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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 /Â 30Â days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Lopez, R. et al. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018). This paper presented scVI, a variational autoencoder designed to model the specific characteristics of single-cell gene expression data.
Gayoso, A. et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022). This article introduces scvi-tools, a Python package for rapidly prototyping probabilistic models for single-cell data.
Wolf, F., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018). This paper introduces scanpy, one of the most widely used software packages for analyzing single-cell data.
DeTomaso, D. & Yosef, N. Hotspot identifies informative gene modules across modalities of single-cell genomics. Cell Systems 12, 446–456 (2021). This paper presents Hotspot, an explainable AI method for interpreting the latent spaces of representation learning methods for single-cell data.
Virshup, I. et al. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat. Biotechnol. 41, 604–606 (2023). This article describes the scverse consortium of software packages for single-cell data analysis.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Weinberger, E., Lin, C. & Lee, S.-I. Isolating salient variations of interest in single-cell data with contrastiveVI. Nat. Methods https://doi.org/10.1038/s41592-023-01955-3 (2023).
Rights and permissions
About this article
Cite this article
Deep-learning-based isolation of perturbation-induced variations in single-cell data. Nat Methods 20, 1287–1288 (2023). https://doi.org/10.1038/s41592-023-01956-2
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41592-023-01956-2
