Recent advances in next generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. Count data of surface proteins from the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology pose new computational challenges, and there is currently a dearth of rigorous mathematical tools for analyzing the data. This work utilizes concepts and ideas from Riemannian geometry to remove batch effects between samples and develops a statistical framework for distinguishing positive signals from background noise. The strengths of these approaches are demonstrated on two independent CITE-seq data sets in mouse and human.

中文翻译：

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黎曼几何和统计模型可纠正批效应并控制单细胞表面蛋白计数数据中的错误发现。

下一代基于测序的单细胞技术的最新进展允许对细胞表面蛋白以及单个细胞中的转录组进行高通量定量检测，从而扩展了我们对处于不同疾病状态的不同组织中细胞群体异质性的理解或在不同的实验条件下。通过测序（CITE-seq）技术从转录组和表位的细胞索引中计数表面蛋白的数据带来了新的计算挑战，目前缺乏用于分析数据的严格数学工具。这项工作利用了黎曼几何学中的概念和思想来消除样本之间的批处理效应，并建立了一个区分正信号与背景噪声的统计框架。