This page is being updated, so some sections will be incomplete. Please feel free to use the instructions that are present, and we will update the page shortly. The prior version of this page is provided here: tSNE protocol 2017-04-04.pdf.

Introduction

This protocol describes how to perform Spectre's 'discovery workflow' using FlowJo – including data preparation, clustering with FlowSOM, downsampling, dimensionality reduction with UMAP, creating plots, annotating clusters, and performing quantitative and statistical analysis. For more information on this process, please see the main 'discovery workflow' page.

Citation

If you use Spectre, or Spectre-themed FlowJo workflows in your research, please consider citing Ashhurst TM, Marsh-Wakefield F, Putri GH et al. (2020). bioRxiv. 2020.10.22.349563. To continue providing open-source tools such as Spectre, it helps us if we can demonstrate that our efforts are contributing to analysis efforts in the community. Please also consider citing the authors of the individual packages or tools (e.g. CytoNorm, FlowSOM, tSNE, UMAP, etc) that are critical elements of your analysis work. We have provided some generic text that you can use for your methods section with each protocol and on the 'about' page.

Sample methods blurb

Here is a sample methods blurb for this workflow. You may need to adapt this text to reflect any changes made in your analysis.

Computational analysis of data was performed using the FlowJo using the analysis workflows from the Spectre R package (Ashhurst et al., 2020). The FlowSOM algorithm (Van Gassen et al., 2015) was then run on a merged dataset to cluster the data, where every cell is assigned to a specific cluster and metacluster. Subsequently, the data was downsampled and analysed by the dimensionality reduction algorithm Uniform Manifold Approximation and Projection (UMAP) (McInnes, Healy, Melville, 2018) for cellular visualisation.

Setup FlowJo and plugins

For this workflow, you will need to install FlowJo and have access to a licence or dongle.

You will also need to ensure you have the following plugins installed:

Downsample
FlowSOM
UMAP (or tSNE, FIt-SNE, etc)

To check your packages, go to the 'Applications' folder (on Mac), and find the folder called 'plugins'.

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For each plugin you should see a '.jar' file, and possibly a folder of the same name.

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Within the each folder will be documentation related to that plugin.

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To download new plugins, go to the FlowJo exchange site. Download the following (if you don't already have them), unzip them, and move the .jar files (and optionally the associated folder) into the 'plugins' folder as above.

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To activate them in FlowJo, go to Workspace/Plugins and select 'Add Workspace plugin'. You can select the plugins from your 'plugins' folder.

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Once they have been 'activated' with FlowJo, they should show up in this list.

1. Data preparation and organisation

To start with, we want to pull our FCS files into FlowJo.

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Gate to your population of interest (POI) in the first sample.

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Add the gates to the group, and adjust for all samples, as per a normal analysis in FlowJo.

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Select your POPI and use the 'select equivalent nodes' tool under the 'edit' menu.

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Right click on one of the nodes, and select 'Export / Concatenate Populations'.

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In the new window, select 'Concatenate' at the top. We will be concatenating FCS files, so we can just use 'all uncompensated parameters'. By default, each sample will be separated in a new parameter called 'SampleID', where samples are organised alphabetically (I think it's alphabetical). Alternatively, you can add additional keywords, and add them as additional parameters to the concatenated file.

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Choose an output location – best to create a folder within your existing experiment folder.

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Click 'Concatenate' on the bottom right, and wait for the new FCS file to be created.

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Open a NEW FlowJo workspace.

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Drag the new FCS file into that workspace, and save the workspace in that folder.

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You can check to see that all your samples have been included by opening the file, and plotting some parameter against 'SampleID' (or your custom parameter).

2. Clustering and dimensionality reduction

Select the file, and go to Workspace / Plugins / FlowSOM.

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Choose the parameters you wish to use for FlowSOM clustering. You should also choose a target number of metaclusters. Click 'OK' when you are done.

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You can see that FlowSOM is running by the appearance of this node below the file in FlowJo. You can also see some folders generated within your experiment folder.