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Immunology and Infection

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published: January 16th, 2019

DOI:

10.3791/57473

1The Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, 2The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 3Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 4Department of Immunology, Johns Hopkins University School of Medicine, 5Department of Pathology, Johns Hopkins University School of Medicine

ExCYT is a MATLAB-based Graphical User Interface (GUI) that allows users to analyze their flow cytometry data via commonly employed analytical techniques for high-dimensional data including dimensionality reduction via t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots.

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically explore characteristics of their cellular samples. However, these technological advancements yield high-dimensional data sets that have become increasingly difficult to analyze objectively within traditional manual-based gating programs. In order to better analyze and present data, scientists partner with bioinformaticians with expertise in analyzing high-dimensional data to parse their flow cytometry data. While these methods have been shown to be highly valuable in studying flow cytometry, they have yet to be incorporated in a straightforward and easy-to-use package for scientists who lack computational or programming expertise. To address this need, we have developed ExCYT, a MATLAB-based Graphical User Interface (GUI) that streamlines the analysis of high-dimensional flow cytometry data by implementing commonly employed analytical techniques for high-dimensional data including dimensionality reduction by t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots. Additionally, ExCYT provides traditional gating options of select populations of interest for further t-SNE and clustering analysis as well as the ability to apply gates directly on t-SNE plots. The software provides the additional advantage of working with either compensated or uncompensated FCS files. In the event that post-acquisition compensation is required, the user can choose to provide the program a directory of single stains and an unstained sample. The program detects positive events in all channels and uses this select data to more objectively calculate the compensation matrix. In summary, ExCYT provides a comprehensive analysis pipeline to take flow cytometry data in the form of FCS files and allow any individual, regardless of computational training, to use the latest algorithmic approaches in understanding their data.

Advances in flow cytometry as well as the advent of mass cytometry has allowed clinicians and scientists to rapidly identify and phenotypically characterize biologically and clinically interesting samples with new levels of resolution, creating large high-dimensional data sets that are information rich1,2,3. While conventional methods for analyzing flow cytometry data such as manual gating have been more straightforward for experiments where there are few markers and those markers have visually discernable populations, this approach can fail to generate reproducible results w....

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1. Collecting and Preparing Cytometry Data

  1. Place all single stains in a folder by themselves and label by the channel name (by fluorophore, not marker).

2. Data Importation & Pre-Processing

  1. To pause or save throughout this analysis pipeline, use the Save Workspace button at the bottom left of the program to save the workspace as a ‘.MAT’ file that can later be loaded via the Load Workspace button. Do not run more .......

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In order to test the usability of ExCYT, we analyzed a curated data set published by Chevrier et al. titled 'An Immune Atlas of Clear Cell Renal Carcinoma' where the group conducted CyTOF analysis with an extensive immune panel on tumor samples taken from 73 patients11. Two separate panels, a myeloid and lymphoid panel, were used to phenotypically characterize the tumor microenvironment. The objective of our study was to recapitulate the results of.......

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Here we present ExCYT, a novel graphical user interface running MATLAB-based algorithms to streamline analysis of high-dimensional cytometry data, allowing individuals with no background in programming to implement the latest in high-dimensional data analysis algorithms. The availability of this software to the broader scientific community will allow scientists to explore their flow cytometry data in an intuitive and straightforward workflow. Through conducting t-SNE dimensionality reduction, applying a clustering method.......

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The authors have no acknowledgements.

....

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NameCompanyCatalog NumberComments
DesktopSuperMicroCustom BuildComputer used to run analysis
MATLABMathworksN/ASoftware used to develop ExCYT

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