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

TL;DR

This paper introduces a novel kernel-weighted regression framework with sparse low-rank factorization for analyzing spatial transcriptomics data, aiding disease mechanism insights.

Contribution

It develops an automated, interpretable method that models spatial effects and gene selection across cell types and disease states, improving analysis of spatial transcriptomics data.

Findings

Demonstrates robustness through simulation studies.

Uncovers biologically meaningful associations in Alzheimer disease data.

Outperforms existing methods in various scenarios.

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.