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

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

Full text not available from this repository.
Item Type: Article
Status: Published
Official URL:
Journal or Publication Title: Cytometry Part A
Date: 2021
Divisions: Science Support
Liver Injury and Cancer
Depositing User: General Admin
Identification Number: 10.1002/cyto.a.24350
ISSN: 1552-4922
Date Deposited: 10 Jun 2021 05:48

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 (

Keywords: FlowSOM; UMAP; clustering; computational analysis; dimensionality reduction; high-dimensional cytometry; mass cytometry; spectral cytometry; t-SNE.

© 2021 International Society for Advancement of Cytometry.

Ashhurst, Thomas Myles
Marsh‐Wakefield, Felix
Putri, Givanna Haryono
Spiteri, Alanna Gabrielle
Shinko, Diana
Read, Mark Norman
Smith, Adrian Lloyd
King, Nicholas Jonathan Cole
Last Modified: 10 Jun 2021 05:48

Actions (login required)

View Item View Item