一种用于多参数流式细胞术数据分析的并行流水线
An Algorithmic Pipeline for Analyzing Multi-parametric Flow Cytometry Data
摘要：
流式细胞术（FC）是一种单细胞分析平台，用于测量生物样品中数百万个细胞中单个细胞的表型。FC采用高吞吐量技术并生成高维数据，因此用于分析数据的算法代表了瓶颈。本论文旨在解决现代细胞术在从高维和高内容的生物学数据中挖掘信息时所面临的几个计算挑战。一个集合的组合和统计算法的定位，匹配，原型设计，并从多参数FC数据的细胞群体进行分类。本论文开发的算法流水线flowMatch由五个定义良好的算法模块组成：（1）转换数据以稳定群体内方差，（2）通过鲁棒聚类算法识别细胞群体，（3）跨样本注册细胞群体，（4）用模板封装一类样本，（5）根据样本与模板的相似性对样本进行分类。flowMatch的组件可以独立工作或相互协作，以执行完整的数据分析。flowMatch在Bioconductor中作为开源R包提供。我们已经使用flowMatch对白血病样本进行分类，评估磷酸化对T细胞的影响，对健康免疫谱进行分类，并对HIV患者的疫苗接种状态进行分类。在这些分析中，流水线能够通过自动算法快速有效地得出具有生物学意义的结论。flowMatch中包含的算法也可以应用于流式细胞术之外的问题，例如微阵列数据分析和图像识别。因此，本论文有助于解决计算细胞学及相关领域的基本问题。
英文摘要：
Flow cytometry (FC) is a single-cell profiling platform for measuring the phenotypes of individual cells from millions of cells in biological samples. FC employs high-throughput technologies and generates high-dimensional data, and hence algorithms for analyzing the data represent a bottleneck. This dissertation addresses several computational challenges arising in modern cytometry while mining information from high-dimensional and high-content biological data. A collection of combinatorial and statistical algorithms for locating, matching, prototyping, and classifying cellular populations from multi-parametric FC data is developed. The algorithmic pipeline, flowMatch, developed in this dissertation consists of five well-defined algorithmic modules to (1) transform data to stabilize within-population variance, (2) identify cell populations by robust clustering algorithms, (3) register cell populations across samples, (4) encapsulate a class of samples with templates, and (5) classify samples based on their similarity with the templates. Components of flowMatch can work independently or collaborate with each other to perform the complete data analysis. flowMatch is made available as an open-source R package in Bioconductor. We have employed flowMatch for classifying leukemia samples, evaluating the phosphorylation effects on T cells, classifying healthy immune profiles, and classifying the vaccination status of HIV patients. In these analyses, the pipeline is able to reach biologically meaningful conclusions quickly and efficiently with the automated algorithms. The algorithms included in flowMatch can also be applied to problems outside of flow cytometry such as in microarray data analysis and image recognition. Therefore, this dissertation contributes to the solution of fundamental problems in computational cytometry and related domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
