multi-omics

A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study

  • Objective: This multi-omics study used Bayesian network analysis and Mendelian Randomization (MR) on the IMI-DIRECT cohort to determine the causal network linking glucose/insulin dynamics, fat distribution (MRI), and plasma proteins to MASLD (liver fat accumulation).
  • Key Causal Driver: High Basal Insulin Secretion Rate (BasalISR) was identified as the primary causal driver of liver fat accumulation in both the non-diabetes and Type 2 Diabetes cohorts, suggesting it is a modifiable therapeutic target.
  • Mechanistic Insights: The study revealed a self-reinforcing bidirectional association between Visceral Adipose Tissue (VAT) and liver fat. It also identified sex-specific proteomic drivers of liver fat, with GUSB being more predictive in females and LEP (Leptin) in males.
23 January 2026

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

PubMed: 33502086 DOI: 10.15252/msb.20209703 Overview generated by: Gemini 2.5 Flash, 27/11/2025
23 January 2026

Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits

  • Method: Multi-INTACT is a novel statistical framework that jointly analyzes GWAS, eQTL, and pQTL summary statistics to model the causal chain from genetic variant \(\rightarrow\) gene expression \(\rightarrow\) protein level \(\rightarrow\) complex trait.
  • Causal Partitioning: The method successfully partitions GWAS heritability and identifies the precise molecular layer (transcriptome or proteome) mediating the genetic effect, showing that many effects are primarily mediated by protein levels.
  • Impact: Applied to complex traits like lipids, Multi-INTACT confirmed known genes and revealed novel gene-trait associations by providing the mechanistic evidence (the specific regulatory path) driving the GWAS signal.
23 January 2026

DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

  • Method: DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics datasets) is a supervised multi-block PLS/GCCA method for joint analysis of heterogeneous omics data.
  • Feature Selection: It uses a sparse penalty (L1) to select a minimal set of key molecular drivers that are highly correlated across omics layers and maximally associated with a specific clinical outcome (e.g., disease status).
  • Impact: DIABLO demonstrated superior classification accuracy and biological coherence in identifying integrated biomarkers for complex diseases, such as the molecular drivers distinguishing breast cancer subtypes.
23 January 2026

Multi-Omics Factor Analysis a framework for unsupervised integration of multi-omics data sets

PubMed: 29925568 DOI: 10.15252/msb.20178124 Overview generated by: Gemini 2.5 Flash, 27/11/2025
23 January 2026

Multi-Omic Graph Diagnosis (MOGDx): a data integration tool to perform classification tasks for heterogeneous diseases

PubMed: 39177104 DOI: 10.1093/bioinformatics/btae523 Overview generated by: Gemini 2.5 Flash, 27/11/2025
23 January 2026

Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO

  • Method: MEFISTO (Multi-omics Factor Analysis Informed by Spatial and Temporal Omics) is an extension of MOFA that uses Gaussian Process (GP) priors on latent factors to model spatial or temporal dependencies between samples.
  • Key Capabilities: It performs spatio-temporally informed dimensionality reduction, allowing factors to change smoothly over time/space. It also enables robust interpolation of data for unobserved locations or time points.
  • Application: MEFISTO successfully analyzed data from spatial transcriptomics, longitudinal microbiome studies, and single-cell multi-omics atlases to align and extract common developmental or temporal patterns.
23 January 2026

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

  • Topic: A systematic benchmarking of nine joint Dimensionality Reduction (jDR) methods for integrating multi-omics data, using simulated data, TCGA cancer cohorts, and single-cell data.
  • Key Findings: intNMF excelled in unsupervised clustering tasks, while MCIA (Multiple Co-Inertia Analysis) was identified as the most robust, all-around performer across various prediction and integration tasks.
  • Resource: The study created a reproducible code platform called momix to aid researchers in selecting and applying jDR methods, offering practical guidelines for multi-omics integration.
23 January 2026

Variable selection for generalized canonical correlation analysis

  • Method: The paper introduces SGCCA (Sparse Generalized Canonical Correlation Analysis), an extension of the RGCCA framework designed for integrating three or more multi-omics data blocks.
  • Key Innovation: SGCCA incorporates a sparse (\(L_1\)) penalty to simultaneously perform dimension reduction and variable selection.
  • Significance: SGCCA pinpoints a minimal, highly relevant set of features from each omics layer that drives the shared correlation structure across the integrated datasets, significantly improving the biological interpretability of multi-omics results.
23 January 2026

A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data

  • Method: The paper proposes a fully Bayesian latent variable model for integrative clustering analysis of multi-omics data, building on the iCluster framework.
  • Key Innovation: The Bayesian approach incorporates adaptive shrinkage priors to enforce sparsity (feature selection) on the omics-specific loading matrices, which simultaneously identifies robust disease subtypes and their minimal molecular signatures.
  • Impact: Applied to TCGA cancer data, the model demonstrated superior performance in identifying clinically relevant, stable subtypes and the specific genes/loci driving the differences across mRNA, methylation, and CNV data.
23 January 2026

The tumor multi-omic landscape of endometrial cancers developed on a germline genetic background of adiposity

  • Objective: This study used Mendelian randomization (MR) to investigate the causal effect of germline genetic risk for adiposity (BMI) on the multi-omic landscape (gene expression, somatic mutations, and immune microenvironment) of endometrial cancers (EC).
  • Key Findings: Genetically predicted higher BMI was causally associated with increased expression of the oncogene MDM2 in EC tumors. It was also linked to a detrimental change in the tumor immune microenvironment, specifically decreasing CD4+ and cytotoxic T cell infiltration.
  • Conclusion: The study suggests that germline adiposity fuels EC progression by promoting a more immunosuppressive tumor environment and activating key survival pathways, but not by altering the frequency of common somatic mutations.
23 January 2026

Statistical Methods for Integrative Clustering of Multi-omics Data

PubMed: 36929074 DOI: 10.1007/978-1-0716-2986-4_5 Overview generated by: Gemini 2.5 Flash, 27/11/2025
23 January 2026

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome

  • Approach: A multi-omics framework was developed, combining untargeted metabolomics (measuring both known and unknown metabolites) with two-sample Mendelian Randomization (MR) using metabolite-QTLs (mQTLs).
  • Causal Findings: 23 metabolites (15 known and 8 unknown) were identified as causally associated with BMI, with specific pathways like amino acid catabolism and lipid metabolism being implicated in the obesity metabolome.
  • Innovation: A novel pathway enrichment method was used to infer the metabolic function of the causally associated unknown metabolites based on their shared genetic links with identified metabolites.
23 January 2026

A General Framework for Integrative Analysis of Incomplete Multi-omics Data

  • Problem: Multi-omics analysis is challenged by missing values (incomplete subject profiling) and detection limits (censored data).
  • Method: A general statistical framework based on a joint likelihood function and an Expectation-Maximization (EM) algorithm was developed to rigorously model and integrate multi-omics data while accounting for arbitrary missingness and censoring.
  • Impact: Applied to the SPIROMICS cohort, the framework demonstrated superior statistical power and reduced bias compared to ad-hoc imputation methods, particularly in identifying protein quantitative trait loci (pQTLs) and biomarker-phenotype associations.
23 January 2026

Biomarker identification by interpretable maximum mean discrepancy

  • Topic: Introduction of SpInOpt-MMD (Sparse, Interpretable, and Optimized Maximum Mean Discrepancy), a novel method for simultaneously performing two-sample testing and biomarker feature selection in high-dimensional omics data.
  • Method: SpInOpt-MMD integrates sparse and interpretable optimization into the Maximum Mean Discrepancy (MMD) test, allowing it to detect statistically significant group differences and identify the features (biomarkers) responsible in a single step.
  • Impact: The method is effective for small sample sizes and outperforms other feature selection approaches (like SHAP) in several contexts, offering a powerful, unified approach for biomarker discovery in multi-omics and biomedical applications.
23 January 2026

Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank

  • Method: MILTON (Machine Learning with Phenotype Associations), an ensemble machine-learning framework, was developed to integrate multi-omics (including plasma proteomics) and biomarker data from the UK Biobank to predict disease risk.
  • Objective: To demonstrate how these biomarker-based predictions can augment genetic association analyses in a phenome-wide context.
  • Impact: MILTON outperformed Polygenic Risk Scores (PRSs) in predicting incident disease. Its application in a PheWAS improved signals for 88 known and 14 novel genetic associations, showing its utility in empowering genetic discovery for complex diseases by improving disease classification.
23 January 2026
No matching items