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Updated: May 23, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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scAI-SNP: a method for inferring ancestry from single-cell data.

Sung Chul Hong¹, Francesc Muyas², Isidro Cortés-Ciriano²

¹Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215 USA.

|May 22, 2025

Summary

This summary is machine-generated.

We developed scAI-SNP, a tool to infer donor ancestry from single-cell genomics data. This method ensures single-cell atlases represent human genetic diversity for equitable health outcomes.

Keywords:

Ancestry SNP Single-cell transcriptomics The 1000 Genomes Project scAI-SNP

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Area of Science:

Genomics
Population Genetics
Bioinformatics

Background:

Large-scale single-cell data initiatives like the Human Cell Atlas require accurate donor ancestry information.
Self-reported race and ethnicity can be biased and unavailable for existing datasets.

Purpose of the Study:

To introduce scAI-SNP, a novel computational tool for inferring donor ancestry directly from single-cell genomics data.
To address the need for representative single-cell atlases reflecting human genetic diversity.

Main Methods:

Trained scAI-SNP using 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) from the 1000 Genomes Project dataset (3201 individuals, 26 populations).
scAI-SNP computes the contribution of 26 population groups to a donor's ancestry from query single-cell data.

Main Results:

scAI-SNP accurately infers ancestry from sparse single-cell data across diverse tissues and cell types (including cancer).
The tool is robust and applicable to various single-cell profiling modalities like scRNA-seq and scATAC-seq.
Demonstrated consistency in ancestry inference using matched whole-genome sequencing data.

Conclusions:

Ensuring diverse ancestry representation in single-cell atlases is crucial for equitable health outcomes.
scAI-SNP provides a robust method to determine ancestry from single-cell genomics data.
Integrating ancestry information alongside race and ethnicity is vital for understanding and addressing human diversity in health.