ArticleNature methods2024
scPerturb: harmonized single-cell perturbation data.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.
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Who cites it
106 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Enhancing RNA Capture Efficiency in Spatial Transcriptomics: A Review of Innovative Technologies and Strategies.International journal of molecular sciences · 2025Pooled it
- scLDM: a conditional diffusion framework for single-cell perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- Quantifying distribution shifts in single-cell data with scXMatch.Bioinformatics (Oxford, England) · 2026Article
- Deep learning perturbation models can outperform baselines on calibrated metrics.Nature biotechnology · 2026Article
- DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A meta-interaction basis for cell-cell communication in tissues.bioRxiv : the preprint server for biology · 2026Article
- Review
- 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
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- Article
- Data-centric feedback loops for next-generation immunotherapy development.Nature biomedical engineering · 2026Review
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- The diverse functions of viral microproteins.bioRxiv : the preprint server for biology · 2026Article
- Pre-Existing Heterogeneity Predicts Rare Proteostasis-Stress Programs Across Diverse Perturbations.Biology · 2026Article
- Integrating single-cell and bulk transcriptomic perturbation resources reveals complementary therapeutic spaces for drug repurposing.bioRxiv : the preprint server for biology · 2026Article
- Article
- Single-cell analysis of microglia and monocyte dynamics uncovers distinct TNF-driven neuroimmune signatures in humans after intracerebral hemorrhage.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Article
46 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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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.