scGen predicts single-cell perturbation responses.

Abstract:

:Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (out-of-sample) has yet been demonstrated. Here, we present scGen (https://github.com/theislab/scgen), a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. We show that scGen accurately models perturbation and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell-type and species-specific responses implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in a healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.

journal_name

Nat Methods

journal_title

Nature methods

authors

Lotfollahi M,Wolf FA,Theis FJ

doi

10.1038/s41592-019-0494-8

subject

Has Abstract

pub_date

2019-08-01 00:00:00

pages

715-721

issue

8

eissn

1548-7091

issn

1548-7105

pii

10.1038/s41592-019-0494-8

journal_volume

16

pub_type

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