flowCut: An R package for automated removal of outlier events and flagging of files based on time versus fluorescence analysis

被引：14

作者：

Meskas, Justin ^{[1
,4
]}

Yokosawa, Daniel ^{[1
]}

Wang, Sherrie ^{[1
,2
]}

Segat, Gabriela C. ^{[1
]}

Brinkman, Ryan Remy ^{[1
,3
]}

机构：

[1] BC Canc, Terry Fox Lab, Vancouver, BC V5Z 1L3, Canada

[2] Cytapex Bioinformat Inc, Vancouver, BC, Canada

[3] Univ British Columbia, Fac Sci, Vancouver, BC, Canada

[4] Univ British Columbia, Dept Med Genet, Vancouver, BC, Canada

来源：

CYTOMETRY PART A | 2023年 / 103卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

anomaly detection; bioinformatics; data acquisition; data cleaning; flow cytometry; outlier detection; quality checking; CYTOMETRY;

D O I：

10.1002/cyto.a.24670

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Technical artifacts such as clogging that occur during the data acquisition process of flow cytometry data can cause spurious events and fluorescence intensity shifting that impact the quality of the data and its analysis results. These events should be identified and potentially removed before being passed to the next stage of analysis. flowCut, an R package, automatically detects anomaly events in flow cytometry experiments and flags files for potential review. Its results are on par with manual analysis and it outperforms existing automated approaches.

引用

页码：71 / 81

页数：11

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