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

Area of Science:

• Computational Biology
• Bioinformatics
• Data Science

Background:

• High-dimensional (HD) cytometry data analysis presents challenges due to increasing size and complexity.
• Current tools lack scalability and integration capabilities for multi-sample, multi-batch, or multi-experiment analyses.
• Streamlined workflows for integrating data across batches or experiments are not widely available.

Purpose of the Study:

• To introduce Spectre, an R package for comprehensive, end-to-end integration and analysis of HD cytometry data.
• To provide a scalable computational approach for handling large and complex cytometry datasets.
• To facilitate reproducible analysis across different batches and experiments.

Main Methods:

• Spectre is an R package offering a modular workflow for data pre-processing, batch alignment, integration, clustering, dimensionality reduction, and visualization.
• It utilizes fundamental data structures and machine learning classifiers for scalable analysis of large HD datasets.
• The package supports various cytometry types (flow, mass, spectral) and can be extended to single-cell RNA sequencing and HD imaging data.

Main Results:

• Spectre enables streamlined, end-to-end analysis of HD cytometry data, including pre-processing, batch alignment, integration, and visualization.
• The package demonstrates scalability for analyzing very large HD datasets through its data structures and machine learning implementation.
• Spectre facilitates quantitative and statistical analysis, population labelling, and supports diverse data types beyond traditional cytometry.

Conclusions:

• Spectre provides a comprehensive, scalable, and user-friendly solution for analyzing complex high-dimensional cytometry data.
• Its modular design and flexible data structures enable integration of data across batches and experiments, enhancing reproducibility.
• The R package and Docker container offer accessible tools for both bioinformaticians and laboratory scientists.