Unfold High-Dimensional Clouds for Exhaustive Gating of Flow Cytometry Data.
Unfold High-Dimensional Clouds for Exhaustive Gating of Flow Cytometry Data.
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展开高维云以实现流式细胞术数据的详尽门控。
DOI：
10.1109/tcbb.2014.2321403
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Qiu,Peng
中科院分区：
文献类型：
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作者：
Qiu,Peng
流式细胞术能够在单细胞水平上同时测量多种蛋白质的表达。对一个生物样品的流式细胞术实验提供了对该样品中大量单个细胞上或内部的几种蛋白质标记物的测量。对这些数据的分析通常旨在鉴定具有不同表型的细胞亚群。目前，流式细胞术界最广泛使用的分析方法是对一系列嵌套双轴图进行手动门控，这是高度主观的，劳动密集型的，并且不是详尽的。为了解决这些问题，已经开发了许多方法来通过聚类算法自动进行门控分析。然而，完全消除主观性可能相当具有挑战性。本文介绍了一种替代方法。而不是自动化的分析，我们开发新的可视化，以方便手动门控。所提出的方法将一个生物样本的单细胞数据视为细胞的高维点云，导出云的骨架，并展开骨架以生成2D可视化。我们证明了实用的建议可视化使用真实的数据，并提供定量比较，从主成分分析和多维缩放生成的可视化。
Flow cytometry is able to measure the expressions of multiple proteins simultaneously at the single-cell level. A flow cytometry experiment on one biological sample provides measurements of several protein markers on or inside a large number of individual cells in that sample. Analysis of such data often aims to identify subpopulations of cells with distinct phenotypes. Currently, the most widely used analytical approach in the flow cytometry community is manual gating on a sequence of nested biaxial plots, which is highly subjective, labor intensive, and not exhaustive. To address those issues, a number of methods have been developed to automate the gating analysis by clustering algorithms. However, completely removing the subjectivity can be quite challenging. This paper describes an alternative approach. Instead of automating the analysis, we develop novel visualizations to facilitate manual gating. The proposed method views single-cell data of one biological sample as a high-dimensional point cloud of cells, derives the skeleton of the cloud, and unfolds the skeleton to generate 2D visualizations. We demonstrate the utility of the proposed visualization using real data, and provide quantitative comparison to visualizations generated from principal component analysis and multidimensional scaling.