This article was first published in December 2023.
Multi-omics integration has evolved into a central pillar of precision medicine, drug discovery, and translational research. As datasets grow in scale and complexity – spanning genomics, transcriptomics, proteomics, metabolomics, epigenomics, and beyond – the challenge is no longer data generation, but meaningful integration.
Drawing on recent frameworks and applied strategies highlighted in The Multiomics Playbook 2025, this updated guide outlines modern integration approaches, computational techniques, and real-world applications.
Figure 1 Integration of multi-omics across the major omics layers. Image Credit: Hossain, et al. 1
Intro to integration
While each omic provides valuable data alone, in concert, new and valuable insights can be gained. Integrating multi-omics data can reveal new cell subtypes, cell interactions and interactions between the different omic layers leading to gene regulatory and phenotypic outcomes. Since each omic layer is causally tied to the next, multi-omics integration serves to disentangle this relationship to properly capture cell phenotype (Figure 1).
We are now in the era of acquiring data from millions of cells. The integration of this large, complex, multimodal data has the potential to reveal much about biological mechanisms and pathways, but this represents a considerable challenge to researchers2. Ultimately, sophisticated computational tools and methodologies are at hand to solve this issue.
But which ones should you use? The principal focus of this feature will be to outline the selection of methodologies that are currently available. It will cover the different types of integration and the options available for bulk, single-cell and spatial datasets.
Why is integration still a challenge?
Ultimately, integration of multi-omics data is a moving target for which a one-size-fits-all approach will not work. Drawing insights from two specific omics requires unique strategies, since each omic has a unique data scale, noise ratio and, hence, its own preprocessing steps. How these omics correlate within the same sample and the same cell is not yet understood.
For example, convention tells us that actively transcribed genes should have greater open chromatin accessibility, which is a correlation we can model but one that may not be true. However, for other modalities, such as RNA-seq and protein data, the most abundant protein may not correlate with high gene expression. This disconnect makes integration very difficult. Moreover, sensitivity remains an issue. A gene detected at the RNA level may simply be missing in the protein dataset.
Furthermore, these omics are not captured with the same breadth, meaning there is inevitably missing data. scRNA-seq can profile thousands of genes, whilst current proteomic methods have a more limited spectrum, with perhaps only 100 proteins. Even if you can predict the level of each protein relatively accurately, the limited number of features in the protein dataset means the cross-modality cell-cell similarity is more difficult to measure. Specific tools are required for specific challenges, hence the variety of tools available.
Types of Integration
When trying to divide up the computational tools for integration meaningfully, one principal distinction between strategies is whether the tool is designed for multi-omics data that is matched (recorded from the same cell) or unmatched (recorded from different cells). While modern methods are frequently able to create the more desirable, matched multi-omics data, much of the previous work in this area involves integrating unmatched data. Furthermore, not only can unmatched data refer to different cells from the same sample, but it can also involve integrating different cells from different samples of the same tissue, taken at different times in different experiments (see Figure 2A).
Integration can also be seen as operating at the horizontal, vertical and diagonal level3. Horizontal integration is the merging of the same omic across multiple datasets. Several tools exist for this purpose, and while it is technically a form of integration, it is not true multi-omics integration and so won’t be considered further.
Figure 2. Computational strategies for single-cell multi-omics integration (A) Levels of input data – paired, partially paired and unpaired. (B) Techniques deployed in integration. Image Credit: Flynn, et al. 4
Vertical integration merges data from different omics within the same set of samples -essentially equivalent to matched integration. The cell is leveraged as the anchor to bring these omics together.
Diagonal integration is the final, and most technically challenging, form of integration. Here, different omics from different cells/different studies are brought together. The anchor can no longer be the cell and instead must be some co-embedded space in which commonality between cells is found. This is essentially ‘unmatched’ integration.
Below you will find a table detailing many of the available multi-omics integration tools. Many companies that provide single cell and spatial sequencing reagents and machines also offer multi-omics analysis programmes, such as Illumina Connected Multiomics and Qiagen OmicSoft. Other companies are purely bioinformatics focused and can range from offering initial data analysis, to cloud-based software and end-to-end support.
Table 1. Table showing a range of tools available for multi-omics integration.
| Provider | Product | Capabilities | Who is this aimed at? |
| Advaita Bio | Multiomics pathway analysis platform | Multidimensional data analysis with pathway, disease and gene ontology analysis, alongside miRNA prediction from mRNA and or protein data | Scientists who wish to upload data and use a simple graphical user interface Ideal for those with limited bioinformatics experience |
| Aspect Analytics | Weave® software for multiomics integration of spatial data | A web-based platform that integrates H&E, IF and lipid MSI data performing cell segmentation | Aimed at those in research institutions, pharmaceutical companies and CROs |
| Bainom | BAINOM-nX: 1 platform | AI/ML driven multiomics and clinical data integration | Ideal for those interested in drug discovery, precision medicine and integration of EHRs |
| Biolizard | Multiomics data integration and analysis service | End-to-end support; integration of user and public data using AI-based modelling and prior scientific knowledge to derive insights | Aimed at those across the sectors and across disciplines who want bioinformatic support |
| BioStrand | LENSai™ Integrated Intelligence Platform | Machine learning and natural language processing (NLP)-based platform for integrating multiple layers of data | Targeted at those wanting to integrate omics and text-based data, wishing to use machine learning and AI without requiring technical expertise Focus appears to be on drug discovery |
| DNAStack | OmicsAI | Standards-compliant solution for performing omics data analysis Free to access; requires payments to clouds for storage and compute requirements | Users who require federated access to data and are looking for a solution that integrates with AWS, Microsoft Azure, or Google Cloud environments |
| Genuity Science | Clinico-genomic datasets for CNS, Cardiometabolic and Inflammatory disease | Multiomics datasets with longitudinal clinical data | They partner with organisations that wish to use datasets for target and biomarker discovery and clinical trails |
| Panomics | Panomics Platform | Platform for data management, analysis and data exploration Project management environment allows addition of metadata and organisation-wide standardisation | Institutions who wish to storage and manage data alongside analysis and collaboration Ideal for multimodal projects with many data files |
| Profiti | SimpliFi™ | Browser-based platform for the integration and visualisation of omics data | Ideal for datasets with ChEBI or UniProt identifiers For users who wish to use cloud-based systems with scalable GPU algorithms |
| Persistent Systems | Cloud-based multiomics analysis platform | Manages large scale data, offering automated pipelines or supporting the use of custom pipelines for analysis and visualisation | For scientists and clinicians looking to partner; use cases include patient stratification and target prioritisation |
| Qlucore | Qlucore Omics Explorer | Performs analysis, visualisation and sharing of omics data Range of analysis and visualisation outputs such as cluster analysis and biomarker discovery analysis Local or cloud-based tool | For those who wish to perform in-depth single cell transcriptomic data analysis without needing bioinformatic input. For those with a clinical focus such as biomarker discovery or cancer diagnosis |
| Resphera Biosciences | Computational and biostatistical analysis service | Offers multiomics integration alongside a range of other biostatistics | They have a microbiome and cancer genomics focus; ideal for those seeking biomarker discovery and precision medicine applications |
| Rosalind Bio | Web-based application for end-to-end single cell and spatial analysis | A range of single cell and spatial analysis solutions, plus access to knowledge graph searching | Ideal for scientists without bioinformatics experience |
| Scailyte | ScaiVision | Integration of multiomics single cell datasets to derive patterns within the data for biomarker discovery or cellular identity | Researchers with a translational clinical interest, such as in analysing clinical trial samples who wish to derive AI-generated insights from their data Ideal for identifying differences between patient cohorts for development of screening tests or patient stratification |
| Signios Bio | Multiomics integration service | Provides integration of genomics, transcriptomics, proteomics, epigenomics, & metabolomics data Offers an AI-driven platform | They serve academic institutions, pharmaceutical and biotech companies, CROs and clinical laboratories Aimed at those who want to outsource analysis |
| Sophia Genetics | SOPHiA DDM™ for Multimodal | Multimodal data integration and visualisation platform Integrated modalities include omics, radiomics, clinical data and digital pathology data | Ideal for those working on precision medicine projects that wish to integrate multimodal data |
| Turing Biosystems | TuringDB.ai | Graph database engine for integration of multimodal/multiscale data using AI, public data and patient data | For those looking for a platform to integrate into systems and tools such as Nextflow, Neo4j and Jupyter Notebooks |
| ViaScientific | Via Foundry platform | An AI-informed platform for the management, processing and analysis of multiomics data Governed by FAIR standards | No restrictions on where data is stored (public/private clouds, local compute clusters) Ideal for those not experienced in bioinformatics that wish to house everything in one place |
| Vindhya Data Science | Bioinformatics service | Small company that offers code development and data analysis/integration | Has partnerships across government, academia, pharmaceutical/biotech companies and the clinic Accepts projects across a range of sizes |
Matched (Vertical) Integration
Vertical integration methods rely on technologies that profile omics data from two or more distinct modalities from within a single cell. From this position, the cell itself can be used as an anchor by which to integrate varying modalities.
Given that the majority of multi-omics tools measure either RNA and protein concurrently or RNA and epigenomic information (mainly via ATAC-seq) concurrently, the majority of tools for vertical integration focus on these pairs of modalities, as can be seen in Table 2.1.
Of the various approaches, there are matrix factorization methods (e.g., MOFA+11), neural network-based (e.g., scMVAE12, DCCA8, DeepMAPS9) and network-based methods (e.g. cite-Fuse, Seurat v4)34. See Figures 2B and 3 for an overview of the techniques.
Figure 3. Single-cell multi-omics data integration methods. The easiest way to integrate multi-omics is to concatenate the original feature matrix of various omics data, but the noise and distinct meaning of values confuse the results of the integration. Machine learning methods extract features from the original matrix and then combine the features across multi-modalities. Deep learning algorithms have also been applied based on various types of networks; e.g., linear, convolution and self-attention. Image Credit: Wang, et al. 34
Unmatched (Diagonal) Integration
Unmatched integration highlights a different and more substantial challenge. The experiments are technically easier to perform because each cell can be treated optimally for the omic analysed. Yet, because the omics data from different modalities are drawn from distinct populations/cells, the cell or tissue cannot be used as an anchor. An anchor has to be derived by some other means.
A general solution to this problem is to project cells into a co-embedded space/non-linear manifold to find commonality between cells in the omics space. Due to the learning-based nature of this task, this space is popularised by a number of machine learning and statistical methods designed to find the most appropriate anchor to align cells.
A popular tool for unmatched integration recently introduced is Graph-Linked Unified Embedding (GLUE28), which can achieve triple-omic integration. Using graph variational autoencoder, GLUE can learn how to anchor features using prior biological knowledge, which it uses to link omic data.
Mosaic Integration
Mosaic integration is an alternative to diagonal integration. This can be used when you have an experimental design where each experiment has various combinations of omics that create sufficient overlap.
For example, if one sample was assessed for transcriptomics and proteomics, another for transcriptomics and epigenomics and a third for proteomics and epigenomics, there is enough commonality between these samples to integrate the data.
Tools such as COBOLT31 and MultiVI32 present modern methods to integrate mRNA and chromatin accessibility in a mosaic fashion. They create a single representation of the cells across datasets to be used in downstream analysis.
Another tool here is MultiMAP35. It is a graph-based method that assumes a uniform distribution of cells across a latent manifold structure to integrate datasets with unique and shared features.
Finally, for mosaic integration, two very recent tools, StabMap30 and bridge integration33 were highlighted in Nature36.
Spatial Integration
With the increasing development of spatial multi-omics methods, new integration strategies are needed for this data. Principally, we are looking at vertical spatial integration as these spatial modalities naturally capture the omics within the confines of a cell or ‘spot’, which works as the anchor.
Existing spatial methods, such as ArchR37, have been successfully deployed for spatial integration. The example here used the RNA modality to indirectly spatially map other modalities, specifically spatial transcriptome and epigenome integration38. Another example is Cell2location39, which was successfully used to integrate spatial RNA and ATAC data in the human heart using a shared nearest neighbours (SNN) strategy40.
Given the popularity of GLUE for diagonal integration of single-cell data, Dr. Jinmiao Chen’s group has recently released SpatialGlue41, a spatial version that allows the integration of omics on spatial sections.
Existing tools are also being modified for spatial analysis. For example, the developers of MOFA+ have recently released MEFISTO42, which uses the same factor analysis approach with a new capability to handle both temporal and spatial components within the model
Ultimately, the development of paired and unpaired spatial integration methods is a space to watch for future developments, as more paired multi-omics methods are released.
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