Ploidy inference from single-cell data: application to human and mouse cell atlases

Abstract

Ploidy is relevant to numerous biological phenomena, including development, metabolism, and tissue regeneration. Single-cell RNA-seq and other omics studies are revolutionizing our understanding of biology, yet they have largely overlooked ploidy. This is likely due to the additional assay step required for ploidy measurement. Here, we developed a statistical method to infer ploidy from single-cell ATAC-seq data. When applied to the data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across a range of cell types. This method allows for the integration of ploidy analysis into single-cell studies.Article summaryPloidy plays a crucial role in many biological processes. Though modern studies offer deep insights into biology, they often neglect ploidy because it’s challenging to measure. In this research, we have created a new method to detect ploidy using single-cell data. This technique helps identify polyploid cells across various cell types, bridging a gap in our understanding.

nQuack: An R package for predicting ploidal level from sequence data using site-based heterozygosity

“…Popular methods for k-mer-based ploidal-level prediction are tetmer (Becher et al, 2022) and smudgeplot, which plots minor allele frequency by total coverage to predict copy number variants (Ranallo-Benavidez et al, 2020). These methods have been recently expanded to singlecell ATAC-seq data (Takeuchi and Kato, 2023). However, a limitation of these methods is that at least 15-25x sequence coverage per homolog is required.…”