SingletSeeker: an unsupervised clustering approach for automated singlet discrimination in cytometry

聚类分析 计算机科学 特征(语言学) 灵敏度(控制系统) 模式识别(心理学) 数据挖掘 过程(计算) 集合(抽象数据类型) 细胞仪 特征选择 人工智能 数据集 层次聚类 算法 化学 细胞 哲学 语言学 生物化学 电子工程 工程类 程序设计语言 操作系统
作者
Mark Colasurdo,Laura Ferrer‐Font,Aaron Middlebrook,A Konecny,Martin Prlic,Josef Špidlen
出处
期刊:Cytometry Part B-clinical Cytometry [Wiley]
标识
DOI:10.1002/cyto.b.22216
摘要

Abstract Flow cytometry is a high‐throughput, high‐dimensional technique that generates large sets of single‐cell data. Prior to analyzing this data, it is common to exclude any events that contain two or more cells, multiplets, to ensure downstream analysis and quantification is of single‐cell events, singlets, only. The process of singlet discrimination is critical yet fundamentally subjective and time‐consuming; it is performed manually by the user, where the proper exclusion of multiplets depends on the user's expertise and often varies from experiment to experiment. To address this problem, we have developed an algorithm to automatically discriminate singlets from other unwanted events such as multiplets and debris. Using parameters derived from imaging, the algorithm first identifies high‐density clusters of events using a density‐based clustering algorithm, and then classifies the clusters based on their properties. Multiplets are discarded in the first step, while singlets are distinguished from debris in the second step. The algorithm can use different strategies on imaging feature selection‐based user's preferences and imaging features available. In addition, the relative importance of singlets precision vs. sensitivity can be further tweaked via a density coefficient adjustment. Twenty‐two datasets from various sites and of various cell types acquired on the BD FACSDiscover™ S8 Cell Sorter with CellView™ Image Technology were used to develop and validate the algorithm across multiple imaging feature sets. A consistent singlets precision >97% with a solid >88% sensitivity has been demonstrated with a LightLoss feature set and the default density coefficient. This work yields a high‐precision, high‐sensitivity algorithm capable of objective and automated singlet discrimination across multiple cell types using various imaging‐derived parameters. A free FlowJo™ Software plugin implementation is available for simple and reproducible singlet discrimination for use at the beginning of any user's workflow.
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