流式细胞术数据的统计文件匹配
Statistical File Matching of Flow Cytometry Data
摘要：
流式细胞术是一种快速测量与细胞群中的细胞相关的基于抗原的标志物的技术。虽然流式细胞术数据的分析传统上一次考虑一个或两个标记，但多维分析的兴趣越来越大。然而，流式细胞仪在它们可以共同观察的标记物的数量方面是有限的，这通常是感兴趣的标记物的数量的一部分。出于这个原因，从业者经常基于标记物的不同重叠组合进行多种测定。在本文中，我们解决的挑战，插补的高维联合分布的标记属性值的基础上重叠的边缘观测。我们表明，简单的最近邻基于插补可能会导致虚假的亚群在插补数据，并介绍了一种替代方法的基础上最近邻插补限制到一个细胞的亚群。这就要求我们对缺失数据进行聚类，我们采用混合模型方法和新颖的期望最大化算法来解决这一问题。由于混合模型拟合可能是不适定的，我们还开发了使用领域知识来初始化期望最大化算法的技术。我们证明了我们的方法对真正的流式细胞术数据。
英文摘要：
Flow cytometry is a technology that rapidly measures antigen-based markers associated to cells in a cell population. Although analysis of flow cytometry data has traditionally considered one or two markers at a time, there has been increasing interest in multidimensional analysis. However, flow cytometers are limited in the number of markers they can jointly observe, which is typically a fraction of the number of markers of interest. For this reason, practitioners often perform multiple assays based on different, overlapping combinations of markers. In this paper, we address the challenge of imputing the high dimensional jointly distributed values of marker attributes based on overlapping marginal observations. We show that simple nearest neighbor based imputation can lead to spurious subpopulations in the imputed data, and introduce an alternative approach based on nearest neighbor imputation restricted to a cell's subpopulation. This requires us to perform clustering with missing data, which we address with a mixture model approach and novel EM algorithm. Since mixture model fitting may be ill-posed, we also develop techniques to initialize the EM algorithm using domain knowledge. We demonstrate our approach on real flow cytometry data.
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