ArticleGenome biology2021
CoCoA-diff: counterfactual inference for single-cell gene expression analysis.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Canon enables causal inference of downstream genes in single-cell CRISPR studies via instrumental variable analysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders.bioRxiv : the preprint server for biology · 2026Article
- Uncovering causal relationships in single-cell omic studies with causarray.Briefings in bioinformatics · 2026Article
- Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Review
- Securing diagonal integration of multimodal single-cell data against ambiguous mapping.Bioinformatics (Oxford, England) · 2025Article
- An autoimmune transcriptional circuit drives FOXP3Science translational medicine · 2024Article
- Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models.Scientific reports · 2024Article
- Causal identification of single-cell experimental perturbation effects with CINEMA-OT.Nature methods · 2023Article
- Review
- Article
- Article
- Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer's disease.Nature neuroscience · 2023Article
- Validation of transcriptome signature reversion for drug repurposing in oncology.Briefings in bioinformatics · 2023Article
- CoCoA-diff: counterfactual inference for single-cell gene expression analysis.Genome biology · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimer's disease. We identify 215 differentially regulated causal genes in various cell types, including highly relevant genes with a proper cell type context. Genes found in different types enrich distinctive pathways, implicating the importance of cell types in understanding multifaceted disease mechanisms.
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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.