scGen predicts single-cell perturbation responses

Nature Methods
Mohammad LotfollahiFabian J Theis

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.

References

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Citations

Jan 15, 2020·Cold Spring Harbor Perspectives in Biology·Iwo Kucinski, Berthold Gottgens
Sep 26, 2020·Molecular Systems Biology·Romain LopezNir Yosef
Dec 25, 2019·Current Genetic Medicine Reports·Fabian V Filipp
Aug 5, 2020·Nature Biotechnology·Volker BergenFabian J Theis
Sep 25, 2020·Nature·Monika LitviňukováSarah A Teichmann
Jan 14, 2021·Stem Cell Reports·Owen RackhamChristine A Wells
Jan 1, 2021·Bioinformatics·Mohammad LotfollahiF Alexander Wolf
Feb 17, 2021·Nature Communications·Anastasiya BelyaevaCaroline Uhler
Apr 4, 2021·Seminars in Cell & Developmental Biology·Alecia-Jane Twigger, Walid T Khaled
Jun 18, 2021·Cell Systems·Yuge JiFabian J Theis
Aug 20, 2021·Cell Systems·Matthias MannMaximilian T Strauss
Sep 1, 2021·Nature Biotechnology·Mohammad LotfollahiFabian J Theis
Oct 14, 2021·International Journal of Molecular Sciences·David PratellaSilvia Bottini
Oct 31, 2021·Genome Biology·Luke Zappia, Fabian J Theis

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Datasets Mentioned

BETA
GSE96583
GSE92332
E-MTAB-6754

Methods Mentioned

BETA
scRNA-seq

Software Mentioned

Scanpy
scikit
BiomaRt
UMAP
Tensorflow
learn
scipy
scGen
ENSEMBL
CVAE

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