# MUUMI: an R package for statistical and network-based meta-analysis for multi-omics data integration

## TL;DR

MUUMI is an R package that combines statistical and network-based methods to integrate multi-omics data, helping researchers uncover meaningful biological insights.

## Contribution

MUUMI unifies statistical meta-analysis and network-based integration in a single framework for multi-omics data.

## Key findings

- MUUMI identifies robust molecular signatures using multiple meta-analytical methods.
- The package integrates multi-omics layers through similarity network fusion.
- Case studies on IPF and THP-1 macrophages revealed biologically coherent signatures.

## Abstract

Disentangling physiopathological mechanisms of biological systems through high-level integration of omics data has become a standard procedure in life sciences. However, platform heterogeneity, batch effects, and the lack of unified methods for single- and multi-omics analyses represent relevant drawbacks that hinder the extrapolation of a meaningful biological interpretation. While statistical meta-analysis is widely used to integrate several omics datasets of the same type, it does not allow the integration of multi-modal data deriving from multi-omics experiments. Network science is at the forefront of systems biology, where the inference of molecular interactomes allowed the investigation of perturbed biological systems, by shedding light on the disrupted relationships that keep the homeostasis of complex systems.

Here, we present MUUMI, an R package that unifies statistical meta-analysis and network-based omics data integration within a single analytical framework. MUUMI allows the identification of robust molecular signatures through multiple meta-analytical methods, inference and analysis of molecular interactomes and the integration of multiple omics layers through similarity network fusion. We demonstrate the functionalities of MUUMI by presenting two case studies in which we analysed (1) 17 transcriptomic datasets on idiopathic pulmonary fibrosis (IPF) from both microarray and RNA-Seq platforms and (2) multi-omics data of THP-1 macrophages exposed to different polarising stimuli. In both examples, MUUMI revealed biologically coherent signatures, underscoring its value in elucidating complex biological processes.

MUUMI leverages omics data meta-analysis, integration and interpretation that implements both traditional and network-based approaches to unleash the power of multi-study datasets. Statistical and network-based approaches are integrated in a unique framework, allowing the user to derive robust and biologically meaningful results from different studies and datasets. MUUMI is an open-source package and is freely available at https://github.com/fhaive/muumi.

The online version contains supplementary material available at 10.1186/s12859-026-06394-3.