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Peer-reviewedCC BY 4.0Molecular Systems Biology

Joint-RPCA: domain-aware multi-omics integration for systems microbiology

  • Bianca Cordazzo Vargas1
  • Cameron Martino1,2
  • Amanda Hazel Dilmore3,4
  • Jessica L Metcalf5
  • Zachary M Burcham5,6
  • Leo Lahti7
  • Aituar Bektanov7
  • Tuomas Borman7
  • Veikko Salomaa8
  • Teemu Niiranen8,9
  • Aki S Havulinna7,8,10
  • Rachel Gregor11
10 more authors
  • Stav Eyal12,13
  • Michael M Meijler12,14
  • Itzhak Mizrahi12,13
  • Se Jin Song15
  • Andrew Bartko15
  • Pieter C Dorrestein3,15,16,17
  • James T Morton3,18
  • Daniel McDonald3
  • Rob Knight3,16,18,19
  • Liat Shenhav1,20,21,22
22 affiliations
  1. 1Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY USA
  2. 2Leaven Foods, San Diego, CA USA
  3. 3Department of Pediatrics, University of California San Diego, La Jolla, CA USA
  4. 4Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA USA
  5. 5Department of Animal Sciences, Colorado State University, Fort Collins, CO USA
  6. 6Department of Microbiology, University of Tennessee, Knoxville, TN USA
  7. 7Department of Computing, FI-20014 University of Turku, Turku, Finland
  8. 8Department of Public Health, Finnish Institute for Health and Welfare, Helsinki, Finland
  9. 9Department of Internal Medicine, Turku University Hospital and University of Turku, Turku, Finland
  10. 10Institute for Molecular Medicine Finland, FIMM- HiLIFE, Helsinki, Finland
  11. 11Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON Canada
  12. 12National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be’er Sheva, Israel
  13. 13Department of Life Sciences, Ben-Gurion University of the Negev, Be’er Sheva, Israel
  14. 14Department of Chemistry, Ben-Gurion University of the Negev, Be’er Sheva, Israel
  15. 15Center for Microbiome Innovation, University of California San Diego, La Jolla, CA USA
  16. 16Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA USA
  17. 17Department of Pharmacology, School of Medicine, University of California San Diego, La Jolla, CA USA
  18. 18Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA USA
  19. 19Department of Bioengineering, University of California San Diego, San Diego, La Jolla, CA USA
  20. 20Department of Microbiology, New York University Grossman School of Medicine, New York, NY USA
  21. 21Department of Obstetrics and Gynecology, New York University Grossman School of Medicine, New York, NY USA
  22. 22Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY USA
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Abstract

Integrating multi-omics data is essential for microbiome research, as microbial communities are shaped by and respond to interdependent processes, including taxonomic composition, metabolite production and utilization, and gene expression. However, accurately capturing ecosystem-wide patterns across these modalities is statistically challenging due to differences in scale, sparsity, and compositionality. While a growing number of multi-omics methods have emerged, they differ in their mathematical objectives and modeling assumptions, which in turn shape how biological patterns are represented and interpreted. This underscores the need for tools that explicitly account for the statistical properties of microbial ecosystems. Here, we present Joint Robust Principal Component Analysis (Joint-RPCA), a method designed with these statistical properties in mind and broadly applicable to multi-omics settings with similar challenges. Built on the OptSpace matrix completion framework, Joint-RPCA assumes an underlying shared low-rank structured component across modalities to identify shared variation and cross-modal associations from matched samples. Within this setting and under these statistical assumptions, Joint-RPCA showed stronger performance than the benchmarked general-purpose methods in phenotype separation and feature association tasks, achieving up to sixfold improvement in classification accuracy and over 100-fold faster runtimes. Applied to real-world datasets, including the Integrative Human Microbiome Project (iHMP), mammalian gut microbiomes, and decomposition studies, Joint-RPCA reveals replicable and interpretable multi-omic patterns, offering a scalable and domain-aware solution for systems-level microbiome analysis. Joint-RPCA is available in both Python (https://github.com/biocore/gemelli) and R (https://bioconductor.org/packages/mia).

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Cite

BibTeXRIS
BibTeX
@article{vargas2026jointrpca,
  title = {{Joint-RPCA: domain-aware multi-omics integration for systems microbiology}},
  author = {Bianca Cordazzo Vargas and Cameron Martino and Amanda Hazel Dilmore and Jessica L Metcalf and Zachary M Burcham and Leo Lahti and Aituar Bektanov and Tuomas Borman and Veikko Salomaa and Teemu Niiranen and Aki S Havulinna and Rachel Gregor and Stav Eyal and Michael M Meijler and Itzhak Mizrahi and Se Jin Song and Andrew Bartko and Pieter C Dorrestein and James T Morton and Daniel McDonald and Rob Knight and Liat Shenhav},
  journal = {Molecular Systems Biology},
  year = {2026},
  month = aug,
  doi = {10.1038/s44320-026-00236-3},
  url = {https://doi.org/10.1038/s44320-026-00236-3},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Joint-RPCA: domain-aware multi-omics integration for systems microbiology
AU  - Bianca Cordazzo Vargas
AU  - Cameron Martino
AU  - Amanda Hazel Dilmore
AU  - Jessica L Metcalf
AU  - Zachary M Burcham
AU  - Leo Lahti
AU  - Aituar Bektanov
AU  - Tuomas Borman
AU  - Veikko Salomaa
AU  - Teemu Niiranen
AU  - Aki S Havulinna
AU  - Rachel Gregor
AU  - Stav Eyal
AU  - Michael M Meijler
AU  - Itzhak Mizrahi
AU  - Se Jin Song
AU  - Andrew Bartko
AU  - Pieter C Dorrestein
AU  - James T Morton
AU  - Daniel McDonald
AU  - Rob Knight
AU  - Liat Shenhav
PY  - 2026
DA  - 2026/08/18
JO  - Molecular Systems Biology
DO  - 10.1038/s44320-026-00236-3
UR  - https://doi.org/10.1038/s44320-026-00236-3
AB  - Integrating multi-omics data is essential for microbiome research, as microbial communities are shaped by and respond to interdependent processes, including taxonomic composition, metabolite production and utilization, and gene expression. However, accurately capturing ecosystem-wide patterns across these modalities is statistically challenging due to differences in scale, sparsity, and compositionality. While a growing number of multi-omics methods have emerged, they differ in their mathematical objectives and modeling assumptions, which in turn shape how biological patterns are represented and interpreted. This underscores the need for tools that explicitly account for the statistical properties of microbial ecosystems. Here, we present Joint Robust Principal Component Analysis (Joint-RPCA), a method designed with these statistical properties in mind and broadly applicable to multi-omics settings with similar challenges. Built on the OptSpace matrix completion framework, Joint-RPCA assumes an underlying shared low-rank structured component across modalities to identify shared variation and cross-modal associations from matched samples. Within this setting and under these statistical assumptions, Joint-RPCA showed stronger performance than the benchmarked general-purpose methods in phenotype separation and feature association tasks, achieving up to sixfold improvement in classification accuracy and over 100-fold faster runtimes. Applied to real-world datasets, including the Integrative Human Microbiome Project (iHMP), mammalian gut microbiomes, and decomposition studies, Joint-RPCA reveals replicable and interpretable multi-omic patterns, offering a scalable and domain-aware solution for systems-level microbiome analysis. Joint-RPCA is available in both Python (https://github.com/biocore/gemelli) and R (https://bioconductor.org/packages/mia).
SN  - 1744-4292
ER  -