun Yong, Ee Tzun Koh, Khai Pang Leong, Nirmala Arul Rayan, Michelle Gek Liang Lim, Shyam Prabhakar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/679d215e24a04b659dd516970921da96
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DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data

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