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高速自动分析流式细胞术数据中的稀有事件。

High-speed automatic characterization of rare events in flow cytometric data.

机构信息

Department of Computer Science, Purdue University, West Lafayette, IN, United States of America.

Department of Statistics, Purdue University, West Lafayette, IN, United States of America.

出版信息

PLoS One. 2020 Feb 11;15(2):e0228651. doi: 10.1371/journal.pone.0228651. eCollection 2020.

DOI:10.1371/journal.pone.0228651
PMID:32045462
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7012421/
Abstract

A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow cytometry. Using a hierarchical Bayesian model and information-sharing via parallel computation, FLARE rapidly explores the high-dimensional marker-space to detect highly rare populations that are consistent across multiple samples. Further it can focus within specified regions of interest in marker-space to detect subpopulations with desired precision.

摘要

一种新的用于稀有事件流式细胞术分析（FLARE）的计算框架已经专门开发出来，用于快速自动识别多参数流式细胞术等平台产生的非常大样本中的稀有细胞群。使用分层贝叶斯模型和通过并行计算进行信息共享，FLARE 可以快速探索高维标记空间，以检测在多个样本中一致的高度稀有群体。此外，它还可以在标记空间的指定感兴趣区域内聚焦，以检测具有所需精度的亚群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/4e37c2e412bf/pone.0228651.g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/4a4d5a6af6bc/pone.0228651.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/efe969adfb9d/pone.0228651.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/4e37c2e412bf/pone.0228651.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/0de98abeddd1/pone.0228651.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/0882d1144f6b/pone.0228651.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/c0402b217b27/pone.0228651.g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b100/7012421/4e37c2e412bf/pone.0228651.g006.jpg

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Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure.运用联合聚类和匹配程序对流式细胞术数据中的样本间变异进行建模。
Cytometry A. 2016 Jan;89(1):30-43. doi: 10.1002/cyto.a.22789. Epub 2015 Oct 22.
3
Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.
多色流式细胞术数据中多维抗原特异性T细胞群体的鉴定与可视化
Cytometry A. 2015 Jul;87(7):675-82. doi: 10.1002/cyto.a.22623. Epub 2015 Apr 23.
4
Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.高维流式细胞术数据队列中细胞群体的联合建模与配准
PLoS One. 2014 Jul 1;9(7):e100334. doi: 10.1371/journal.pone.0100334. eCollection 2014.
5
Setting objective thresholds for rare event detection in flow cytometry.设定流式细胞术稀有事件检测的客观阈值。
J Immunol Methods. 2014 Jul;409:54-61. doi: 10.1016/j.jim.2014.04.002. Epub 2014 Apr 12.
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SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design.用于在大型高维流式细胞术数据集中自动识别稀有细胞群体的SWIFT可扩展聚类，第1部分：算法设计
Cytometry A. 2014 May;85(5):408-21. doi: 10.1002/cyto.a.22446. Epub 2014 Feb 14.
7
Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.用于流式细胞术样本中稀有事件检测和细胞亚群对齐的层次建模。
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Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies.用于组合编码流式细胞术研究中抗原特异性T细胞亚型分型的分层贝叶斯混合建模。
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