Aug 2026· Molecular Systems Biology· 1 citation· 47 references
Medicine
TL;DR
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, reveals replicable and interpretable multi-omic patterns.
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 ).
Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science. In this Perspective, the authors discuss recent advances in multi-omics microbiome research, outlining key obstacles in data integration and metadata harmonization, and proposing a roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence advances.
T. Van Den Bossche, Eunice Lazau, Velma T. E. Aho et al.· Nature Communications· 0 citations
A model-to-mechanism burden of proof is proposed that distinguishes prediction from explanation, imputation from observation, attribution from causality, cross-layer coherence from mechanism, and diagnostic performance from biological validity to strengthen, not constrain, computational microbiome science.
The human microbiome functions as a metabolically active organ whose biochemical output is continuously integrated with host physiology. Conventional microbiome surveys, built largely on taxonomic profiling, capture community composition and diversity but resolve neither the functional capacity of these communities nor the bidirectional metabolic exchange that links them to the host. A central limitation is that taxonomy is a poor proxy for function: phylogenetically distinct organisms can perform equivalent reactions, and closely related taxa can diverge metabolically. Resolving host microbiome interactions, therefore, requires integration across heterogeneous, high-dimensional molecular layers, such as metagenomics, metatranscriptomics, proteomics, metabolomics, and host genomic and phenotypic data at a scale and complexity that exceeds classical analytical pipelines. Artificial intelligence (AI) has emerged as a complementary framework for this problem. Machine learning, deep learning, and graph-based models can integrate multi-omics data, infer latent metabolic structure, predict microbial functional potential, and model microbe-metabolite-host relationships as connected networks rather than isolated parts. These approaches have sharpened the discovery of disease-associated microbial and metabolic signatures and candidate therapeutic targets, and they underpin emerging precision medicine applications, including individualized risk stratification, biomarker discovery, and treatment response prediction. Substantial barriers remain, however, including incomplete and non-standardized reference data, limited model interpretability, vulnerability to bias and overfitting, and a shortage of prospective clinical validation. Continued progress in foundation models, real-time microbiome monitoring, and patient-specific metabolic modelling is expected to move the field from descriptive association toward predictive, preventive, and personalized clinical application.
To Lawal, James Momoh, M. Odedele et al.· Australian Journal of Biomed...· 0 citations
Clinical metagenomic data are typically high-dimensional, sparse, and zero-inflated, and are often characterized by limited sample sizes and measurement noise. In addition, different feature-importance methods may produce inconsistent taxon rankings, which limits the stability of single-method feature selection and complicates the identification of candidate core microbiome members. To address this issue, we propose SPFuseRanker, a score-fusion-based ranking framework for integrating multiple microbial importance measures in metagenomic data. The method constructs a consensus score vector by combining heterogeneous importance signals, including statistical tests, correlation analysis, univariate classification performance, and tree-based feature importance. A top-weighted distance function based on Softmax normalization is introduced to emphasize highly ranked taxa during the fusion process. The optimization of the fused score vector is formulated as a minimum-distance problem and solved using a genetic algorithm (GA). We evaluate the proposed method using both synthetic simulations and a real-world systemic lupus erythematosus (SLE) gut microbiome dataset. Experimental results show that SPFuseRanker achieves more stable ranking performance compared with several representative rank aggregation and score fusion methods, particularly in terms of ranking consistency and robustness under noise. In addition, the selected candidate microbial taxa demonstrate improved predictive performance in disease classification tasks, suggesting their potential relevance to SLE-associated microbial signatures. Overall, SPFuseRanker provides a practical framework for integrating multi-source importance information and may serve as a useful tool for candidate core microbiome identification in metagenomic studies.
Si-Rong Chen, Qi Guan, Da Zhou et al.· IEEE transactions on computa...· 0 citations
Taxonomic profiling is fundamental to microbiome research, yet achieving high species-level accuracy remains challenging for complex communities that span bacteria, viruses, eukaryotes, and archaea, and these limitations are exacerbated in low-biomass, host-dominated samples. We introduce Metax, a cross-domain taxonomic profiler that integrates coverage-based probabilistic modeling with an expectation-maximization framework to distinguish true microbial signals from artifacts. Across >600 samples from host-associated, environmental, wastewater, and low-biomass clinical settings, including benchmarks with limited reference representation, Metax improved profiling accuracy, achieving on average 55% higher F1 scores and 45% lower Bray-Curtis dissimilarity than other methods. Moreover, this broad evaluation demonstrated that Metax resolved bacterial and viral signatures of peri-implantitis in oral microbiomes and revealed signals suggestive of reagent-borne contaminants and reference misassemblies in plasma-cell-free DNA. By leveraging genome-wide coverage evidence, Metax enables robust cross-domain profiling across diverse sample types and sequencing depths, including settings where reference databases are highly incomplete.
Zhi-Luo Deng, Nasim Safaei, A. Mchardy· Cell· 0 citations
Integrative systems microbiology increasingly relies on algorithmic approaches capable of extracting biologically meaningful patterns from heterogeneous and often high dimensional, low-sample-size (HDLSS) biological datasets. A major obstacle in this setting is the instability of inferred molecular signatures across cohorts, tissues, and measurement platforms. Here, we address this problem by formulating molecular system inference as a multi-dataset integration task and by applying the Matthews Correlation Coefficient–Recursive Ensemble Feature Selection (MCC-REFS) algorithm to jointly analyze five independent transcriptomic datasets spanning peripheral blood mononuclear cells, whole blood, plasma, and post-mortem tissues. We compared MCC-REFS with three commonly used feature-selection strategies, GRACES, SelectKBest, and Deep Neural Pursuit (DNP), in order to evaluate robustness, convergence, and cross-context reproducibility. MCC-REFS consistently converged on a compact seven-gene system (PPP2CB, SOCS3, ARG1, IL6R, ECHS1, FZD2, TRGV3/5) exhibiting higher stability indices and stronger classification performance than alternative methods. Generalization was assessed using an independent multi-layer perceptron classifier across validation cohorts with differing tissue origin and sequencing technologies, demonstrating preservation of discriminative structure. To support interpretation, we integrated functional, pharmacological, and interventional knowledge from DrugBank, DGIdb, and Open Targets, enabling the mapping of inferred gene systems onto pathways, known drug targets, and ongoing clinical investigations. Taken together, this work presents an algorithmic framework for multi-dataset and multi-omics integration in systems microbiology, illustrating how stable and interpretable molecular patterns can be identified from heterogeneous data, with Long COVID serving as a representative case study.
B. Varga, M. Martínez-Archundia, L. Willemsen et al.· Frontiers in Systems Biology· 0 citations
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