首页> 外文会议>Algorithms in bioinformatics >Identifying Rare Cell Populations in Comparative Flow Cytometry
【24h】

Identifying Rare Cell Populations in Comparative Flow Cytometry

机译:在比较流式细胞仪中鉴定稀有细胞群

获取原文
获取原文并翻译 | 示例

摘要

Multi-channel, high throughput experimental methodologies for flow cytometry are transforming clinical immunology and hematol-ogy, 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,高通量实验方法正在改变临床免疫学和血液学,并需要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,以从急性早幼粒细胞白血病小鼠中鉴定出罕见细胞群。对流式细胞仪数据进行聚类,然后比较来自白血病,白血病前和野生型小鼠的样品,以鉴定属于患病状态的簇。我们在聚类数据上描述了三个有助于识别稀有种群的指标。我们在二部图模型中制定了广义边缘覆盖方法,以直接比较两个样本中的聚类,以识别属于一个样本但不属于另一个样本的聚类。为了检测许多患病样本但不属于野生型的罕见种群,我们描述了一种基于群体的分支定界算法。我们提供了稀有种群重要性的统计依据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号