George Wright

Histological images are composed of diverse local structures such as cells, glands, or tissue patches, whose organisation and relative abundance reflect underlying biological processes. While most computational approaches focus on analysing individual features, many clinically relevant signals arise from changes in the composition of these structures rather than isolated measurements. This motivates the need for explicitly modelling how groups of similar instances vary across samples and relate to outcomes. We present DCAFA (Differential Community Abundance and Feature Attribution Analysis), a regression-based framework for analysing hierarchical biomedical data through both compositional and feature-level perspectives. DCAFA groups instances into latent communities representing recurring morphological or phenotypic patterns, and then performs two complementary analyses: (i) community composition analysis, which identifies groups that are enriched or depleted across outcomes, and (ii) feature attribution analysis, which quantifies how instance-level features relate to outcomes directly or within specific communities. Both use generalised linear and mixed-effects models, enabling covariate adjustment and inference through effect sizes, confidence intervals, and false discovery rate control. We demonstrate the utility of DCAFA across multiple biomedical settings, including endometrial histopathology, spatial transcriptomics, multiplex immunofluorescence imaging, and predefined cell-type analyses in colorectal cancer. These examples identify interpretable compositional shifts and context-specific feature associations that are not captured by conventional feature-based approaches. By unifying differential abundance testing and feature attribution within a single statistical framework, DCAFA serves as an openly available toolbox that provides a practical and interpretable means of linking tissue composition with clinical or molecular outcomes in biomedical imaging data. Code available at: https://github.com/wgrgwrght/DCAFA

Motivation: Many biological and clinical processes are dynamic, yet most datasets are cross-sectional, capturing populations at discrete states rather than tracking individuals over time. This makes it difficult to quantify how populations change across developmental, physiological, or disease-associated conditions. Existing trajectory and transport-based methods often rely on fixed feature spaces, assumptions tailored to transcriptomic time-course data, or approximately linear progression, limiting their ability to model heterogeneous and unbalanced transitions across diverse biomedical modalities. Flexible methods are needed that can infer directed population-level change from cross-sectional data while retaining biological interpretability. Results: We present GOTFlow, a framework for learning directed population transitions from cross-sectional biomedical data using graph-constrained optimal transport in a learned latent space. GOTFlow integrates representation learning with unbalanced optimal transport to jointly estimate embeddings and transport couplings between biological states. This enables hypothesis-driven modelling of progression structures while accommodating non-linear geometry, branching relationships, and changes in population mass. From the inferred transport plans, GOTFlow derives interpretable summaries of dynamics, including drift vectors quantifying transitions, and feature-level transported changes that highlight molecular drivers of progression. In synthetic data, GOTFlow recovered known transitions with strong agreement between inferred and ground-truth drifts. Across three biological applications, endometrial remodelling, breast cancer risk progression, and prion disease, GOTFlow identified state-to-state transitions and biologically meaningful feature shifts reflecting impaired decidualisation, increasing cancer risk, and neurodegenerative progression. These results establish GOTFlow as a general and interpretable framework for analysing directed population dynamics from cross-sectional data. Availability: Code available at: https://github.com/wgrgwrght/GOTFlow