# A spectral dimension reduction technique that improves pattern detection in multivariate spatial data

## TL;DR

This paper introduces a new method for analyzing spatial gene expression data that improves pattern detection by reducing non-spatial noise.

## Contribution

A novel dimension reduction technique that optimizes spatial dependency for multivariate spatial transcriptomics.

## Key findings

- The method outperforms PCA in capturing spatial patterns in gene expression data.
- The projection reduces noise and enhances the detection of spatially variable genes.
- The algorithm requires no parameter tuning and provides a calibrated statistical test.

## Abstract

We introduce a statistical approach for pattern recognition in multivariate spatial transcriptomics data.

Our algorithm constructs a projection of the data onto a low-dimensional feature space which is optimal in maximizing Moran’s I, a measure of spatial dependency. This projection mitigates non-spatial variation and outperforms principal components analysis for pre-processing. Patterns of spatially variable genes are well represented in this feature space, and their projection can be shown to be a denoising operation. Our framework does not require any parameter tuning, and it furthermore gives rise to a calibrated, powerful test of spatial gene expression.

The algorithm is implemented in the open source software R and is available at https://github.com/IMSBCompBio/SpaCo.