|Data preparation and export|
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|
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.
|Rapidly generate tSNE/UMAP plots from CSV or FCS files||Convert FCS to CSV (and vise versa)||Computational analysis using FlowJo|
|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).|
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|
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.
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
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|