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As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wieder et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Chronic obstructive pulmonary disease (COPD) occurs typically in current or former smokers, but only a minority of people with smoking history develops the disease. Besides environmental factors, genetics is an important risk factor for COPD. However, the relationship between genetics, environment and phenotypes is not well understood. Sample sizes for genome-wide expression studies based on lung tissue have been small due to the invasive nature of sample collection. Increasing evidence for the systemic nature of the disease makes blood a good alternative source to study the disease, but there have also been few large-scale blood genomic studies in COPD. Due to the complexity and heterogeneity of COPD, examining groups of interacting genes may have more relevance than identifying individual genes. Therefore, we used Weighted Gene Co-expression Network Analysis to find groups of genes (modules) that are highly connected. However, module definitions may vary between individual data sets. To alleviate this problem, we used a consensus module definition based on two cohorts, COPDGene and ECLIPSE. We studied the relationship between the consensus modules and COPD phenotypes airflow obstruction and emphysema. We also used these consensus module definitions on an independent cohort (TESRA) and performed a meta analysis involving all data sets. We found several modules that are associated with COPD phenotypes, are enriched in functional categories and are overrepresented for cell-type specific genes. Of the 14 consensus modules, three were strongly associated with airflow obstruction (meta p ≤ 0.0002), and two had some association with emphysema (meta p ≤ 0.06); some associations were stronger in the case-control cohorts, and others in the cases-only subcohorts. Gene Ontology terms that were overrepresented included "immune response" and "defense response." The cell types whose type-specific genes were overrepresented in modules (p < 0.05) included natural killer cells, dendritic cells, and neutrophils. Together, this is the largest investigation of gene blood expression in COPD with 469 cases in COPDGene, ECLIPSE and TESRA combined, with 6267 genes common to all data sets. Additional, we have 42 and 83 controls in COPDGene and ECLIPSE, respectively.

Summary: comb-p is a command-line tool and a python library that manipulates BED files of possibly irregularly spaced P-values and (1) calculates auto-correlation, (2) combines adjacent P-values, (3) performs false discovery adjustment, (4) finds regions of enrichment (i.e. series of adjacent low P-values) and (5) assigns significance to those regions. In addition, tools are provided for visualization and assessment. We provide validation and example uses on bisulfite-seq with P-values from Fisher's exact test, tiled methylation probes using a linear model and Dam-ID for chromatin binding using moderated t-statistics. Because the library accepts input in a simple, standardized format and is unaffected by the origin of the P-values, it can be used for a wide variety of applications., Availability: comb-p is maintained under the BSD license. The documentation and implementation are available at https://github.com/brentp/combined-pvalues., Contact: bpederse@gmail.com