This protocol page contains reproducible workflows for different types of analysis using Spectre. Shortcuts:

• Getting started
• Standard cytometry analysis workflows
• Spatial and single-cell genomics workflows
• Specialised cytometry workflows

For those gettings started with analysis, you can also check out these two commentary papers where we provide considerations for new and experienced users in the design and analysis of high-dimensional experiments: Marsh-Wakefield et al 2021 (ICB), Liechti et al 2021 (Nature Immunology). For more educational content you can check out the tutorials page.

Getting started

Getting started with R, RStudio, and Spectre Here we provide instructions for installing R, RStudio, and Spectre, along with introductory tutorials for getting started. GO TO PAGE

Standard high-dimensional cytometry analysis

Initial data preparation	Simple discovery workflow	Discovery workflow with batch alignment using CytoNorm
Here we provide instructions for initial data preparation, including compensation, cleanup gating, and data export from programs such as FlowJo. For cell isolation and staining protocols for flow, spectral, or mass (CyTOF) cytometry, please see our resources page.	A simple workflow (with worked example) using a single R script (or single FlowJo workspace) to run clustering/dimensionality reduction, make plots, and perform some limited quantitative/statistical analysis. No batch alignment steps included.	A analysis workflow for the alignment and analysis of data from multiple batches, using CytoNorm for batch alignment.
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Quick analysis scripts

Rapidly generate tSNE or UMAP plots from CSV or FCS files	Convert CSV to FCS files (and vise versa)	Rapid generation of graphs and heatmaps for quantitative, differential, and statistical analysis
An R script to automatically generate tSNE or UMAP plots, after tSNE or UMAP has run in programs such as FlowJo.	An R script to rapidly convert FCS files to CSV files (or vise versa).	Here we present a quick workflow script to rapidly generate graphs and heatmaps for quantitative, differential, and statistical analysis. Note, this can be used on summary data generated by any program, including FlowJo.
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Specialised analysis areas

Time-series analysis	scRNAseq analysis	Advanced quantitative and statistical analysis
Here we provide a time-series clustering workflow using TrackSOM.	Here we provide analysis options and tools to support scRNAseq analysis, in conjunction with existing tools such as Seurat and SingleCellExperiment.	A workflow to rapidly generate graphs and heatmaps from summary data to perform quantitative and statistical analysis.
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Advanced applications

These are approaches that are in use within our team, but are still under active development. These are described in our preprint (Ashhurst*, Marsh-Wakefield*, Putri*, et al. 2021. Cytometry A). If you are interested in using any of these approaches, please get in touch with us.

Integrating data derived from different experiments or technologies	Automated cell classification	Workflows to manage larger-than-memory datasets
Strategies to integrate data from separate experiments or technologies.	Strategies to facilitate automated cell classification.	Strategies for the analysis of very large datasets, that are larger than the memory capacity of the computer being used.
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Code for the table structure adapted from https://satijalab.org/seurat