PDF(Pubmed)
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, addressing this gap. When applied to data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across diverse cell types. This method allows for the integration of ploidy analysis into single-cell studies. Additionally, this method can be adapted to detect the proliferating stage in the cell cycle and copy number variations in cancer cells. The software is implemented as the scPloidy package of the R software and is freely available from CRAN.
摘要：
倍变性与许多生物现象有关，包括发展,新陈代谢，和组织再生。单细胞RNA-seq和其他组学研究正在彻底改变我们对生物学的理解，然而他们在很大程度上忽视了倍性。这可能是由于倍性测量所需的额外测定步骤。这里,我们开发了一种统计方法从单细胞ATAC-seq数据推断倍性，解决这个差距。当应用于来自人类和小鼠细胞图谱的数据时，我们的方法能够系统检测不同细胞类型的多倍体.该方法允许将倍性分析整合到单细胞研究中。此外,这种方法可以适用于检测细胞周期的增殖阶段和癌细胞的拷贝数变异。该软件作为R软件的scPloidy包实现，可从CRAN免费获得。
