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Spectre - simple discovery workflow

This protocol was constructed using Spectre version:

## [1] '0.4.1'


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Introduction


Overview

Here we provide a worked example of a ‘simple’ discovery analysis workflow, where the entire process (data prep, clustering, dimensionality reduction, cluster annotation, plotting, summary data, and statistical analysis) is contained within a single script. The analytical workflow is described in our pre-print (Ashhurst TM, Marsh-Wakefield F, Putri GH et al., 2020).

The ‘simple’ workflow is most suitable for fast analysis of small datasets. For larger or more complex datasets, or datasets with multiple batches, we recommend the general discovery workflow, where the data preparation, batch alignment, clustering/dimensionality reduction, and quantitative analysis are separated into separate scripts. The demo dataset used for this worked example are cells extracted from mock- or virally-infected mouse brains, measured by flow cytometry.

Strategy

The ‘simple’ and ‘general’ discovery workflows are designed to facilitate the analysis of large and complex cytometry datasets using the Spectre R package. We’ve tested up to 30 million cells in a single analysis session so far. The workflow is designed to get around the cell number limitations of tSNE/UMAP. The analysis starts with clustering with FlowSOM – which is fast and scales well to large datasets. The clustered data is then downsampled, and dimensionality reduction is performed with tSNE/UMAP. This allows for visualisation of the data, and the clusters present in the dataset. Once the possible cell types in the datasets have been explored, the clusters can be labelled with the appropriate cellular identities.