Analysis of Clinical Flow Cytometric Immunophenotyping Data by Clustering on Statistical Manifolds: Treating Flow Cytometry Data as High-Dimensional Objects
Analysis of Clinical Flow Cytometric Immunophenotyping Data by Clustering on Statistical Manifolds: Treating Flow Cytometry Data as High-Dimensional Objects
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DOI：
10.1002/cyto.b.20435
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发表时间：
2009-01-01
影响因子：
3.4
通讯作者：
Hero, Alfred O.
中科院分区：
文献类型：
--
作者：
Finn, William G.;Carter, Kevin M.;Hero, Alfred O.
背景：临床流式细胞术通常涉及二维直方图的顺序解释，通常从六个或更多的细胞特征中筛选，然后根据这些特征的不同子集对细胞群进行初始选择（门控）。我们通过最近描述的一种称为Fisher信息非参数嵌入（FINE）的方法，利用信息几何原理，研究了将门控n参数临床流式细胞术数据作为嵌入在n维空间中的对象的可行性。方法：在通过迭代门控策略初始选择相关细胞群后，我们将四种颜色（六参数）临床流式细胞术数据集转换为六维概率密度函数，并使用Kullback-Leibler散度（一种相对分布熵的测量方法，被证明是某些类型统计流形中Fisher信息距离的适当近似值）计算这些分布之间的差异。基于Kullback-Leibler散度的邻域地图被投影到二维显示器上进行比较。结果：这些方法在54例患者样本中从生理性b细胞前体（造血）扩增的病例中有效地无监督地聚类急性淋巴细胞白血病病例。结论：将流式细胞术数据集作为嵌入高维空间的对象（而不是连续的二维分析）进行处理，有可能在临床实践中用作决策支持工具，或作为基于存储列表模式数据中包含的高维分布模式的基于上下文的临床流式细胞术数据存档和搜索的手段。需要进一步的研究来进一步测试这种方法在临床实践中的有效性。(C) 2008年临床细胞术学会
Background: Clinical flow cytometry typically involves the sequential interpretation of two-dimensional histograms, usually culled from six or more cellular characteristics, following initial selection (gating) of cell populations based on a different subset of these characteristics. We examined the feasibility of instead treating gated n-parameter clinical flow cytometry data as objects embedded in n-dimensional space using principles of information geometry via a recently described method known as Fisher Information Non-parametric Embedding (FINE).Methods: After initial selection of relevant cell populations through an iterative gating strategy, we converted four color (six-parameter) clinical flow cytometry datasets into six-dimensional probability density functions, and calculated differences among these distributions using the Kullback-Leibler divergence (a measurement of relative distributional entropy shown to be an appropriate approximation of Fisher information distance in certain types of statistical manifolds). Neighborhood maps based on Kullback-Leibler divergences were projected onto two dimensional displays for comparison.Results: These methods resulted in the effective unsupervised clustering of cases of acute lymphoblastic leukemia from cases of expansion of physiologic B-cell precursors (hematogones) within a set of 54 patient samples.Conclusions: The treatment of flow cytometry datasets as objects embedded in high-dimensional space (as opposed to sequential two-dimensional analyses) harbors the potential for use as a decision-support tool in clinical practice or as a means for context-based archiving and searching of clinical flow cytometry data based on high-dimensional distribution patterns contained within stored list mode data. Additional studies will be needed to further test the effectiveness of this approach in clinical practice. (C) 2008 Clinical Cytometry Society