Evaluating genetic-ancestry inference from single-cell transcriptomic datasets

Characterizing the ancestry of donors in single-cell transcriptomic studies is crucial to ensure genetic homogeneity, reduce biases in analyses, identify ancestry-specific regulatory mechanisms and their downstream roles in disease, and ensure that existing datasets are representative of human genetic diversity. While these datasets are now widely available, information on the ancestry of donors is often missing, hindering further analysis. Here, we propose a framework to evaluate methods for inferring genetic-ancestry from genetic polymorphisms detected in single-cell sequencing reads. We demonstrate that widely used tools (e.g., ADMIXTURE) provide accurate inference of genetic-ancestry and admixture proportions, despite the limited number of genetic polymorphisms identified and imperfect variant calling from sequencing reads. We infer genetic-ancestry for 401 donors from ten Human Cell Atlas datasets and report a high proportion of donors of European ancestry in this resource. For researchers generating single-cell transcriptomic datasets, we recommend reporting genetic-ancestry inference for all donors and generating datasets that represent diverse ancestries.