SpaCoEx: Sparse Gene Selection for Spatially Varying Co-expression in Spatial Transcriptomics
Year: 2026
Type: preprint
Abstract: Spatial transcriptomics enables gene expression to be measured while preserving tissue location, but most existing analyses focus on spatial variation in individual genes or expression-defined domains. Here, we introduce SpaCoEx, a sparse spatial representation framework that integrates gene-expression levels with spatially varying gene-gene co-expression. SpaCoEx first performs structured gene selection then uses the selected genes to construct both expression-level features and local co-expression features. We applied SpaCoEx to two human spatial transcriptomics samples. In the cutaneous squamous cell carcinoma dataset, SpaCoEx selected 29 of 45 keratinocyte-related genes while preserving 96.74% of the spatial co-expression variation. In the breast cancer dataset, SpaCoEx identified spatially varying co-expression between B2M and HLA-C, a biologically meaningful major histocompatibility complex (MHC) class I antigen-presentation gene pair. Their local correlation was significantly higher in cancerassociated regions than in non-cancer regions (mean difference = 0.30, spatially adjusted SE = 0.045, P < 1.0 × 10 −10 ), whereas B2M and HLA-C expression individually did not differ significantly between cancer and non-cancer. In benchmarking against manual tissue annotations, co-expression-only SpaCoEx achieved the strongest spatial coherence (percentage of abnormal spots [PAS] = 0.076), while the joint expression/co-expression representation achieved the highest annotation agreement, with an adjusted Rand Index (ARI) of 0.584 and normalized mutual information (NMI) of 0.663. By integrating marginal gene-expression information with local gene–gene co-expression structure, SpaCoEx provides a sparse, low-dimensional, and interpretable representation of spatial transcriptomics data that captures complementary aspects of tissue organization beyond expression-based variation alone.
Authors: Michael Jian, Shaojun Pei, Gil Alterovitz
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212430622