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Related Experiment Video

Updated: Oct 23, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

CoCoA-diff: counterfactual inference for single-cell gene expression analysis.

Yongjin P Park^1,2, Manolis Kellis^3,4

¹Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC, Canada. ypp@stat.ubc.ca.

|August 18, 2021

Summary

This summary is machine-generated.

This study introduces CoCoA-diff, a new method to identify disease-causing genes using single-cell RNA sequencing data. It enhances accuracy in pinpointing causal genes, crucial for genomic medicine and understanding complex diseases like Alzheimer's.

Keywords:

Alzheimer’s disease Causal inference Counterfactual inference Single-cell RNA-seq

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Last Updated: Oct 23, 2025

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Area of Science:

Genomics
Computational Biology
Neuroscience

Background:

Identifying causal genes is essential for understanding genetic diseases.
Single-cell RNA sequencing (scRNA-seq) generates complex data with potential confounders.
Prior methods struggle to adjust for confounders without predefined control variables.

Purpose of the Study:

To develop a causal inference framework, CoCoA-diff, for prioritizing disease genes from scRNA-seq data.
To improve statistical power in identifying causal genes by adjusting for confounders.
To apply the method to Alzheimer's disease research using a large brain cell dataset.

Main Methods:

Developed CoCoA-diff, a causal inference framework for scRNA-seq data.
Implemented confounder adjustment without prior knowledge of control variables.
Applied the method to analyze 70,000 brain cells from Alzheimer's disease studies.

Main Results:

CoCoA-diff significantly improved statistical power in simulations and real-world data.
Identified 215 differentially regulated causal genes in various brain cell types.
Discovered cell-type-specific gene expression patterns linked to Alzheimer's disease.

Conclusions:

CoCoA-diff effectively prioritizes disease-associated genes in scRNA-seq data.
Cell type context is critical for understanding the multifaceted mechanisms of complex diseases.
The identified genes and pathways offer new insights into Alzheimer's disease pathogenesis.