ArticleScience (New York, N.Y.)2019
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.
Article in Science (New York, N.Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 201 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
201 citing papers in PubMed.
- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Testing the Transferability of Perturbational Similarity Maps Across Species, Cell Systems, and Assay Modalities.Neuroinformatics · 2026Article
- Quantifying distribution shifts in single-cell data with scXMatch.Bioinformatics (Oxford, England) · 2026Article
- scLDM: a conditional diffusion framework for single-cell perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- Deep learning perturbation models can outperform baselines on calibrated metrics.Nature biotechnology · 2026Article
- Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts.bioRxiv : the preprint server for biology · 2026Article
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Review
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Article
- Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts.Genome medicine · 2026Article
- PertAdapt: unlocking single-cell foundation models for genetic perturbation prediction via condition-sensitive adaptation.Bioinformatics (Oxford, England) · 2026Article
- Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients.Nature communications · 2026Article
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- ScalablebioRxiv : the preprint server for biology · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
141 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
8 authors.
Funding
Abstract
How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between
Indexed as
Identifiers
What OpenQuestion holds
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.