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

Summary

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.

Abstract

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.

Introduction

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 rich^1,2,3. 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 sets^6,7.

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 'plug-n-play' 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 & 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 "t-SNE Heatmaps" 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 & 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.

Protocol

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 than one instance of the program at a time. Therefore, when loading a new workspace, make sure to check there is no other instance of ExCYT running.
2. To begin analysis pipeline, first select type of cytometry (Flow Cytometry or Mass Cytometry – CYTOF), under the File Selection Parameters select number of events to sample from the file (for this example use 2,000). Once data has been successfully imported, a dialogue box will pop up informing the user that the data has been successfully imported.
3. Press the Auto-Compensation button to conduct an optional auto-compensation step, as done by Bagwell & Adams⁹. Select the directory containing single stains. Select the unstained sample within the user interface dialogue.
   1. Place a forward/side-scatter gate on any of the samples in this directory that will be used to select events to calculate the compensation matrix. It is recommended to use the unstained sample for this purpose. At this point, an algorithm has been implemented to set consistent thresholds at the 99^th percentile of the unstained sample to define positive events in each of the single stains to calculate the compensation matrix. When this is finished, a dialogue box will inform the user that the compensation has been performed.
4. Next, press Gate Population and select the populations of cells of interest, as is the convention in flow cytometry analyses. When population of cells is selected, enter number of percentage of events downstream analysis (in this 10,000 events).
5. Next, select the number channels to be used for analysis in the listbox in the far right of the Pre-Processing box (use the specific channels shown in the example).

3. t-SNE Analysis

1. Press the t-SNE button to have the program begin start to compute the reduced dimensionality data set for visualization in the window below the t-SNE button. To save image of t-SNE, press Save TSNE Image. On a machine with 8 CPU @ 3.4 GHz each and 8 GM RAM this step should take about 2 minutes for 10,000 events, 10 minutes for 50,000 events, and 20 minutes for 100,000 events.
2. To create a ‘t-SNE heatmap’, as seen in several CYTOF publications^10,11, select an option from the Marker-Specific t-SNE pop-up menu (use the specific markers CD64 or CD3 as shown in the example). A figure will pop up showing a heatmap representation of the t-SNE plot that can be saved for figure generation.
3. Select areas of interest in the t-SNE plots by the user for further downstream analyses using the Gate t-SNE button.

4. Cluster Analysis

1. To begin clustering analysis, select an option in Clustering Method listbox (in this example us DBSCAN with a distance factor of 5 in dialogue box to the right of the listbox). Press the Cluster button.
2. Use one of the following options for automated clustering algorithms found in the ‘Automated Clustering Parameters’ panel:
   1. Hard KMEANS (on t-SNE): Apply k-means clustering to the reduced 2-dimensional t-SNE data and requires the number of clusters to be provided to the algorithm¹².
   2. Hard KMEANS (on HD Data): Apply k-means clustering to the original high-dimensional data that was given to the t-SNE algorithm. Once again, the number of clusters needs to be provided to the algorithm.
   3. DBSCAN: Apply the clustering method of clustering, called Density-Based Spatial Clustering of Applications with Noise¹³ that clusters the reduced 2-dimensional t-SNE data and requires a non-dimensional distance factor that determines the general size of the clusters. This type of clustering algorithm is well suited to cluster the t-SNE reduction as it is able to cluster non-spheroidal cluster that are often present in the reduced t-SNE representation. Additionally, due to the fact that it operates on the 2-dimensional data, it is one of the faster clustering algorithms.
   4. Hierarchical Clustering: Apply the conventional hierarchical clustering method to the high-dimensional data where the entire Euclidean distance matrix is calculated between all events before providing the algorithm a distance factor that sets the size of the cluster.
   5. Network Graph-Based: Apply a clustering method that has been most recently introduced into analyzing flow cytometry data when there are rare subpopulations that the user wants to detect^11,14. This method relies on first creating a graph that determines the connections between all events in the data. This step consists of providing an initial parameter to create the graph, which is the number of k-nearest neighbors. This parameter generally governs the size of the clusters. At this point, another dialogue box pops up asking the user to employ one of 5 clustering algorithms that is applied to the graph. These include 3 options to maximize the modularity of the graph, the Danon Method, and a spectral clustering algorithm^{14,15,16,17,18}. If one wants a generally faster clustering solution, we recommend Spectral Clustering or the Fast Greedy Modularity Maximization. While the Modularity Maximization methods along with the Danon method determine the optimal number of clusters, Spectral Clustering requires the number of clusters to be given to the program.
   6. Self-Organized Map: Employ an artificial neural network to cluster the high-dimensional data.
   7. GMM – Expectation Maximization: Create a Gaussian Mixture Model using Expectation Maximization (EM) technique to cluster the high-dimensional data.¹⁹ This type of clustering method also requires the user to input the number of clusters.
   8. Variational Bayesian Inference for GMM: Create a Gaussian Mixture Model but unlike EM, it can automatically determine the number of the mixture components k.²⁰ While the program does require a number of clusters to be given (larger than the expected number of clusters), the algorithm will determine the optimal number on its own.
3. To study a particular area of the t-SNE plot, press the Select Cluster Manually button to draw a set of user-defined clusters. Of note, clusters cannot share members (i.e., each event can only belong to 1 cluster).

5. Cluster Filtration

1. Set(s) of clusters identified either manually or via one of the automatic methods described above can be filter via as follows.
   1. To sort clusters (in the Cluster Filter panel) by any of the markers measured in the experiment, select an option from the Sort pop-up menu. To set whether the order is ascending or descending, press the Ascending/Descending button to the right of the Sort pop-up menu. This will update the list of Clusters in the ‘Clusters (Filtration)’ listbox and re-order them in descending order of median cluster expression of that marker. The percentage denoted in the ‘Clusters (Filtration)’ listbox denotes the percent of the population that this cluster represents.
   2. To set a minimum threshold value for a given cluster across a certain channel, select an option from the Threshold pop-up menu (in this example us the marker CD65 and set a threshold at 0.75). Either type a value in the numerical box below the graph or use the slide-bar to set a threshold. Once threshold is set, press Add Above Threshold or Add Below Threshold to specify the direction of threshold. Once this threshold has been set, it will be listed in the Thresholds box next to the ‘Cluster Filter’ panel where the marker, the threshold value, and the direction will be listed so the user is aware of which thresholds are currently being applied. Finally, the t-SNE plot will update by blurring out clusters that do not meet the requirements of the filtration and the ‘Clusters (Filtration)’ listbox will update to show clusters that meet the filtration requirements.
   3. To set a minimum threshold for frequency of a cluster, enter a numerical cut-off in the Cluster Frequency Threshold (%) box in the Cluster Filter panel (in this example use 1%).

6. Cluster Analysis & Visualization

1. To select clusters for further analysis and visualization, select clusters In Clusters (Filtration) listbox and press the Select à button to move them to the Cluster Analyze listbox.
2. To create heatmaps of clusters, select the clusters of interest in the Cluster Analyze listbox and press the HeatMap of Clusters button. When this button is pressed, a figure will pop up containing a heat map along with dendrograms on the cluster and parameter axes. The dendrogram on the vertical axis will group clusters by those that are closely related while the dendrogram on the horizontal axis will group markers that are co-associated. To save heatmap, press File | Export Setup | Export.
3. To create a ‘High Dimensional Box Plot’ or ‘High Dimensional Flow Plot,’ select the clusters of interest in the Cluster Analyze listbox and press either the High Dimensional Box Plot button or the High Dimensional Flow Plot button. These plots can be used to visually assess the distribution of given channels of various clusters across all dimensions.
4. To show clusters in traditional 2D flow plots, select the transformation (linear, log10, arcsinh) and channel in the Conventional Flow Plot panel and press Conventional Flow Plot.

Results

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...

Discussion

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...

Materials

Name	Company	Catalog Number	Comments
Desktop	SuperMicro	Custom Build	Computer used to run analysis
MATLAB	Mathworks	N/A	Software used to develop ExCYT