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).
Highlights
The current standard approach for analyzing and interpreting flow cytomtery data involves 2D dot plots and Boolean gates in which cell populations are sequentially selected for further analysis based on gates regions drawn manually [2]
(WTSI & King’s College London (KCL) bone marrow dataset) in Figure 13 are expressed as fractions of CD45+ cells and it can be seen that the coefficient of variation (CV) for automated analysis is lower than that of manual gating for all the populations
A manuscript on the comparison between the automated and manual analysis results of the Wellcome Trust Sanger Institute (WTSI) & KCL datasets are in preparation by the KCL team in London, UK, where greater details are provided on how the automated analysis superseded the manual analysis
Summary
The current standard approach for analyzing and interpreting flow cytomtery data involves 2D dot plots and Boolean gates in which cell populations are sequentially selected for further analysis based on gates regions drawn manually [2]. The large number of possible pairs of parameters (e.g., 350 markers or 7:18e+23 potential subsets with 50 markers) can make manual gating both extremely labour intensive and time consuming for high dimensional data [2, 3] This increase in dimensionality has enabled previously unknown cell populations to be identified [4]. The IMPC, a $900 million open-access health research project involving 15 centres across 5 continents is aiming to address this knowledge gap [5] This global infrastructure is creating 20,000 knockout mouse strains, characterizing each strain through a standardized phenotyping protocol, integrating the data to existing mouse and human disease resources, and providing strains and phenotype data for use by the research community. We demonstrate how automated analysis tools can perform quality checking, automated gating, and biomarker identification
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