Multi-omics data integration and analysis
Genomic, transcriptomic, proteomic, imaging and clinical data rarely tell a complete story in isolation. This page explains when integration is scientifically worthwhile, how Sonrai does it, and what it produces for a programme.
A single data type rarely answers the question that matters
Most precision medicine programmes generate data across multiple modalities. Genomic sequencing, RNA-seq, proteomic profiling, digital pathology, and clinical outcome data are all collected, often by different teams, stored in different systems, and analysed independently.
That independence is the problem. Individual modalities answer narrower questions than the programme actually needs. A transcriptomic analysis might identify differentially expressed genes without explaining whether those genes translate to protein-level changes. A pathology analysis might reveal tissue-level patterns without connecting them to the molecular biology. Clinical outcome data shows who responded — not why.
Multi-omics integration may add value when the biological question requires evidence from more than one dimension. It is not a substitute for independent validation — findings from integrated analysis still need to be assessed across additional cohorts and evidence sources before they can support major programme decisions. Common scenarios where integration changes what is possible:
Why datasets that exist alongside each other are not the same as datasets that work together
Most translational teams have data. The challenge is that data collected for different purposes, by different teams, in different formats, on different timescales, does not integrate automatically. Several distinct problems need to be solved before biological analysis can begin.
The data types Sonrai works with and the analytical methods applied
Sonrai scientists work alongside your team to integrate molecular, imaging, and clinical data within Sonrai Discovery, a governed cloud-native environment built for multimodal biomedical data. The combination of scientific expertise and analytical infrastructure means that integration goes beyond data engineering — findings are interpreted in the context of your programme from the start.
For a complete reference of the data types, formats, pipelines and analytical workflows Sonrai supports across each modality, see the precision medicine data modalities page.
Analytical methods
Sonrai applies analytical methods based on the biological question and available data types rather than applying a fixed workflow. Methods used across multi-omics integration engagements include:
Outputs and evidence from a multi-omics integration engagement
Outputs from Sonrai engagements are structured to support programme decisions — not just to report findings. Every output is delivered with full analytical provenance, meaning findings can be traced, reviewed, and reproduced as the programme evolves.
How integrated analysis connects to the decisions that move programmes forward
Multi-omics integration is not an end in itself. It is most valuable when the findings directly inform a programme decision. Sonrai's three principal Decision Modules each represent a distinct decision context where integrated evidence plays an important role.
Not sure which decision is most relevant to your current programme? The Precision Medicine Decisions page explores seven common decision points in translational and clinical development — and may help you identify where integrated evidence would have the most immediate impact.
What multi-omics integration has supported in practice
The case studies below represent programmes where multi-omics integration was central to the analytical work. All outcomes described are drawn from published case study content on this website.
"For us, it's been like having an extension of our company, with that expertise that we could bring in."
Questions about multi-omics data integration and analysis
What is multi-omics data integration?
Multi-omics data integration is the process of combining data from multiple biological measurement types, such as genomics, transcriptomics, proteomics, epigenomics, and imaging, within a single analytical framework. Rather than analysing each data type independently, integration enables researchers to identify patterns and relationships that would not be visible in any single modality. In a clinical trial context, this means connecting molecular signals with imaging findings and clinical outcomes to build a more complete picture of treatment response.
When is multi-omics integration scientifically worthwhile?
Multi-omics integration is most valuable when a single data type cannot answer the biological question at hand. If your programme needs to explain why patients responded differently to treatment, identify biomarkers that hold up across independent cohorts, understand a drug's mechanism of action, or build patient selection criteria for a future trial, a single modality will rarely provide sufficient evidence. Integration becomes essential when the programme decision depends on convergent evidence from multiple biological dimensions.
What data types can Sonrai integrate?
Sonrai integrates genomic, transcriptomic, epigenomic, proteomic, metabolomic, imaging, and clinical data within a single governed analytical environment. This includes RNA-seq, whole genome sequencing, methylation data, mass spectrometry-derived proteomics, flow cytometry, CyTOF, Olink proteomics, digital pathology and H&E imaging, and structured clinical trial datasets. Data from different sample sources, such as tissue, plasma, and urine, can be combined within the same analytical framework.
What is the main technical challenge in integrating omics, imaging and clinical data?
The main challenges are data heterogeneity, governance, and reproducibility. Different modalities use different formats, scales, and collection methods. Harmonising them requires careful data engineering, quality assessment, and normalisation before any biological analysis begins. Beyond the technical integration, the environment in which analysis runs must be governed, versioned, and auditable so that findings can be reviewed, reproduced, and defended as the programme progresses.
What analytical methods does Sonrai use for multi-omics integration?
Sonrai applies a range of analytical methods depending on the biological question and available data types. These include Multi-Omics Factor Analysis (MOFA+) for unsupervised integration across modalities, differential expression analysis, survival analysis, machine learning including XGBoost for predictive modelling, and imaging AI for pathology data. All analyses run within Sonrai Discovery, a governed cloud environment with versioned datasets and traceable pipelines.
How does Sonrai support reproducibility in integrated analyses?
All analyses run within Sonrai Discovery, a secure Trusted Research Environment with versioned datasets, traceable analytical pipelines, and complete output provenance. Every finding can be traced back to the exact data version and analytical steps used to generate it. This means analyses can be revisited, independently reviewed, and reproduced months or years after the original work, which is essential for regulatory review, partner scrutiny, and internal governance.
Can Sonrai integrate our proprietary data with publicly available datasets?
Yes. Sonrai supports integration of proprietary clinical and molecular datasets alongside curated public resources within the same governed environment. Public proteomic databases, gene expression datasets, and published cohort data can be incorporated to provide validation context or additional biological reference points. Proprietary data remains protected within each client's isolated instance of Sonrai Discovery and is never shared across organisational boundaries.
How does multi-omics integration support patient stratification?
Patient stratification built on a single biomarker or data type is often fragile, particularly in heterogeneous disease populations. Multi-omics integration enables stratification based on converging evidence across molecular, imaging, and clinical dimensions. Subgroups identified through multiple independent lines of evidence are more biologically co