Motivation

In today's research landscape, single-cell data holds the key to understanding cellular diversity and function. However, comprehensive analysis performed by bioinformaticians is often locked away in opaque R or Python objects—formats that can be challenging for experimental biologists to interpret. CellAnalyst transforms this paradigm by converting complex single-cell datasets into dynamic, interactive visualizations that invite real‐time exploration and discovery.

Available as both server and desktop implementations, CellAnalyst allows researchers to transition effortlessly from static data interpretation to interactive, insight-driven analysis. The platform offers a range of powerful views including embedding projections for visualizing cell clustering, detailed cell quality assessments, and innovative marker gene analyses that incorporate gene expression and functional enrichment. Features like Split View further enhance the analytical experience, enabling users to conduct comparative studies across different metadata fields.

Main Scope

CellAnalyst is a robust software platform designed to simplify and enhance the analysis of single-cell datasets. Go to Demo for quick exploring. Here's a brief overview of its core modules:

Main Embedding View

This module displays the primary embedding of your data, typically using a UMAP plot. It provides an intuitive overview of cell populations and their relationships, enabling rapid identification of clusters and patterns within complex datasets.

Function Visualization View

This module offers a suite of visualization tools for deeper data interrogation, including:

• Overview: A summary display that highlights key metrics and characteristics of the dataset.
• Marker Dots: Dot plots that showcase marker gene expression across different cell clusters.
• Gene Expression: Dual views that overlay gene expression on the main embedding and provide detailed distribution patterns via violin plots.
• Enrichment: Functional analysis to reveal enriched biological processes and pathways, which aids in understanding the underlying functions associated with different cell groups.

Meta Table View

This section provides detailed metadata, including cell type markers, cell annotations, and other relevant information. It supports thorough data validation and in-depth characterization of cellular populations.