High throughput automated analysis of big flow cytometry data

Methods : a Companion to Methods in Enzymology
Albina RahimRyan Brinkman

Abstract

The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC).

Associated Datasets

References

Sep 23, 2018·Electrophoresis·Yanli GongHai Jiang
May 18, 2019·Cytometry. Part a : the Journal of the International Society for Analytical Cytology·Bac NguyenWillem Waegeman
Aug 2, 2019·American Journal of Respiratory Cell and Molecular Biology·Robert M TigheAlexander V Misharin
Nov 7, 2019·Cytometry. Part a : the Journal of the International Society for Analytical Cytology·Disi JiPadhraic Smyth
Mar 12, 2019·Frontiers in Immunology·Anja Ten BrinkeEva Martinez-Caceres
Dec 18, 2019·Nature Immunology·Lucie Abeler-DörnerAdrian C Hayday

Related Concepts

Flow Cytometry
Computer Programs and Programming
Immunophenotyping
Cell Growth
Lymphocyte Subtyping
Analysis
Scientist
Cytometry
Phenotype Determination
Population Group

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