Exploring transcription modalities from bimodal, single-cell RNA sequencing data

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Bimodal single-cell RNA sequencing (scRNA-seq) data is increasingly used for biological pathway analysis.
• Current methods primarily use RNA velocities for phenotypic trajectories, neglecting the shape information in 2D data.

Purpose of the Study:

• To develop a novel method for analyzing the shape information in 2D bimodal scRNA-seq data.
• To identify new gene expression modalities and their biological interpretations.

Main Methods:

• Elliptical parametrization of 2D RNA-seq data.
• Derivation of statistics to reveal distinct gene expression modalities.
• Application to cell cycle and colorectal cancer datasets.

Main Results:

• Identified four distinct gene expression modalities from elliptical parametrization.
• Interpreted modalities as indicators of changes in splicing, transcription, or degradation rates.
• Discovered genes delineating phenotypes that were missed by differential gene expression analysis (DGEA).

Conclusions:

• The new elliptical parametrization method expands the analysis of bimodal scRNA-seq data.
• The identified modalities offer a new approach to discover phenotype-defining genes.
• Incorporating RNA processing insights enhances regulatory and biomarker discovery analyses.