Name: excyt: a graphical user interface for streamlining analysis of high-dimensional cytometry data
Uploaded: 2019-01-16T12:30:00
Description: Watch this Scientific Journal Video about ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data at JoVE.com

The authors have no acknowledgements.

1. Collecting and Preparing Cytometry Data
<ol>
	<li>Place all single stains in a folder by themselves and label by the channel name (by fluorophore, not marker).</li>
</ol>
2. Data Importation &#38; Pre-Processing
<ol>
	<li>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 &#8216;.MAT&#8217; file that can later be loaded via the Load Workspace button. Do not run more ...

<ol>
	<li>Benoist, C., Hacohen, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry,+amped+up.">Flow cytometry, amped up.</a> Science. 332 (6030), 677-678 (2011).</li><li>Ornatsky, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+multiparametric+analysis+by+mass+cytometry.">Highly multiparametric analysis by mass cytometry.</a> Journal of immunological methods. 361 (1), 1-20 (2010).</li><li>Tanner, S. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometer+with+mass+spectrometer+detection+for+massively+multiplexed+single-cell+biomarker+assay.">Flow cytometer with mass spectrometer detection for massively multiplexed single-cell biomarker assay.</a> Pure and Applied Chemistry. 80 (12), 2627-2641 (2008).</li><li>Maecker, H. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardization+of+cytokine+flow+cytometry+assays.">Standardization of cytokine flow cytometry assays.</a> BMC immunology. 6 (1), 13 (2005).</li><li>Brazma, A., Vilo, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+expression+data+analysis.">Gene expression data analysis.</a> FEBS letters. 480 (1), 17-24 (2000).</li><li>Pyne, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+high-dimensional+flow+cytometric+data+analysis.">Automated high-dimensional flow cytometric data analysis.</a> Proceedings of the National Academy of Sciences. 106 (21), 8519-8524 (2009).</li><li>Ge, Y., Sealfon, S. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=flowPeaks:+a+fast+unsupervised+clustering+for+flow+cytometry+data+via+K-means+and+density+peak+finding.">flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.</a> Bioinformatics. 28 (15), 2052-2058 (2012).</li><li>Venkatesh, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determinants+of+perceived+ease+of+use:+Integrating+control,+intrinsic+motivation,+and+emotion+into+the+technology+acceptance+model.">Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model.</a> Information systems research. 11 (4), 342-365 (2000).</li><li>Bagwell, C. B., Adams, E. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+spectral+overlap+compensation+for+any+number+of+flow+cytometry+parameters.">Fluorescence spectral overlap compensation for any number of flow cytometry parameters.</a> Annals of the New York Academy of Sciences. 677 (1), 167-184 (1993).</li><li>Lavin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Innate+immune+landscape+in+early+lung+adenocarcinoma+by+paired+single-cell+analyses.">Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses.</a> Cell. 169 (4), 750-765 (2017).</li><li>Chevrier, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+immune+atlas+of+clear+cell+renal+cell+carcinoma.">An immune atlas of clear cell renal cell carcinoma.</a> Cell. 169 (4), 736-749 (2017).</li><li>Hartigan, J. A., Wong, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Algorithm+AS+136:+A+k-means+clustering+algorithm.">Algorithm AS 136: A k-means clustering algorithm.</a> Journal of the Royal Statistical Society. Series C (Applied Statistics). 28 (1), 100-108 (1979).</li><li>Ester, M., Kriegel, H. P., Sander, J., Xu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Density-based+spatial+clustering+of+applications+with+noise.">Density-based spatial clustering of applications with noise.</a> International Conference Knowledge Discovery and Data Mining. 240, (1996).</li><li>Levine, J. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data-driven+phenotypic+dissection+of+AML+reveals+progenitor-like+cells+that+correlate+with+prognosis.">Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis.</a> Cell. 162 (1), 184-197 (2015).</li><li>Blondel, V. D., Guillaume, J. L., Lambiotte, R., Lefebvre, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+unfolding+of+communities+in+large+networks.">Fast unfolding of communities in large networks.</a> Journal of statistical mechanics: theory and experiment. 2008 (10), P10008 (2008).</li><li>Le Martelot, E., Hankin, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+multi-scale+detection+of+relevant+communities+in+large-scale+networks.">Fast multi-scale detection of relevant communities in large-scale networks.</a> The Computer Journal. 56 (9), 1136-1150 (2013).</li><li>Newman, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+algorithm+for+detecting+community+structure+in+networks.">Fast algorithm for detecting community structure in networks.</a> Physical review E. 69 (6), 066133 (2004).</li><li>Hespanha, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> An efficient matlab algorithm for graph partitioning. , 1-8 (2004).</li><li>Moon, T. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+expectation-maximization+algorithm.">The expectation-maximization algorithm.</a> IEEE Signal processing. 13 (6), 47-60 (1996).</li><li>Bishop, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Pattern recognition and machine learning. , (2006).</li></ol>

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...

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 when analyzing higher-dimensional data sets or those with markers staining on a spectrum. For example, in a multi-institutional study, where intra-cellular staining (ICS) assays were being performed to assess the reproducibility of quantitating antigen-specific T cell responses, despite good inter-laboratory precision, analysis, particularly gating, introduced a significant source of variability4. Furthermore, the process of manually gating population of interests, besides being highly subjective is highly time consuming and labor intensive. However, the problem of analyzing high-dimensional data sets in a robust, efficient, and timely manner is not one new to the research sciences. Gene expression studies often generate extremely high-dimensional data sets (often on the order of hundreds of genes) where manual forms of analysis would be simply infeasible. In order to tackle the analysis of these data sets, there has been much work in developing bioinformatic tools to parse gene expression data5. These algorithmic approaches have just been recently adopted in the analysis of cytometry data as the number of parameters has increased and have proven to be invaluable in the analysis of these high dimensional data sets6,7.
Despite the generation and application of a variety of algorithms and software packages that allow scientists to apply these high-dimensional bioinformatic approaches to their flow cytometry data, these analytical techniques still remain largely unused. While there may be a variety of factors that have limited the widespread adoption of these approaches to cytometry data8, the major hindrance we suspect in use of these approaches by scientists, is a lack of computational knowledge. In fact, many of these software packages (i.e., flowCore, flowMeans, and OpenCyto) are written to be implemented in programming languages such as R that still require substantive programming knowledge. Software packages such as FlowJo have found favor among scientists due to simplicity of use and &#39;plug-n-play&#39; nature, as well as compatibility with the PC operating system. In order to provide the variety of accepted and valuable analytical techniques to the scientist unfamiliar programming, we have developed ExCYT, a graphical-user interface (GUI) that can be easily installed on a PC/Mac that pulls many of the latest techniques including dimensionality reduction for intuitive visualization, a variety of clustering methods cited in the literature, along with novel features to explore the output of these clustering algorithms with heatmaps and novel high-dimensional flow/box plots.
ExCYT is a graphical user interface built in MATLAB and therefore can either be run within MATLAB directly or an installer is provided that can be used to install the software on any PC/Mac. The software is available at https://github.com/sidhomj/ExCYT. We present a detailed protocol for how to import data, pre-process it, conduct t-SNE dimensionality reduction, cluster data, sort &#38; filter clusters based on user preferences, and display information about the clusters of interest via heatmaps and novel high-dimensional flow/box plots (Figure 1). Axes in t-SNE plots are arbitrary and in arbitrary units and as such as not always shown in the figures for simplicity of the user interface. The coloring of data points in the &#34;t-SNE Heatmaps&#34; is from blue to yellow based on the signal of the indicated marker. In clustering solutions, the color of the data point is based arbitrary on cluster number. All parts of the workflow can be carried out in the single panel GUI (Figure 2 &#38; Table 1). Finally, we will demonstrate the use of ExCYT on previously published data exploring the immune landscape of renal cell carcinoma in the literature, also analyzed with similar methods. The sample dataset we used to create the figures in this manuscript along with the protocol below can be found at https://premium.cytobank.org/cytobank/projects/875, upon registering an account.

<table border="0" cellpadding="0" cellspacing="0" width="597">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Desktop</td>
			<td style="width:95px;">SuperMicro</td>
			<td style="width:119px;">Custom Build</td>
			<td style="width:167px;">Computer used to run analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MATLAB</td>
			<td style="width:95px;">Mathworks</td>
			<td style="width:119px;">N/A</td>
			<td>Software used to develop ExCYT</td>
		</tr>
	</tbody>
</table>

excyt: a graphical user interface for streamlining analysis of high-dimensional cytometry data

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.

In order to test the usability of ExCYT, we analyzed a curated data set published by Chevrier et al. titled &#39;An Immune Atlas of Clear Cell Renal Carcinoma&#39; 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...

Watch this Scientific Journal Video about ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data at JoVE.com

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

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 ...

This method can help answer key questions in the biomedical field, such as understanding the phenotype of biologically relevant sub-populations. The main advantage of this technique is that it allows an individual with no programming experience to analyze their cytometry data with the latest in high-dimensional techniques. To begin an analysis pipeline, first select the type of cytometry, and the number of events to sample from the file.Next, click Gate Population, select the cell populations of interest, and enter the percentage of events for downstream analysis. Then select the number of channels to be used for the analysis in the list box. For T-distributed Stochastic Neighbor Embedding, or t-SNE analysis, click t-SNE to begin computing the reduced dimensionality dataset.When the set has been computed, click Save TSNE Image, and in the Marker-Specific t-SNE popup menu, select a specific marker of interest. A figure will appear showing a heat map representation of the t-SNE plot that can be saved for figure generation. To begin clustering analysis, select an option in the Clustering Method list box, and click Cluster.To sort the clusters by a marker of interest, select the appropriate corresponding option from the Sort popup menu, and click Ascending, Descending, to update the list of clusters in the Clusters list box. To set a minimal threshold value for a given cluster across a certain channel, select an option from Threshold popup menu, and set an appropriate threshold. Once threshold has been set, click Add Above Threshold, or Add Below Threshold, to specify the direction of the threshold, and enter a numerical cut-off in the Cluster Frequency Threshold box, in the Cluster Filter panel, to set a minimal threshold for the frequency of a cluster.To select clusters for further analysis individualization, select the clusters of interest, in the Clusters list box. And use the Select button, to move the options to the Cluster Analyze list box. To create heat maps of the clusters, select the clusters of interest in the Cluster Analyze list box, and click the HeatMap of Clusters button.To create a high-dimensional box plot, or a high-dimensional flow plot, select the clusters of interest in the Cluster Analyze list box, and click either High-Dimensional Box Plot, or High-Dimensional Flow Plot, to visually assess the distribution of given channels of various clusters across all dimensions. To show clusters in traditional 2D flow plots, select the appropriate transformation and channel in the Conventional Flow Plot panel, and click Conventional Flow Plot. Here, a representative t-SNE analysis of heat maps for various markers within a myeloid panel analysis pipeline, are shown.Using the fast greedy implementation within ExCYT to cluster the data with 100, 000 of the nearest neighbors, 19 sub-populations of cells were revealed. Comparison of the original heat maps to the clusters created by ExCYT allowed similar clusters of the myeloid cells to be identified between the two data groups. Analysis of the lymphoid panel with a more conventional and faster hierarchical clustering approach, yielded similar marker distributions via t-SNE heat maps.Further, clustering of the data via hierarchical clustering demonstrated similar clusters of lymphoid cells. Notably, a unique regulatory T cell population was also identified via a high-dimensional flow plot. To quickly and quantitatively assess co-associations among markers, first a hard K-means clustering algorithm was used to lay down 5000 clusters on the two dimensional t-SNE data.The median expression of all the markers from all the clusters was then used, to create a heat map from these clusters, allowing co-associations to be easily identified, such as the co-association of Tim-3, PD-1, CD38 and 4-1BB. While attempting this procedure, it's important to remember to explore the different parameters, such as different clustering methods, to fully explore the data you are studying.

Research

JoVE Journal

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