Analysis overviews
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High-dimensional cytometry data analysis
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High-dimensional imaging and spatial data analysis
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Working across cytometry and single-cell data
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| An introduction to analysis strategies for high-dimensional cytometry data, including flow, spectral, and mass cytometry (CyTOF). | An introduction to analysis strategies for high-dimensional imaging and spatial data, including Imaging Mass Cytometry (IMC) data. | An introduction to working across high-dimensional cytometry and single-cell datasets. |
Cytometry analysis workflows
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Simple discovery workflow
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Discovery workflow with batch alignment using CytoNorm
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Discovery workflow with batch alignment using reciprocal PCA (rPCA)
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| 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. | A analysis workflow for the alignment and analysis of data from multiple batches, using reciprocal PCA (rPCA) for batch alignment. |
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COMING SOON</span></a> |
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Guidelines for initial data preparation
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Timeseries clustering using TrackSOM
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Automated cell classification
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| Instructions for initial data preparation, including compensation, cleanup gating, and data export from programs such as FlowJo. | Time-series clustering workflow using TrackSOM. | Strategies to facilitate automated cell classification. |
Spatial analysis workflows
Primary spatial analysis workflow
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Image visualisation and TIFF export
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Boundary-based multicut segmentation with Ilastik
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Spatial analysis workflow using Spectre (following segmentation in Ilastik)
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| Here we provide instructions for initial image visualisation and TIFF export using MCD Viewer or HistoCat++. From here, TIFF files can be exported for use in our segmentation approaches. | Some IMC images contain extremely dense collections of cells, where cytoplasmic (and sometimes) nuclear signal from one cell is difficult to distinguish from another. In this protocol we describe boundary-based segmentation using the 'multi-cut' workflow in Ilastik. Additional cell type and region masks can be included, which dramatically enhances analysis. | An analysis workflow in R using Spectre that facilitates simultaneous cellular and spatial analysis, following multicut segementation. |
Alternative segmentation and analysis approaches
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Spatial analysis workflow using Spectre (following segementation in CellProfiler)
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Modified Bodenmiller lab segmentation approach
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IMC data analysis using FlowJo
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| This is the simplest form of cellular segmentation and analysis. The nuclear signal is identified, and the boundary is expanded outwards by a certain number of pixels. This then becomes the boundary of the cell mask. Although a very simplistic approach, only nuclear signal is required. | In this approach, the user trains a classifier to identify and predict 'nuclear', 'cytoplasmic', or 'background' pixels. This can be used to create more comprehensive cell masks than can be achieved using simple pixel expansion methods. This is based on the Bodenmiller lab workflow described here. Additional cell type and region masks can be included, which dramatically enhances analysis. | A workflow for analysing IMC data using FlowJo, after initial conversion of TIFF files and masks into FCS files. |
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Simple nuclear expansion segmentation with CellProfiler
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| An analysis workflow in R using Spectre that facilitates simultaneous cellular and spatial analysis. |
General scripts and tools
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Rapidly generate tSNE or UMAP plots from CSV or FCS files
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Convert CSV to FCS files (and vise versa)
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Rapid generation of graphs and heatmaps for quantitative, differential, and statistical analysis
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| 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. |