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 diverse samples.
• Analyzing sample heterogeneity and its impact on organism phenotype requires robust bioinformatic methods.
• Current methods often fall short in adequately addressing inter-sample variation for 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 biological samples.
• To facilitate essential bioinformatic tasks such as sample-level visualization and quality control.

Main Methods:

• Proposed a framework to generate a comprehensive single-cell profile for each sample, termed GloScope representation.
• Implemented GloScope on scRNA-Seq datasets with varying numbers of samples (12 to over 300).
• Demonstrated the utility of GloScope for sample-level bioinformatic analyses.

Main Results:

• GloScope provides a unified representation of single-cell data within each sample.
• The framework successfully handles datasets with a large number of samples.
• Enabled effective sample-level visualization and quality control assessment.

Conclusions:

• GloScope offers a powerful new approach for analyzing scRNA-Seq data at the sample level.
• The framework addresses a critical gap in existing bioinformatic tools for population-level studies.
• Facilitates deeper understanding of biological systems by accounting for sample heterogeneity.