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

Abstract

As the size and complexity of high-dimensional 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 high-dimensional 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 high-dimensional datasets, generated by flow cytometry, mass cytometry (CyTOF), or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing (scRNAseq) or high-dimensional imaging technologies, such as Imaging Mass Cytometry (IMC). 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).

High-parameter cytometry unmasks microglial cell spatio-temporal response kinetics in severe neuroinflammatory disease

“…The FCS files were compensated and gated in FlowJo prior to exporting median fluorescent intensity (MFI) signals from populations of interest. Heatmaps were applied to MFI's in RStudio (1.2.1335) using the Spectre script [35,38] (package publicly available: https://github. com/ImmuneDynamics/Spectre) or with the PheatMap package [39].…”

A differential regulatory T cell signature distinguishes the immune landscape of COVID-19 hospitalized patients from those hospitalized with other respiratory viral infections

“…To extensively characterize the cellular immunotypes present in the peripheral blood of patients hospitalized with Flu, RSV, or SARS-CoV-2 infection compared to healthy donors, we combined several high-parameter flow cytometry panels to profile myeloid cells, T cells, NK cells, or regulatory T cells (Treg) ( Supplemental Table 1 ). For exploratory analysis of this high-dimensional data set we utilized clustering by Flow-SOM 29, 30 and dimensionality reduction with uniform manifold approximation projection (UMAP) 31 , which revealed strikingly similar distributions of cell populations between healthy donors and all three respiratory infections ( Figure 1B ). A heatmap of markers to identify cell populations distinguished the main clusters as Lin - HLADR + myeloid cells, B cells, T cells, and NK cells ( Figure 1C ).…”

“…Pipelines outlined in the Spectre R package were used to generate UMAP and FlowSOM clusters 29, 30, 76 . In FlowJo from the APC Panel, cells were gated by time (Time, FSC-A), cell size (SSC-A, FSC-A), singlets (FSC-H, FSC-A), and live (SSC-A, Dead).…”

TrackSOM: mapping immune response dynamics through sequential clustering of time- and disease-course single-cell cytometry data

“…ARI implementation was provided by the mclust R package version 5.4.7 (Scrucca et al, 2016). The FiTSNE plots and scatter plots for the WNV CNS data are provided through the Spectre R package version 0.4.0 (Ashhurst et al, 2021).…”