ArticleNature methods2023
A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Preclinical Anticipation of On- and Off-Target Resistance Mechanisms to Anti-Cancer Drugs: A Systematic Review.International journal of molecular sciences · 2024Pooled it
- Estimating protein isoform abundances with [Formula: see text].Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Causal effect estimation from trans-regulatory single-cell CRISPR screens.Cell genomics · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Associations of genetic variants with gene expression factors reveal biological pathways underlying complex traits.American journal of human genetics · 2026Article
- Pertpy: an end-to-end framework for perturbation analysis.Nature methods · 2026Article
- High-throughput screening to engineer optimal T cell therapies: current knowledge and future prospects.Frontiers in oncology · 2026Review
- Large-scale causal discovery using interventional data sheds light on gene network structure in k562 cells.Nature communications · 2025Article
- GPerturb: Gaussian process modelling of single-cell perturbation data.Nature communications · 2025Article
- Unlocking Plant Resilience: Metabolomic Insights into Abiotic Stress Tolerance in Crops.Metabolites · 2025Review
- Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025Review
- Decoding heterogeneous single-cell perturbation responses.Nature cell biology · 2025Article
- PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization.Nucleic acids research · 2025Article
- Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA.bioRxiv : the preprint server for biology · 2024Article
- High-throughput screening for optimizing adoptive T cell therapies.Experimental hematology & oncology · 2024Review
- Engineered virus-like particles for transient delivery of prime editor ribonucleoprotein complexes in vivo.Nature biotechnology · 2024Article
- A scalable, high-throughput neural development platform identifies shared impact of ASD genes on cell fate and differentiation.bioRxiv : the preprint server for biology · 2024Article
- Guide assignment in single-cell CRISPR screens using crispat.Bioinformatics (Oxford, England) · 2024Article
- Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection.Genome biology · 2024Article
- CRISPR-Cas9 applications in T cells and adoptive T cell therapies.Cellular & molecular biology letters · 2024Review
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5 authors.
Funding
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8
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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.