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

Abstract

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).

Deep deconvolution of the hematopoietic stem cell regulatory microenvironment reveals a high degree of specialization and conservation between mouse and human

“…paired data, which leverages our ability to dissect and molecularly characterize the intrinsic organization of the bone marrow niche environment. Advances in computational biology have started to develop such tools [62][63][64][65] .…”

Mucosal TLR2-activating protein-based vaccination induces potent pulmonary immunity and protection against SARS-CoV-2 in mice

“…Computational analysis of lung leukocytes was rst performed using the Spectre R package 32 , to perform unbiased clustering of all CD45 + cells. To verify bone marrow replacement, relative expression of TLR2 was examined by FIt-SNE, with expression above background detected only in mice receiving WT BM (Fig.…”

“…Computational analysis of lung leukocytes was performed using the Spectre R package 32 , with instructions and source code provided at https://github.com/ImmuneDynamics/spectre. Compensated sample data were initially prepared in FlowJo (BD) with quality control gating to exclude debris and dead cells, then the CD45 + population was exported as raw value CSV les.…”

How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

“…Also, the Spectre R package, can be used. This package provides endto-end workflow for R, can be adapted to work with spectral flow cytometry data and offers different cluster and dimensionality reduction methods (16).…”

“…Not knowing where to start, new analysis strategies can be intimidating to work with and mistakes are easily made. Furthermore, specific characteristics of spectral flow cytometry, such as removal of auto-fluorescence per cell, higher maximum fluorescence intensities and minimal requirement for spectral compensation, also hinder easy use of already published workflows for data analysis in mass cytometry or conventional flow cytometry (14,15).A few groups published advanced workflows also suited for spectral flow cytometry (16)(17)(18). Commercial flow cytometry software, such as Cytobank, also provide automated pipelines, such as CITRUS (19).…”