Imaging-based spatial transcriptomic (ST) assays now profile targeted gene panels for hundreds of thousands of cells while preserving tissue context, yet most downstream analyses still need a compact, interpretable subset. We introduce CiCLoDS (Clustering in Critical & Low-Dimensional Subspace), an unsupervised framework adapted for spatial transcriptomics to jointly optimize clustering and feature selection under a strict, user-defined gene budget. The method returns an explicit feature subset aligned with the discovered partitions, accepts spatial side information via sine–cosine positional encodings, and converges in minutes on commodity CPUs. We benchmark CiCLoDS on three diverse datasets: Vizgen MERFISH mouse liver (1.27 M cells), 10x Xenium human colon (23 K cells), and Human DLPFC. On hepatocyte zonation, CiCLoDS raises the adjusted Rand index by up to +0.36 over PCA and geneBasis, preserves neighborhood structure with a kNN-overlap AUC of 0.89, and detects vessel-associated voids at 92.8% F1. On Human DLPFC (Visium), CiCLoDS demonstrates robust generalization, achieving a mean Adjusted Rand Index (ARI) of 0.40—surpassing BayesCafe (0.32)—and maintaining high accuracy on heterogeneous samples where baselines fail. Furthermore, we reveal a synergistic utility: using CiCLoDS to initialize BayesSpace raises the mean ARI to 0.50, resolving local minima issues to outperform either method alone. These results show that a lightweight, jointly optimized objective can achieve competitive accuracy, robust generalization, and synergistic utility while producing assay-ready feature panels.
Wang et al. (Mon,) studied this question.