After successfully setting up and running a FlowSOM analysis, you can perform exploratory or quantitative downstream analysis on your FlowSOM results. As part of the analysis process, you’ll want to assess the quality of the FlowSOM analysis, which can be done by displaying FlowSOM metaclusters on a Dimensionality Reduction (DR) map and with heatmaps that show the marker expression of the FlowSOM metaclusters. This article outlines the content of static output files from FlowSOM, how to interact with the analysis output in the Cytobank platform, ways to assess FlowSOM quality, and how to perform downstream exploratory data analysis with FlowSOM. Click the links below to jump to the relevant section:
- Output files for FlowSOM
- Interacting with FlowSOM analysis output in the Cytobank platform
- How to assess FlowSOM Quality
- Overlay FlowSOM metaclusters on a viSNE map
- Heatmap view of flowSOM metaclusters by clustering channel
- Color FlowSOM metaclusters by clustering channels
- Exploratory Data Analysis with FlowSOM
You will receive an email notification once the analysis is completed. You can find your analysis from the email link, from the setup page for the FlowSOM run within the original experiment (navigate here via the Experiment Summary page or the Advanced Analyses menu), and in the Attachments section of the FlowSOM analysis experiment. From the setup page, you can View created experiment or Download run info and Plots. Click on Download the run info and plots to download FlowSOM_test_results folder.
FlowSOM run info file
Within that folder, there is FlowSOM run info file which specifies the run info that is associated with this particular analysis and settings used for the run as references. This file contains essential attributes of the run including basic metadata such as which user executed the run, what settings were used, and URLs back to the setup page in the Cytobank platform. This helps improve traceability of results packages that get downloaded off of the Cytobank platform onto local file systems.
Within the same top level folder, there is a folder called supporting_files. This folder will generally not help interpret the results of the analysis, but contains important supporting files for other uses. For example, these files are used behind the scenes when writing new FCS file based on FlowSOM clusters, and also can be used as a complete record of settings at the time of analysis.
Within the results folder, you will find a list of PDF and/or PNG files, depending on the output file type you chose during the FlowSOM setup. Please note that if you choose to output PNG files, the resulting file sizes will be much bigger. An example of the results folder contents is shown below.
The legend.pdf file contains the IDs in minimum spanning tree (MST) and self-organizing map (SOM) grid format, for both clusters and metaclusters, and is available in relative size and fixed size. During the analysis setup process, you can choose to toggle the metacluster background on or off.
The channel_colored_MSTs folder contains aggregated_channel_colored_MSTs.pdf, which shows the MST of all samples combined, colored by each channel you selected for the PDF output settings during the FlowSOM setup. Within the same folder, each sample selected for the analysis has its own PDF file showing the MST colored by each channel. The metacluster background is on by default, but during analysis setup, you can choose to toggle the metacluster background on or off.
Metaclustering_comparisons.pdf is the file that shows the metacluster MST comparison between different clustering methods, hierarchical consensus clustering, hierarchical clustering, and k-Means. The method used in the analysis is labeled. It may be useful to inspect the results of the methods not used downstream in the analysis in case a different method produced more favorable results and you want to use that for further optimization.
Star_plots.pdf: This file shows the results in MST and SOM grid formats where each cluster is represented by a star plot. These star plots indicate the mean intensities of all clustering markers for all cells in that cluster. The height of each segment indicates the intensity: if the segment reaches the border of the circle, the cells have high expression for that marker. An example of the star plot is shown below.
The advantage of FlowSOM is that you can re-run the analysis with the same seed and highlight clustering channels vs output coloring channels.
If you get a result with empty clusters, you should try re-running FlowSOM with a lower target number of clusters. If you’re still getting empty clusters, you could be feeding in a homogenous population where more events are grouped into fewer clusters, or you could have a low event-count per file and should re-evaluate number of clusters needed in either case.
Population_pie_charts.pdf: This file compares manual gating with automated clustering. Pie charts indicate the percentage each manually gated population from the originating experiment, for each cluster identified by FlowSOM., The output can be visualized in both the grid and MST formats. An example of the pie charts in MST is shown below.
During analysis setup, you can choose to set the metacluster background on or off.
The mst_definition folder contains information that defines the minimum spanning tree (MST) coordinates and other information relevant to the MST settings.
The rest of the files in the results folder that are not listed above are CSV files that contain CV, median, and abundance stats for both the FlowSOM clusters and metaclusters for each sample.
When a FlowSOM run completes, a new experiment is created that contains the original files with new channels added for the FlowSOM cluster ID and metacluster ID. You can access this experiment from the FlowSOM Analysis complete page for the run by clicking on View created experiment. Alternatively, you can access the FlowSOM results from the Experiment Summary page of the originating experiment by clicking on the FlowSOM run name.
Following completion of the FlowSOM run, the metaclusters identified by FlowSOM are automatically gated as populations from FlowSOM_metacluster1 to FlowSOM_metacluster"n", where "n" is the number of metaclusters you specified during setup. Here Here is an example showing the FlowSOM_metacluster_id and FlowSOM_cluster_id channels in the Gating editor:
You can also gate the clusters as populations or combine clusters/metaclusters and generate a new population. To generate new gates with the clusters/metaclusters, go to the Gating editor and click on the Cluster gates button.
In the popup window, start by choosing either FlowSOM_cluster_id or FlowSOM-metacluster_id channel. Continue giving a gate name prefix. Preferably, use either cluster_ or metacluster_ as a prefix in order to distinguish which clusters those new gates are generated from. Finally, under Define specific gates, include one of the following options to generate new gates:
1) A comma-separated list of cluster ID numbers will result in the creation of gates for each number entered, one gate for each cluster.
2) Use parentheses in the format of (1,2,3) or (6,8,10) to merge clusters. Here the number inside the bracket stands for the cluster or metacluster ID. With this formula, they are combined into a new gate. If you are combining sequential clusters such as 1, 2, and 3, you can also use (1:3) where a colon denotes a consecutive range of clusters. You can combine non-consecutive clusters using a syntax such as (1:3,10:12), which would group together clusters 1 through 3 and 10 through 12 into one gate.
Check here for more details on the Cluster gates tool.
With metaclusters/clusters now as new populations, you can use all the Cytobank platform tools available to build graphical and statistical reports, and to assess FlowSOM quality, detailed below.
Overlaying FlowSOM-identified metaclusters onto a viSNE map or any other DR map can help you assess the quality of the metaclustering and inform on how you may need to iterate various settings including target number of metaclusters and normalization. If you run DR on your dataset prior to running FlowSOM, a pre-built illustration will be automatically generated in the experiment named Metacluster Dot Overlays. This illustration will depict the DR channels as X and Y axis in a dot plot and all metaclusters overlaid or backgated on the DR map. Learn more about how to perform this workflow.
(Example of saved illustration Metacluster Dot Overlays in the created FlowSOM experiment.)
- Heatmap View of FlowSOM Metaclusters by Clustering Channel
Once a FlowSOM run is complete, a heatmap illustration of the FlowSOM metaclusters by clustering channel will be automatically generated. This illustration allows you to quickly identify the phenotype of each metacluster. For more details, please check the how to create and configure a Heatmap article.
(Example of saved illustration Metacluster Heatmaps in the created FlowSOM experiment.)
- Color FlowSOM Metaclusters by Clustering Channel
You can also use Dot Plots Colored by Channel to color each metacluster with the clustering channels. An example of the dot plots colored by channel view of the FlowSOM populations is shown below.
In the created FlowSOM experiment, two illustrations will always be generated automatically, the Metacluster Box Plots and Metacluster Heatmaps (see below). You may adjust the layout accordingly based on the sample annotations. Please see here for more details. Overview of Summary plots generation using the Illustration Editor.
(Example of saved illustration Metacluster Box Plots in the created FlowSOM experiment.)
- Color Heatmaps and Dot Plots by Functional Markers
Metacluster Heatmaps are also saved in the illustration. See more here on How to create and configure a Heatmap.
(Example of saved illustration Metacluster Heatmaps in the created FlowSOM experiment.)
If your panel includes functional markers such as signaling markers, activation markers, or inhibitory receptors, you can use the dot plots colored by clustering channel described above for those functional markers that you may not have included as clustering channels.
- Assess differences between groups or quality control with contour plot
With single-cell data, you can use contour plots to see differences in abundance across samples from different experimental or disease conditions (e.g. unstimulated vs stimulated or responder vs non-responder). You can gate these plots to reveal statistical differences or export statistics for tests of significance in downstream applications. You may select the contour plots from the plot settings to view the FlowSOM populations.
- Assess differences between groups of quality controls by concatenating samples
When analyzing FlowSOM results, you may want to combine different FCS files to view the consensus map for the completed FlowSOM analysis instead of visualizing each file separately. This is often the case if you are trying to compare across groups and have multiple samples per group.
For most of the visualizations described above, it is possible to virtually concatenate the FCS files in the Cytobank platform. Use the virtual concatenation tool available for dot plots colored by Z-axis channel, contour plots, histograms and Summary plots such as heatmaps or box plots. Check this article for more information on how to use the virtual concatenation tool.
For other visualizations you may need to concatenate the FCS files. One example is to visualize the metaclusters backgated on a DR map. After FlowSOM analysis completes, navigate to the FlowSOM experiment. Select the Actions menu, Export, Download files, to download the FCS files and concatenate them. Finally, upload the concatenated files to a new experiment. There are multiple ways to concatenate your files. Please refer to the FCS file concatenation tool for instructions.
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