Sparse Reduced-rank Regression Methods for Spatially Misaligned Data with Application to Spatial Transcriptomics

2026-04-26

AI Summary

This study addresses the modeling challenges posed by spatial misalignment between transcriptomic and protein data in spatial omics by proposing a novel framework that integrates kernel-weighted regression with sparse low-rank decomposition. The approach models amyloid plaque size in Alzheimer’s disease as a collective effect of neighboring cellular transcriptomes, leveraging information sharing across genes, cell types, and time points to enable efficient gene selection. It further supports fully automated, data-driven hyperparameter tuning. Evaluated on both simulated and real datasets, the method demonstrates robust performance and significantly outperforms existing approaches, successfully identifying biologically relevant pathways associated with plaque formation and offering new insights into disease mechanisms.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Kernel Methods
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Understanding the spatiotemporal dynamics of disease progression in relation to transcriptomic profiles provides key insights into complex conditions such as Alzheimer disease. To enable such investigations, STARmap PLUS technology offers joint profiling of high-resolution spatial transcriptomics and protein detection within the same tissue section. Motivated by data from Zeng et al. (2023), we develop a novel kernel-weighted regression framework that models plaque size as a collective effect of the spatial transcriptomics of neighboring cells, automatically integrating across cell types and tissue samples from different disease states. To further strengthen interpretability and efficiency, we incorporate a sparse low-rank factorization that enables gene selection while borrowing strength across genes, cell types, and time points. The proposed approach is implemented in a fully automated manner with data-driven specification of key model components. Through simulation studies, we demonstrate the robustness of the proposed method and its superiority across a range of specification scenarios. Applied to Alzheimer disease data, the proposed framework uncovers biologically meaningful associations, highlighting its potential for advancing the understanding of disease mechanisms.

Problem

• spatially misaligned data
• spatial transcriptomics
• Alzheimer disease
• plaque size
• high-dimensional regression

Innovation

• sparse reduced-rank regression
• spatially misaligned data
• spatial transcriptomics
• kernel-weighted regression
• gene selection