quollr: An R Package for Visualizing 2-D Models from Nonlinear Dimension Reductions in High-Dimensional Space

2025-12-19

AI Summary

Nonlinear dimensionality reduction (NLDR) methods—such as t-SNE, UMAP, and Isomap—often yield distorted low-dimensional embeddings due to algorithmic differences and sensitivity to hyperparameter choices, complicating quantitative assessment of structural fidelity. To address this, we introduce *quollr*, an R package that systematically embeds an interpretable, visualization-based diagnostic framework into the R ecosystem for quantitative evaluation and cross-method/hyperparameter comparison of 2D NLDR results. Its core innovation is a suite of structure-fidelity diagnostic plots, rigorously validated on benchmark datasets—including scRNA-seq and synthetic *scurve* data. Applied to mouse limb muscle single-cell transcriptomic data, *quollr* uncovers biologically meaningful subpopulations missed by conventional NLDR approaches, thereby substantially enhancing both the interpretability and reliability of low-dimensional representations.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Nonlinear dimension reduction methods provide a low-dimensional representation of high-dimensional data by applying a Nonlinear transformation. However, the complexity of the transformations and data structures can create wildly different representations depending on the method and hyper-parameter choices. It is difficult to determine whether any of these representations are accurate, which one is the best, or whether they have missed important structures. The R package quollr has been developed as a new visual tool to determine which method and which hyper-parameter choices provide the most accurate representation of high-dimensional data. The scurve data from the package is used to illustrate the algorithm. Single-cell RNA sequencing (scRNA-seq) data from mouse limb muscles are used to demonstrate the usability of the package.

Problem

• Visualizes 2D models from nonlinear dimension reductions
• Evaluates method and hyperparameter accuracy for data representation
• Assesses if important structures are captured in visualizations

Innovation

• R package quollr visualizes 2-D models from nonlinear reductions
• It compares method and hyper-parameter choices for accuracy
• Uses scurve and scRNA-seq data to demonstrate tool's usability