Sunday, Aug 2: 2:00 PM - 3:50 PM
6422
Contributed Papers
Thomas M. Menino Convention & Exhibition Center
Room: CC-257A
Main Sponsor
Section on Statistics in Genomics and Genetics
Presentations
Spatial transcriptomics (ST) enables high-resolution mapping of gene expression across tissues, offering spatial insights into cellular organization, tissue development, disease progression, and treatment response. A key objective in ST analysis is the identification of spatially expressed (SE) genes. Most existing approaches, however, analyze genes independently and therefore fail to account for biologically meaningful gene–gene dependencies. We propose SPHERE (Spatial Poisson Hierarchical modEl with pathway-infoRmed gEne networks), a Bayesian spatial Poisson log-normal model that jointly captures spatial dependence across tissue locations and gene level dependence informed by biological pathways. To address the high dimensionality of ST data, we introduce a pathway-informed conditional autoregressive (CAR) prior that incorporates external biological knowledge to model dependencies among genes within pathways. The proposed hierarchical framework enables the simultaneous detection of clusters of SE and non-SE genes while borrowing strength across related genes. By integrating these localized gene dependencies into a hierarchical spatial framework, SPHERE improves both sensitivity and interpretability in detecting SE genes. Simulation studies and applications to real ST datasets demonstrate that SPHERE achieves higher power and accuracy than existing approaches while providing biologically meaningful insights into gene–gene relationships.
Keywords
Spatially expressed
Pathway
Conditional autoregressive
Spatial transcriptomics
Dependence
Detection
Co-expression analysis is key for understanding disease mechanisms and gene regulatory and functional relationships.In spatial transcriptomics, layered sources of variation such as spatial gradients, microenvironmental structure, and technical effects can induce correlations between genes that arise from shared location rather than true biological regulation. To address this, we propose SpaceDecorr, a method that adjusts gene expression for technical artifacts and spatial dependencies by modeling each gene independently using a Negative Binomial Generalized Additive Model (NB-GAM) with spatial splines. Co-expression is then estimated from the Pearson residuals, yielding decorrelated expression values suitable for downstream analysis. This method targets cell-intrinsic coordination, rather than clustering genes by shared spatial patterns, and supports multi-sample analysis through independent per-sample adjustment. Across simulations and real datasets, it consistently reduces false-positive correlations and improves the functional coherence of co-expression modules.
Keywords
Spatial transcriptomics
gene co-expression
spatial autocorrelation
multi-sample analysis
High-throughput spatial transcriptomics (HST) is a powerful experimental technology that allows for profiling gene expression in tissue samples at or near the single-cell level, while retaining the spatial location of each sequencing unit. In the HST data analysis, the identification of the tissue architecture, which reflects biological cell types or states, is routinely implemented using either spatial clustering or label transfer approaches. However, cellular heterogeneity differences and similarities between tissue regions are often ignored, although they can elucidate cellular dynamics in important settings such as the tumor microenvironment. To address these limitations, we developed a statistical framework for the analysis of cellular heterogeneity and community connectivity analysis using HST data, which facilitates our understanding of the heterogeneity of each tissue region and relationships between different tissue regions. We will illustrate this framework through simulation studies and real data applications, including 10X Visium data of melanoma brain metastases and invasive ductal carcinoma.
Keywords
spatial transcriptomics
community connectivity
cellular heterogeneity
network analysis
Spatial transcriptomics has transformed our ability to explore gene expression within its tissue context, enabling us to dissect subtle yet biologically significant variations in situ. While numerous computational methods have been proposed for detecting Spatially Varying Genes (SVGs) expression by modeling each gene separately, much less effort has been devoted to understanding how correlations between genes change across space. Such Spatially Varying Correlations (SVCs) are critical for understanding biological processes such as gene regulatory mechanisms shaped by local tissue environments, yet existing tools remain limited for this task. To address this gap, we present spCorr, a flexible and scalable regression framework for studying SVCs. spCorr provides interpretable, spot-level estimates of gene correlation and detects gene pairs whose correlations vary across locations or between tissue domains. Through extensive simulations and real-data analyses, we show that spCorr achieves high detection power, reliably controls the False Discovery Rate (FDR), and is computationally efficient.
Keywords
spatial transcriptomics
gene correlation
regression
Spatially variable genes (SVGs) reveal the molecular and functional heterogeneity of cells across different spatial regions of a tissue. We found that sample-wide SVGs, identified by previous methods across the whole sample, largely overlap with cell-type marker genes derived from single-cell gene expression, leaving the spatial location information largely underutilized. We developed ctSVG, a computational method specifically tailored for Visium HD spatial transcriptomics at single-cell resolution. ctSVG accurately assigns Visium squares to cells and identifies cell-type-specific SVGs. We show that cell-type-specific SVGs identified by ctSVG include many new genes that do not overlap with sample-wide SVGs or cell-type marker genes, and that these genes reveal important biological functions in real spatial datasets.
Keywords
Spatial transcriptomics
Visium HD
Spatially variable gene (SVG)
In living tissues, mechanisms such as ligand-receptor signaling, immune synapse formation, and coordinated tissue repair depend on physical proximity or localized arrangements of cells in order to function effectively. To gain insight into these spatial cellular phenomena, we utilize a Bayesian spatial deep Gaussian process factor model that allows for the modeling of intracellular as well as intercellular gene association. Differences between cell types are also captured by the structure of the model and can be analyzed using the posterior samples of the MCMC.
The model is made scalable to thousands of cells and genes through the use of the SPDE formulation of the Matérn covariance kernel as well as through random Fourier features.
Sampling is carried out using a partially collapsed Gibbs sampler with conjugate updates for the uncollapsed portion and no U-turn sampling used for the collapsed parameters.
Keywords
SPDE
Spatial Factor Model
Deep Gaussian Process
Cell type differences
High-throughput spatial transcriptomics (HST) captures high-dimensional gene expression profiles in tissue samples while preserving spatial coordinates and has gained broad attention across biomedical research fields. Although many statistical methods exist for HST analysis, experimental design remains under-studied despite the high cost of data generation. To fill this gap, we propose spaDesign2, a simulation-based framework for the power analysis in multi-sample HST. To generate biologically realistic synthetic data, spaDesign2 learns generative models from pilot data, using Beta–Binomial regression for domain composition, a Fisher–Gaussian kernel mixture model for domain morphology, and Gaussian-processes for gene expression patterns, accounting for between-sample variability. It then estimates power across varying candidate sample sizes via simulation and identifies the minimum sample size required to achieve a target power for detecting changes in domain composition, geometry, and gene expression. Across multiple HST datasets, spaDesign2 yields robust sample size recommendations across experimental settings and platforms.
Keywords
Spatial transcriptomics
Multi-sample study
Experimental design
Power analysis
Spatial statistics