المؤلفون: Wieder, Cecilia, Cooke, Juliette, Frainay, Clement, Poupin, Nathalie, Bowler, Russell, Jourdan, Fabien, Kechris, Katerina J, Lai, Rachel Pj, Ebbels, Timothy

المصدر: PLoS Comput Biol ; ISSN:1553-7358 ; Volume:20 ; Issue:3

الوصف: 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.

العلاقة: https://doi.org/10.1371/journal.pcbi.1011814Test; https://pubmed.ncbi.nlm.nih.gov/38527092Test; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10994553Test/

الإتاحة: https://doi.org/10.1371/journal.pcbi.1011814Test
https://pubmed.ncbi.nlm.nih.gov/38527092Test
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10994553Test/

المؤلفون: Liu, Weixuan, Pratte, Katherine A, Castaldi, Peter J, Hersh, Craig, Bowler, Russell P, Banaei-Kashani, Farnoush, Kechris, Katerina J

المصدر: bioRxiv

الوصف: Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

العلاقة: https://doi.org/10.1101/2024.01.22.576667Test; https://pubmed.ncbi.nlm.nih.gov/38328226Test; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10849540Test/

الإتاحة: https://doi.org/10.1101/2024.01.22.576667Test
https://pubmed.ncbi.nlm.nih.gov/38328226Test
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10849540Test/

المؤلفون: Wieder, Cecilia, Cooke, Juliette, Frainay, Clement, Poupin, Nathalie, Bowler, Russell, Jourdan, Fabien, Kechris, Katerina J, Lai, Rachel Pj, Ebbels, Timothy

المصدر: bioRxiv

الوصف: 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. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrateTest.

العلاقة: https://doi.org/10.1101/2024.01.09.574780Test; https://pubmed.ncbi.nlm.nih.gov/38260498Test; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10802464Test/

الإتاحة: https://doi.org/10.1101/2024.01.09.574780Test
https://pubmed.ncbi.nlm.nih.gov/38260498Test
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10802464Test/