ArticleNature methods2019
scGen predicts single-cell perturbation responses.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 365 papers, 1 of them a synthesis that pooled it.
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Who cites it
365 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- A systematic transcriptomic atlas and predictive framework for metabolite-driven T cell state transitions.iScience · 2026Article
- scLDM: a conditional diffusion framework for single-cell perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- Regulated HSPG Signaling Directs Epicardial Behavior to Support Cardiac Formation.Circulation research · 2026Article
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- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- PHAROS: turning single-cell perturbation models into target-directed drug-combination screens.bioRxiv : the preprint server for biology · 2026Article
- Article
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- Article
- Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.Briefings in bioinformatics · 2026Article
- scBalFlow: a staged flow matching framework for imbalanced single-cell drug perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- DECANT: decoupling mechanism from context in single-cell drug perturbation representation.Bioinformatics (Oxford, England) · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data.BioData mining · 2026Article
- Unify learns cellular evolution with universal multimodal embeddings.Nature communications · 2026Article
- Single-cell foundation modeling with species-native protein tokens links regenerative competence across frog and mouse.bioRxiv : the preprint server for biology · 2026Article
- Pre-Existing Heterogeneity Predicts Rare Proteostasis-Stress Programs Across Diverse Perturbations.Biology · 2026Article
- Article
305 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.