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

Understanding the spatiotemporal dynamics of disease progression in relation to transcriptomic profiles provides key insights into complex conditions such as Alzheimer's disease.

To enable such investigations, STARmap PLUS technology offers joint profiling of high-resolution spatial transcriptomics and protein detection within the same tissue section.

Detailed visual and clustering-based analyses of STARmap PLUS data by Zeng et al.

(2023) provided important insights into molecular and cellular changes associated with Alzheimer's disease pathology.

Motivated by this work, we develop a kernel-weighted sparse reduced-rank regression framework that estimates associations between plaque size and neighboring cell-level transcriptomic profiles while enabling gene selection and borrowing strength across genes, cell types, and disease stages.

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 simulation scenarios.

Applied to Alzheimer's disease data, the proposed framework uncovers biologically meaningful associations, highlighting its potential for advancing the understanding of disease mechanisms.