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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
qpp完成签到,获得积分10
3秒前
00发布了新的文献求助10
4秒前
研友_VZG7GZ应助爱科研采纳,获得10
4秒前
Leo发布了新的文献求助10
7秒前
NexusExplorer应助Darkangel采纳,获得10
8秒前
爆米花应助Yummy采纳,获得10
8秒前
肖大神完成签到,获得积分10
8秒前
10秒前
Singularity应助湖里采纳,获得10
11秒前
CodeCraft应助Sunny采纳,获得10
11秒前
动听的面包完成签到,获得积分10
12秒前
zzz发布了新的文献求助10
12秒前
yc发布了新的文献求助10
13秒前
王沿橙发布了新的文献求助10
13秒前
14秒前
爱科研完成签到,获得积分10
15秒前
木南完成签到,获得积分10
16秒前
16秒前
不安毛豆发布了新的文献求助10
16秒前
欣慰的天荷完成签到 ,获得积分10
16秒前
16秒前
后少年的story完成签到,获得积分10
17秒前
marco完成签到 ,获得积分10
19秒前
睡着的鱼完成签到,获得积分10
19秒前
hcx发布了新的文献求助10
20秒前
卡皮巴拉发布了新的文献求助10
21秒前
131949发布了新的文献求助10
21秒前
湖里完成签到,获得积分10
22秒前
njupt连赛通完成签到,获得积分10
22秒前
YangXJ完成签到,获得积分10
22秒前
22秒前
刘大可发布了新的文献求助10
23秒前
Rousongxiaobei完成签到,获得积分10
24秒前
24秒前
不配.应助聪明的宛菡采纳,获得10
24秒前
26秒前
xiaocoub发布了新的文献求助10
28秒前
yc完成签到,获得积分20
28秒前
28秒前
爆米花应助Likz采纳,获得10
29秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
2024 Medicinal Chemistry Reviews 400
Dictionary of socialism 350
Mixed-anion Compounds 300
Geochemistry, 2nd Edition 地球化学经典教科书第二版 300
Idoxuridine 260
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3195124
求助须知:如何正确求助?哪些是违规求助? 2844019
关于积分的说明 8047807
捐赠科研通 2508458
什么是DOI,文献DOI怎么找? 1340838
科研通“疑难数据库(出版商)”最低求助积分说明 639041
邀请新用户注册赠送积分活动 608006