PieParty: visualizing cells from scRNA-seq data as pie charts.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is a powerful tool for biological research.
• Dimensional reduction techniques like UMAP and tSNE are standard for visualizing scRNA-seq data.
• Current visualization methods often use single colors per cell, limiting the representation of multiple gene expressions.

Purpose of the Study:

• To introduce PieParty, a novel visualization tool for scRNA-seq data.
• To enable simultaneous visualization of multiple gene expressions and networks within individual cells.
• To provide a more intuitive and information-dense representation compared to traditional methods.

Main Methods:

• PieParty represents each cell as a pie chart.
• Each slice of the pie chart corresponds to the expression level of a single gene.
• This approach allows for the mapping of multiple gene expressions onto reduced-dimension plots.

Main Results:

• Pie charts provide an information-dense and space-efficient visualization.
• The method allows for intuitive interpretation of gene expression patterns across cell populations.
• Simultaneous visualization of multiple genes and networks is achieved.

Conclusions:

• PieParty offers a novel and effective approach for visualizing complex scRNA-seq data.
• The tool enhances the interpretability of gene expression patterns and biological networks.
• This visualization method improves upon traditional single-color cell representations.