Visualizing scRNA-Seq data at population scale with GloScope.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-Seq) is increasingly used to study cell populations across multiple samples.
• Analyzing sample heterogeneity and its impact on organism phenotype requires robust bioinformatic methods.
• Existing methods often struggle to adequately address inter-sample variation in population-level analyses.

Purpose of the Study:

• To develop a novel bioinformatic framework for representing and analyzing scRNA-Seq data at the sample level.
• To introduce a method that effectively accounts for variation between samples in large-scale scRNA-Seq studies.
• To facilitate essential bioinformatic tasks, including visualization and quality control, for sample-level scRNA-Seq data.

Main Methods:

• A new framework, termed GloScope representation, was developed to capture the complete single-cell profile of a sample.
• GloScope was implemented and tested on scRNA-Seq datasets with varying numbers of samples (12 to over 300).
• The framework was evaluated for its utility in sample-level bioinformatic analyses.

Main Results:

• GloScope provides a comprehensive representation of single-cell data at the sample level.
• The framework successfully handles datasets with a significant number of samples.
• Demonstrated utility of GloScope for sample-level visualization and quality control.

Conclusions:

• GloScope offers a powerful new approach for analyzing scRNA-Seq data across multiple samples.
• The framework addresses a critical gap in current bioinformatic tools for population-level single-cell studies.
• GloScope enhances the ability of researchers to perform sample-level analysis, improving data interpretation and quality assessment.