Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases

TL;DR

This paper introduces a novel wavelet-based dimensionality reduction method for spatial transcriptomics data that preserves spatial structure and improves gene expression reconstruction.

Contribution

It combines wavelet transformation with matrix factorization and Bayesian regularization to effectively identify spatially-varying genes and visualize spatial patterns.

Findings

Outperforms regular decomposition in gene expression reconstruction

Effectively captures global spatial patterns in gene data

Provides smoother visualization of gene fluctuations

Abstract

Spatially resolved transcriptomics (ST) measures gene expression along with the spatial coordinates of the measurements. The analysis of ST data involves significant computation complexity. In this work, we propose gene expression dimensionality reduction algorithm that retains spatial structure. We combine the wavelet transformation with matrix factorization to select spatially-varying genes. We extract a low-dimensional representation of these genes. We consider Empirical Bayes setting, imposing regularization through the prior distribution of factor genes. Additionally, We provide visualization of extracted representation genes capturing the global spatial pattern. We illustrate the performance of our methods by spatial structure recovery and gene expression reconstruction in simulation. In real data experiments, our method identifies spatial structure of gene factors and outperforms regular decomposition regarding reconstruction error. We found the connection between the fluctuation of gene patterns and wavelet technique, providing smoother visualization. We develop the package and share the workflow generating reproducible quantitative results and gene visualization. The package is available at https://github.com/OliverXUZY/waveST.