Integration, exploration, and analysis of high‐dimensional single‐cell cytometry data using Spectre

• Published in: Cytometry. Part A Vol. 101; no. 3; pp. 237 - 253
• Main Authors: Ashhurst, Thomas Myles, Marsh‐Wakefield, Felix, Putri, Givanna Haryono, Spiteri, Alanna Gabrielle, Shinko, Diana, Read, Mark Norman, Smith, Adrian Lloyd, King, Nicholas Jonathan Cole
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Cluster Analysis; Clustering; computational analysis; Computer applications; Data integration; Data structures; Dimensional analysis; dimensionality reduction; Experiments; Flow cytometry; Flow Cytometry - methods; FlowSOM; Gene sequencing; high‐dimensional cytometry; Integration; Labeling; Learning algorithms; Machine learning; mass cytometry; Modular design; Reduction; Single-Cell Analysis; Software; spectral cytometry; Statistical analysis; Statistical methods; Toolkits; t‐SNE; UMAP; FlowSOM; UMAP; dimensionality reduction; spectral cytometry; high-dimensional cytometry; t-SNE; clustering; computational analysis; mass cytometry
• ISSN: 1552-4922; 1552-4930; 1552-4930
• DOI: 10.1002/cyto.a.24350

As the size and complexity of high‐dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end‐to‐end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre‐processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single‐cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).