Data preparation
| Data preparation and export | |
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| Here we provide instructions for initial data preparation, including compensation, cleanup gating, and data export from programs such as FlowJo. |
Core analysis workflows
Simple discovery workflow | Batch alignment discovery workflow | Spatial |
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A simple workflow (with worked example) using a single R script to run clustering/dimensionality reduction, make plots, and perform some limited quantitative/statistical analysis. No batch alignment steps included. | A comprehensive and adaptable workflow (with worked example) for the integration and analysis of data from multiple batches. | Analysis workflows for high-dimensional imaging mass cytometry (IMC) data, using Spectre and SpectreMAP, an extension of Spectre, to facilitate spatial analysis. |
Other workflows
| Rapidly generate tSNE/UMAP plots from CSV or FCS files | Convert FCS to CSV (and vise versa) | Computational analysis using FlowJo |
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| An R script to automatically generate tSNE/UMAP plots, after clustering/tSNE/UMAP has run in programs such as FlowJo. | An R script to rapidly convert FCS files to CSV files (or vise versa). | Here we provide protocols for performing Spectre's discovery analysis workflows using FlowJo. |
Specialised analysis areas
| Time-series clustering and analysis with ChronoClust | Analysis tools and functions to assist in the analysis of scRNAseq data | Quantitative and statistical analysis from summary data |
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Here we provide a time-series clustering workflow using ChronoClust. | 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. |
| IN DEVELOPMENT |
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. 2020. bioRxiv). If you are interested in using any of these approaches, please get in touch with us.
| Integrating data derived from different experiments or instruments | Automated cell classification | Workflows to manage larger-than-memory datasets |
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A workflow to facilitate the alignment and automated classification of cell types in new cytometry datasets, based on an existing labelled reference dataset. | 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. |
| IN DEVELOPMENT | IN DEVELOPMENT | IN DEVELOPMENT |
