TrajLens: Visual Analysis for Constructing Cell Developmental Trajectories in Cross-Sample Exploration

TL;DR

TrajLens is a visual analytics system that combines a GNN-based model and interactive visualization to help biologists construct and refine cross-sample cell developmental trajectories in single-cell RNA sequencing data.

Contribution

We introduce a novel GNN-based model for predicting cross-sample cell trajectories and develop TrajLens, an interactive system for exploring and refining these trajectories visually.

Findings

The GNN model accurately predicts cross-sample cell developmental links.

TrajLens effectively supports biologists in exploring spatial cellular dynamics.

Quantitative evaluations and expert feedback validate the system's usefulness.

Abstract

Constructing cell developmental trajectories is a critical task in single-cell RNA sequencing (scRNA-seq) analysis, enabling the inference of potential cellular progression paths. However, current automated methods are limited to establishing cell developmental trajectories within individual samples, necessitating biologists to manually link cells across samples to construct complete cross-sample evolutionary trajectories that consider cellular spatial dynamics. This process demands substantial human effort due to the complex spatial correspondence between each pair of samples. To address this challenge, we first proposed a GNN-based model to predict cross-sample cell developmental trajectories. We then developed TrajLens, a visual analytics system that supports biologists in exploring and refining the cell developmental trajectories based on predicted links. Specifically, we designed the visualization that integrates features on cell distribution and developmental direction across multiple samples, providing an overview of the spatial evolutionary patterns of cell populations along trajectories. Additionally, we included contour maps superimposed on the original cell distribution data, enabling biologists to explore them intuitively. To demonstrate our system's performance, we conducted quantitative evaluations of our model with two case studies and expert interviews to validate its usefulness and effectiveness.