A unified framework for deconvolution-based clustering

Music City Center 

We propose a novel statistical framework for simultaneously clustering and deconvoluting spatially resolved transcriptomic (SRT) data. Specifically, we propose an estimation criterion that can identify clusters of spatial spots, while also providing estimates of the cell-type compositions for each cluster. Our approach formulates the clustering problem as a well-posed optimization, minimizing the proposed criterion that incorporates spatial structure and cellular heterogeneity. This is solved efficiently using a block coordinate descent algorithm, where each subproblem is convex. To ensure robust and data-driven model selection, we introduce a new strategy for parameter tuning, alongside a novel post-clustering inference framework. This framework addresses challenges like inflated Type I error rates, enabling valid hypothesis testing on the identified regions, providing a statistically rigorous basis for downstream analysis. Extensive simulation studies and real data applications demonstrate that our method significantly outperforms existing competitors, offering a scalable, interpretable, and reliable tool for analyzing complex SRT data.

Clustering

Deconvolution

Spatial Transcriptomics

Post clustering inference

Optimization 

Section on Statistics in Genomics and Genetics