A visual analytics approach for models of heterogeneous cell populations

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Cell population models are increasingly used to study cell-to-cell variability, crucial in primary cells, cancer cells, and stem cells.
• Existing tools lack in-depth analysis capabilities for these complex models, hindering the study of heterogeneity sources and biomarker selection.

Purpose of the Study:

• To develop and present a novel visual analytics approach for the in-depth analysis of cell population models.
• To enable the determination of heterogeneity sources (genetic, epigenetic) and the selection of potential biomarkers.

Main Methods:

• The proposed method combines parallel-coordinates plots for visual assessment of high-dimensional dependencies.
• Nonlinear support vector machines are integrated for the quantification of effects within cell populations.
• The approach is designed to study both qualitative and quantitative differences among cells.

Main Results:

• The visual analytics approach effectively addresses the complexity of cell population models.
• The method allows for the identification of factors contributing to cell heterogeneity.
• Case study using the proapoptotic signal transduction pathway demonstrates the utility of the approach.

Conclusions:

• The developed visual analytics method provides essential tools for analyzing cell population models.
• This approach facilitates a deeper understanding of cell variability and aids in biomarker discovery.
• The method is applicable to various cell types, including cancer and stem cells, advancing biological research.