A unified approach to spatial domain detection and cell-type deconvolution in spot-based spatial transcriptomics

TL;DR

This paper introduces DUET, a new method for analyzing spatial transcriptomics data that simultaneously detects tissue domains and estimates cell-type proportions within spots, improving interpretability and biological understanding.

Contribution

The paper presents DUET, a novel convex clustering-based approach that jointly performs spatial domain detection and cell-type deconvolution in spot-based transcriptomics data.

Findings

DUET outperforms existing methods in clustering accuracy.

It effectively estimates cell-type proportions within tissue spots.

The method is versatile across different data distributions.

Abstract

Popular technologies for generating spatially resolved transcriptomic data measure gene expression at the resolution of a "spot", i.e., a small tissue region 55 microns in diameter. Each spot can contain many cells of different types. In typical analyses, researchers are interested in using these data to identify and profile discrete spatial domains in the tissue. In this paper, we propose a new method, DUET, that simultaneously identifies discrete spatial domains and estimates each spot's cell-type proportion. This allows the identified spatial domains to be characterized in terms of the cell type proportions, which affords interpretability and biological insight. DUET utilizes a constrained version of model-based convex clustering, and as such, can accommodate Poisson, negative binomial, normal, and other types of expression data. Through simulation studies and multiple applications, we show that DUET can achieve better clustering and deconvolution performance than existing methods.