scBubbletree

scBubbletree provides a scalable computational method for quantitative visualization of single-cell RNA sequencing (scRNA-seq) data that identifies clusters of cells with similar transcriptomes and represents them as bubbles at dendrogram tips.

Key Features:

• Bubble tree visualization: Represents cell clusters as bubbles at the tips of dendrograms, corresponding to quantitative summaries of cluster properties and relationships.
• Overplotting resolution: Addresses limitations of standard visualization approaches by reducing overplotting in large scRNA-seq datasets.
• Biological property preservation: Preserves global and local biological patterns relative to the original high-dimensional space.
• Quantitative information integration: Stacks bubble trees with cluster-associated information for quantitative assessment of scRNA-seq data.
• Scalability: Handles large scRNA-seq datasets including those with over 1.2 million cells.

Scientific Applications:

• Cell relationship analysis: Enables analysis of cell relationships in scRNA-seq experiments through quantitative visual summaries.
• Biological interpretation: Facilitates biological interpretation of scRNA-seq data by making cluster properties and relationships visually accessible.
• Large dataset visualization: Supports visualization and analysis of large-scale scRNA-seq studies with millions of cells.

Methodology:

scBubbletree identifies clusters of cells with similar transcriptomes and visualizes such clusters as bubbles at the tips of dendrograms, stacking bubble trees with cluster-associated information.