Deep-learning-based isolation of perturbation-induced variations in single-cell data

被引：0

作者：

Weinberger, Ethan ^{[1
]}

Lee, Su-In ^{[1
]}

机构：

[1] Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA

来源：

NATURE METHODS | 2023年 / 20卷 / 09期

关键词：

D O I：

10.1038/s41592-023-01956-2

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Single-cell perturbation screens are routinely conducted to study the effects of different perturbations on cellular state, yet such studies are easily confounded by nuisance sources of variation shared with control cells. We present a deep learning method that isolates perturbation-specific sources of variation, enabling a better understanding of the perturbation's effects.

引用

页码：1287 / 1288

页数：2

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