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Identifying Rare Cell Populations in Comparative Flow Cytometry

机译：在比较流式细胞术中识别稀有细胞群

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Multi-channel, high throughput experimental methodologies for flow cytometry are transforming clinical immunology and hematology, and require the development of algorithms to analyze the high-dimensional, large-scale data. We describe the development of two combinatorial algorithms to identify rare cell populations in data from mice with acute promyelocytic leukemia. The flow cytometry data is clustered, and then samples from the leukemic, pre-leukemic, and Wild Type mice are compared to identify clusters belonging to the diseased state. We describe three metrics on the clustered data that help in identifying rare populations. We formulate a generalized edge cover approach in a bipartite graph model to directly compare clusters in two samples to identify clusters belonging to one but not the other sample. For detecting rare populations common to many diseased samples but not to the Wild Type, we describe a clique-based branch and bound algorithm. We provide statistical justification of the significance of the rare populations.
机译：用于流式细胞术的多通道，高通量实验方法是转化临床免疫学和血液学，并要求开发算法来分析高维，大规模数据。我们描述了两种组合算法的发展，以鉴定急性早产细胞白血病的小鼠中的稀有细胞群。将流式细胞术数据进行聚类，然后比较来自白血病，白血病和野生型小鼠的样品，以鉴定属于患病状态的簇。我们在群集数据上描述了三个指标，有助于识别稀有群体。我们在二分钟图模型中制定广义边缘覆盖方法，直接比较两个样本中的簇，以识别属于一个但不是其他样本的群集。为了检测许多患病样本的罕见群体，而不是野生类型，我们描述了一种基于集团的分支和绑定算法。我们提供了稀有人群的重要性的统计理由。

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