MOTIVATION: Disruption in normal gene expression can contribute to the development of diseases and chronic conditions. However, identifying disease-specific gene signatures can be challenging due to the presence of multiple co-occurring conditions and limited sample sizes. Unsupervised representation learning methods, such as matrix decomposition and deep learning, simplify high-dimensional data into understandable patterns, but often do not provide clear biological explanations. Incorporating prior biological knowledge directly can enhance understanding and address small sample sizes. Nevertheless, current models do not jointly consider prior knowledge of molecular interactions and sample labels. RESULTS: We present GRACKLE, a novel nonnegative matrix factorization approach that applies Graph Regularization Across Contextual KnowLedgE. GRACKLE integrates sample similarity and gene similarity matrices based on sample metadata and molecular relationships, respectively. Simulation studies show GRACKLE outperformed other NMF algorithms, especially with increased background noise. GRACKLE effectively stratified breast tumor samples and identified condition-enriched subgroups in individuals with Down syndrome. The model's latent representations aligned with known biological patterns, such as autoimmune conditions and sleep apnea in Down syndrome. GRACKLE's flexibility allows application to various data modalities, offering a robust solution for identifying context-specific molecular mechanisms in biomedical research. AVAILABILITY AND IMPLEMENTATION: GRACKLE is available at: https://github.com/lagillenwater/GRACKLE.

BACKGROUND: In this paper, we are interested in interactions between a high-dimensional -omics dataset and clinical covariates. The goal is to evaluate the relationship between a phenotype of interest and a high-dimensional omics pathway, where the effect of the omics data depends on subjects' clinical covariates (age, sex, smoking status, etc.). For instance, metabolic pathways can vary greatly between sexes which may also change the relationship between certain metabolic pathways and a clinical phenotype of interest. We propose partitioning the clinical covariate space and performing a kernel association test within those partitions. To illustrate this idea, we focus on hierarchical partitions of the clinical covariate space and kernel tests on metabolic pathways. RESULTS: We see that our proposed method outperforms competing methods in most simulation scenarios. It can identify different relationships among clinical groups with higher power in most scenarios while maintaining a proper Type I error rate. The simulation studies also show a robustness to the grouping structure within the clinical space. We also apply the method to the COPDGene study and find several clinically meaningful interactions between metabolic pathways, the clinical space, and lung function. CONCLUSION: TreeKernel provides a simple and interpretable process for testing for relationships between high-dimensional omics data and clinical outcomes in the presence of interactions within clinical cohorts. The method is broadly applicable to many studies.

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.