Scale-Invariant Joint Clustering and Deconvolution of Spatial Transcriptomics

Thomas M. Menino Convention & Exhibition Center 

We propose a statistical framework for simultaneous clustering and deconvolution of spatial transcriptomics (ST) that is reference-free. Our method jointly estimates the reference cell-type-specific gene expression profiles and the cluster assignments and their cell-type composition of ST spots through solving an well-defined optimization problem. We introduce a regularization term that makes the problem scale invariant which separates compositional signatures from library‑size effects and enabling clustering that groups spots by true compositional similarity even when counts differ. Furthermore, we leverage spatial information to encourage neighboring spots to have similar cell-type compositions. We develop a computationally efficient algorithm to solve the optimization problem and establish theoretical properties of our estimator. Through extensive simulations and applications to real ST data, we demonstrate that our method outperforms existing reference-free methods and is able to uncover biologically meaningful clusters and accurately estimate cell-type compositions without relying on external single-cell references.

Clustering

Spatial Transcriptomics

Scale invariant

Post selection inference

Optimization

Reference‑free deconvolution 

Section on Statistics in Genomics and Genetics