Topological Methods for Visualization and Analysis of High Dimensional Single-Cell RNA Sequencing Data.

Area of Science:

• Computational Biology
• Genomics
• Data Visualization

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for analyzing cellular heterogeneity.
• Existing visualization methods like t-SNE can distort continuous gene expression profiles.
• Effective visualization is key for extracting biological insights from complex scRNA-seq datasets.

Purpose of the Study:

• To introduce and evaluate Topological Data Analysis (TDA) using the Mapper algorithm for scRNA-seq data visualization.
• To demonstrate TDA's ability to preserve both cell clustering and continuous gene expression structures.
• To explore the utility of TDA in identifying differential gene expression patterns within co-expression modules.

Main Methods:

• Application of the Mapper algorithm, a Topological Data Analysis technique, to scRNA-seq datasets.
• Utilizing filter functions within Mapper to explore specific pathways and genes.
• Integration of Mapper visualization with gene co-expression network analysis.

Main Results:

• The Mapper-based method successfully captures the clustering of cells in scRNA-seq data.
• This approach preserves the intrinsic continuous topology of gene expression profiles.
• Combined analysis revealed differential expression patterns of gene co-expression modules along the topological network.

Conclusions:

• TDA-based Mapper visualization provides a powerful alternative for scRNA-seq data analysis.
• This method enhances the understanding of cellular heterogeneity by preserving data continuity.
• The integration with gene co-expression analysis offers new avenues for biological discovery.