Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data.
Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data.
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黎曼几何和统计模型可纠正批次效应并控制单细胞表面蛋白计数数据中的错误发现。
DOI：
10.1103/physreve.102.012409
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Song,JunS
中科院分区：
文献类型：
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作者：
Zhang,Shuyi;Leistico,JacobR;Cook,Christopher;Liu,Yale;Cho,RaymondJ;Cheng,JeffreyB;Song,JunS
基于下一代测序的单细胞技术的最新进展允许高通量定量检测单个细胞中的细胞表面蛋白沿着转录组，扩展了我们对处于不同疾病状态或在不同实验条件下的不同组织中的细胞群体异质性的理解。通过测序（CITE-seq）技术从转录组和表位的细胞索引中获得的表面蛋白的计数数据提出了新的计算挑战，并且目前缺乏用于分析数据的严格数学工具。这项工作利用黎曼几何的概念和思想来消除样本之间的批次效应，并开发了一个统计框架，用于区分正信号和背景噪声。这些方法的优势在小鼠和人类的两个独立的CITE-seq数据集上得到了证明。
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.