This paper introduces DIMPLE, a scalable analytical framework and accompanying R package designed to quantify, visualize, and model cell-cell interactions in the tumor microenvironment (TME) using advanced multiplexed imaging technologies. By applying DIMPLE to publicly available MI data, we identify statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates, highlighting the potential of this tool to enhance our understanding of tumor biology.

This paper presents SPARTIN, a Bayesian method for spatially quantifying immune cell infiltration in high-resolution digital pathology images, introducing the Cell Type Interaction Probability (CTIP) to measure tumor-immune interactions. Using SPARTIN to analyze 335 melanoma biopsies, the study reveals significant associations between CTIP scores and immune cell prevalence as well as genomic and clinical outcomes. The findings highlight SPARTIN's potential to enhance understanding of spatial cellular interactions and their implications for treatment and prognosis in skin cutaneous melanoma.

This paper presents SpaceX, a Bayesian methodology designed to identify shared and cluster-specific gene co-expression networks in spatially resolved transcriptomics data. By employing an over-dispersed spatial Poisson model and a high-dimensional factor model, SpaceX enhances the detection of co-expression patterns while accounting for spatial correlations and noise. Analysis of datasets from mouse hypothalamus and human breast cancer reveals multiple hub genes associated with cognitive functions and tumor biology.

This paper introduces GraphR, a Bayesian framework for incorporating sample heterogeneity in probabilistic graphical models to study complex biological networks in multi-omics data. GraphR utilizes a regression-based formulation to capture sample-specific network structures and variational Bayes for computational efficiency, enabling sparse, scalable, and interpretable network estimation. Applied to diverse multi-omics and spatial transcriptomics datasets, GraphR identifies network features associated with intra- and inter-sample variability, uncovering biological insights that are challenging for traditional homogeneous models.