CellScout: Visual Analytics for Mining Biomarkers in Cell State Discovery

TL;DR

CellScout is a visual analytics tool combined with a machine learning algorithm that helps biologists discover meaningful biomarkers and cell states more reliably, reducing trial-and-error in biological research.

Contribution

The paper introduces a novel system, CellScout, integrating a Mixture-of-Experts algorithm with visual analytics to improve biomarker discovery in cell state analysis.

Findings

Effective identification of cell-biomarker associations.

Validated system with expert feedback and case study.

Enhanced accuracy in cell state discovery.

Abstract

Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often invalid due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective tools to help uncover the hidden association relationships between different cell populations and their potential biomarkers. To address this problem, we first designed a machine-learning algorithm based on the Mixture-of-Experts (MoE) technique to identify meaningful associations between cell populations and biomarkers. We further developed a visual analytics system, CellScout, in collaboration with biologists, to help them explore and refine these association relationships to advance cell state discovery. We validated our system through expert interviews, from which we further selected a representative case to demonstrate its effectiveness in discovering new cell states.