Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) generates complex datasets.
• Characterizing cell clusters and their gene expression patterns is crucial for biological insight.
• Existing methods may not fully capture spatial gene expression uniformity within clusters.

Purpose of the Study:

• To develop and present Polar Gini Curve, a novel method for characterizing cell clusters in scRNA-seq data.
• To quantify gene distribution uniformity within cell clusters.
• To facilitate the discovery of novel biomarkers and cell subtypes.

Main Methods:

• Polar Gini Curve integrates gene expression and 2D spatial coordinates from scRNA-seq data.
• It quantifies gene distribution uniformity by comparing "foreground" and "background" polar Gini curves.
• The method analyzes patterns of uniformity in clustered cells.

Main Results:

• Genes with dissimilar foreground and background curves exhibit spatially divergent expression patterns.
• Genes with similar curves show uniform expression within the cell cluster.
• The framework successfully identified potential novel cardiac muscle cell subtypes in a neonatal mouse heart dataset.

Conclusions:

• Polar Gini Curve provides a quantitative approach to assess gene expression uniformity in scRNA-seq data.
• The method enhances biomarker discovery across cell clusters.
• It offers a valuable tool for characterizing cell populations and identifying novel subtypes.