We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

Multi-Omics Data Integration Platforms Market Outlook

According to our latest research, the global Multi-Omics Data Integration Platforms market size is valued at USD 1.62 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.1% expected during the forecast period. By 2033, the market is projected to reach approximately USD 4.38 billion, driven by the surging demand for comprehensive biological data analysis in healthcare and life sciences. Key growth factors include the increasing adoption of precision medicine, the rapid expansion of genomics research, and the need for integrated solutions that can manage, analyze, and interpret complex multi-omics datasets for actionable insights.

The primary growth driver for the Multi-Omics Data Integration Platforms market is the escalating demand for precision medicine and personalized therapies. As healthcare providers and pharmaceutical companies increasingly shift towards individualized treatment regimens, the integration of diverse omics data—such as genomics, transcriptomics, proteomics, and metabolomics—has become essential. These platforms enable researchers to uncover complex biological interactions, identify novel biomarkers, and accelerate drug discovery processes. The convergence of high-throughput sequencing technologies with advanced computational tools has further amplified the need for robust multi-omics integration, facilitating more accurate disease modeling and patient stratification.

Another significant factor fueling market expansion is the rising volume and complexity of biological data generated by next-generation sequencing (NGS), mass spectrometry, and other high-throughput omics technologies. Research institutions, academic centers, and pharmaceutical companies are increasingly investing in multi-omics data integration platforms to manage and analyze these vast datasets efficiently. The integration of artificial intelligence and machine learning algorithms into these platforms further enhances their analytical capabilities, enabling the extraction of meaningful patterns and insights from heterogeneous data sources. This technological advancement is not only accelerating research and development activities but also improving clinical decision-making and patient outcomes.

Additionally, the increasing prevalence of chronic diseases and the growing emphasis on translational research are propelling the adoption of multi-omics data integration platforms across various healthcare settings. Hospitals, clinics, and diagnostic laboratories are leveraging these platforms to support early disease detection, monitor disease progression, and tailor therapeutic interventions. The expanding applications of multi-omics platforms in agriculture, environmental science, and food safety are also contributing to market growth. Furthermore, strategic collaborations among academic institutions, industry players, and government agencies are fostering innovation and driving the development of next-generation data integration solutions.

From a regional perspective, North America currently leads the global multi-omics data integration platforms market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of leading biotechnology and pharmaceutical companies, advanced healthcare infrastructure, and substantial investments in omics research. Europe follows closely, driven by strong government support for genomics and precision medicine initiatives. Meanwhile, the Asia Pacific region is poised for the fastest growth over the forecast period, fueled by increasing healthcare expenditure, expanding research activities, and rising awareness of the benefits of integrated omics approaches. Latin America and the Middle East & Africa are also witnessing steady growth, supported by improving research capabilities and growing healthcare investments.

Component Analysis

The Component segment of the Multi-Omics Data Integration Platforms market is primaril

The global multiomics market, valued at $3.11 billion in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 15.26% from 2025 to 2033. This expansion is driven by several key factors. Advancements in sequencing technologies, particularly next-generation sequencing (NGS), are enabling researchers to analyze multiple omics datasets simultaneously, providing a more comprehensive understanding of complex biological systems. This holistic approach is proving invaluable in drug discovery and development, accelerating the identification of novel therapeutic targets and biomarkers. Furthermore, the increasing prevalence of chronic diseases, such as cancer and neurodegenerative disorders, is fueling demand for more precise diagnostic and therapeutic tools, bolstering the multiomics market. Growing investments in research and development across both academia and the pharmaceutical and biotechnology sectors further contribute to this market's rapid growth. The integration of artificial intelligence (AI) and machine learning (ML) in multiomics data analysis is also significantly impacting the field, enabling faster and more accurate interpretations of complex datasets. The market segmentation reveals significant opportunities across various product types, platforms, and applications. While instruments and reagents constitute major segments, the 'Other Products' category, encompassing software and data analysis tools, is experiencing rapid growth due to the increasing complexity of multiomics data. Single-cell multiomics, offering higher resolution and insights into cellular heterogeneity, is gaining traction over bulk multiomics. Within platforms, genomics maintains a dominant position, followed by transcriptomics and proteomics. However, integrated omics platforms, offering a more comprehensive analysis of multiple datasets simultaneously, are showing significant potential for future growth. Oncology and neurology are leading application areas, with substantial research focused on developing personalized medicine approaches leveraging multiomics data. The academic and research institutes segment remains a key end-user, while pharmaceutical and biotechnology companies are increasingly adopting multiomics for drug discovery and development, promising sustained long-term market growth. Competition among established players like Illumina, Thermo Fisher Scientific, and Agilent Technologies, alongside emerging innovative companies, drives further market dynamism and technological advancement. Recent developments include: February 2024: Vizzhy Inc. launched the world's inaugural Multiomics Lab in Bengaluru, India, heralding a major advancement in healthcare innovation. Equipped with cutting-edge tools and health AI technology, the lab enables physicians to pinpoint root causes and offer personalized recommendations for their patients.September 2023: MGI, a provider of technology and tools for life science, introduced the DCS Lab Initiative to stimulate crucial scientific research. This initiative encourages large-scale multiomics laboratories. Under the initiative, the organization offers products for numerous applications, including cell omics, DNA sequencing, and spatial omics based on DNBSEQ technologies, to specified research institutions globally.April 2023: Biomodal, formerly Cambridge Epigenetix, introduced a new duet multiomics solution that can enable simultaneous phased reading of epigenetic and genetic information in a single, low-volume sample.. Key drivers for this market are: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Potential restraints include: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Notable trends are: The Bulk Multiomics Segment is Expected to Hold the Largest Share of the Market.

The Multiomics Market offers a range of products, including instruments, consumables, software, and services. Instruments include sequencing systems, mass spectrometers, and flow cytometers. Consumables encompass reagents, kits, and microarrays. Software solutions provide data analysis and visualization capabilities. Services include sample preparation, data analysis, and interpretation. Recent developments include: September 2023: The chromium single-cell gene expression flex assay manufactured by 10x Genomics Inc. now offers high throughput multi-omic cellular profiling as a commercially available capability thanks to the introduction of a new kit. Researchers and their options may detect simultaneous gene and protein expression, which can be expanded at a greater scale thanks to the new kit, which makes the multi-omic characterization of cell populations simple and efficient. The company's product portfolio was able to grow due to this technique., February 2023: Becton, Dickinson, and Company introduced the Rhapsody HT Xpress System, a high-throughput single-cell multiomics platform, to broaden the field of scientific research. With up to eight times more cells per sample than previous BD single-cell analyzers, this innovative technology allows scientists to extract, label, and analyze individual cells at a high sample throughput. This plan should assist the business in expanding its product's uses and serving more clients.. Notable trends are: Rising integration of multi-omics data is driving the market growth.

Multi-Omics Data Integration Platforms Market Outlook

According to our latest research, the global Multi-Omics Data Integration Platforms market size reached USD 1.47 billion in 2024, reflecting robust growth driven by the increasing adoption of precision medicine and advanced bioinformatics. The market is projected to expand at a CAGR of 14.2% during the forecast period, reaching a value of USD 4.19 billion by 2033. This remarkable growth is primarily fueled by the rising demand for comprehensive data analysis in genomics, proteomics, and other omics sciences, facilitating breakthroughs in drug discovery, diagnostics, and personalized healthcare.

One of the primary growth factors for the Multi-Omics Data Integration Platforms market is the escalating volume and complexity of biological data generated through next-generation sequencing, mass spectrometry, and other high-throughput technologies. As research institutions and healthcare providers increasingly rely on multi-omics approaches to gain a holistic view of biological systems, there is a pressing need for platforms that can seamlessly integrate, manage, and interpret diverse datasets. The convergence of genomics, transcriptomics, proteomics, metabolomics, and epigenomics data is enabling researchers to uncover novel biomarkers, understand disease mechanisms, and develop more targeted therapies, thereby driving the demand for sophisticated integration solutions.

Another significant driver is the rapid advancement in artificial intelligence and machine learning algorithms, which are being incorporated into multi-omics data integration platforms to enhance data analysis capabilities. These technologies empower platforms to deliver actionable insights from complex, multidimensional datasets, accelerating the pace of discovery in drug development and precision medicine. Pharmaceutical and biotechnology companies are increasingly investing in these platforms to streamline their R&D processes, reduce time-to-market for new drugs, and improve patient outcomes. Furthermore, the growing trend toward cloud-based deployment is making these platforms more accessible, cost-effective, and scalable, further propelling market growth.

The expanding application of multi-omics integration in clinical diagnostics and personalized healthcare is also contributing to market expansion. With the global healthcare sector shifting toward patient-centric models, there is a heightened emphasis on identifying individual molecular profiles to guide treatment decisions. Multi-omics platforms enable clinicians to integrate genetic, proteomic, and metabolomic data for comprehensive patient assessment, leading to more accurate diagnoses and the development of tailored therapeutic strategies. This paradigm shift is particularly evident in oncology, rare diseases, and complex chronic conditions, where multi-omics integration is proving invaluable for early detection, prognosis, and therapeutic monitoring.

From a regional perspective, North America continues to dominate the Multi-Omics Data Integration Platforms market, accounting for the largest share in 2024 due to its advanced healthcare infrastructure, strong presence of leading biotech companies, and substantial investments in genomics research. Europe follows closely, driven by supportive government initiatives and a thriving academic research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by increasing healthcare expenditure, expanding genomics research capabilities, and rising awareness of precision medicine. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with growing adoption of multi-omics technologies in research and clinical settings.

Component Analysis

The component segment of the Multi-Omics Data Integration Platforms market is bifurcated into software and services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of data integration, offering robust analytical tools, visualization modules, and interoperability features that facilitate the seamless amalgamation of diverse omics datasets. These platforms are designed to handle massive data volumes, manage data heterogeneity, and provide user-friendly interfaces for researchers and clinicians. The increasing sophistication of software, including AI-driven analytics and cloud-based functionalities, is enhancing their adoption across pharmaceutical, academic, and clinical

Multi-Omics Data Integration SaaS Market Outlook

According to our latest research, the global Multi-Omics Data Integration SaaS market size in 2024 is valued at USD 1.98 billion, reflecting the rapidly growing adoption of integrated omics solutions worldwide. The market is registering a robust CAGR of 15.3% and is forecasted to reach USD 5.54 billion by 2033. This exceptional growth is primarily driven by the increasing demand for comprehensive biological data analysis in drug discovery, precision medicine, and clinical diagnostics. As per our latest research, the convergence of cloud computing, advanced analytics, and the exponential rise in omics data generation are key propellants fueling this market’s expansion.

One of the most significant growth factors underpinning the expansion of the Multi-Omics Data Integration SaaS market is the surge in next-generation sequencing (NGS) and high-throughput omics technologies. The cost of sequencing genomes and other omics layers has plummeted over the past decade, resulting in an unprecedented volume of data generation across genomics, proteomics, metabolomics, and transcriptomics. This data deluge necessitates advanced integration platforms, and SaaS-based solutions are uniquely positioned to provide scalable, secure, and collaborative environments for researchers and clinicians. The integration of AI and machine learning algorithms further enhances the value of these platforms by enabling sophisticated data mining, biomarker discovery, and predictive modeling, which are critical for advancing precision medicine and accelerating drug development pipelines.

Another pivotal growth driver is the increasing focus on personalized healthcare and precision medicine initiatives globally. Governments, research institutions, and healthcare providers are investing heavily in multi-omics approaches to unravel complex disease mechanisms, identify novel therapeutic targets, and tailor interventions to individual patient profiles. SaaS-based multi-omics platforms offer the flexibility and interoperability required to combine diverse datasets from genomics, proteomics, transcriptomics, and beyond, providing holistic insights into biological systems. This capability is particularly valuable in oncology, rare disease research, and chronic disease management, where integrated omics analyses are transforming clinical diagnostics and treatment paradigms. The seamless accessibility and collaborative features of SaaS platforms are further accelerating cross-institutional research and translational medicine efforts.

Regulatory support and increasing investments from both public and private sectors are also catalyzing the growth of the Multi-Omics Data Integration SaaS market. Governments in North America, Europe, and Asia Pacific are launching large-scale genomics and multi-omics projects, providing funding for infrastructure development, and fostering public-private partnerships. Additionally, the pharmaceutical and biotechnology industries are embracing SaaS-based multi-omics solutions to enhance R&D productivity, reduce time-to-market, and improve the success rates of clinical trials. The growing awareness of the benefits of integrated omics analysis among hospitals, clinics, and academic research institutes is further expanding the customer base for these platforms, paving the way for sustained market growth over the forecast period.

From a regional perspective, North America continues to dominate the Multi-Omics Data Integration SaaS market, driven by the presence of leading technology providers, advanced healthcare infrastructure, and significant R&D investments. However, Asia Pacific is emerging as the fastest-growing region, fueled by expanding genomics initiatives, increasing healthcare digitalization, and rising investments in precision medicine. Europe also holds a substantial market share, supported by robust government funding and a strong focus on collaborative research networks. The Middle East & Africa and Latin America, while currently smaller in market size, are witnessing growing adoption as awareness of multi-omics integration and its clinical applications spreads.

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Data supporting for thesis titled “Large-scale data analysis and integration to advance precision prognosis, therapy stratification and understanding of human disease”

## GMarsGT: For rare cell identification from matched scRNA-seq (snRNA-seq) and scATAC-seq (snATAC-seq),includes genes, enhancers, and cells in a heterogeneous graph to simultaneously identify major cell clusters and rare cell clusters based on eRegulon.

## Data Collection The data was collected using GEO Database.

## Data Format The data is stored as TSV file and MTX file where each row represents a gene and each column represents a sample.

## Variables - Gene IDs: Gene Symbols (e.g., MALAT1) - Sample IDs: Sample identifiers (e.g., AAACATGCAAATTCGT-1) - Expression level: Row gene expression level.

The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.

Multi-Omics Data Visualization Platforms Market Outlook

According to our latest research, the multi-omics data visualization platforms market size reached USD 1.28 billion in 2024, reflecting robust momentum driven by advancements in bioinformatics and computational biology. The market is projected to grow at a compelling CAGR of 13.4% from 2025 to 2033, leading to a forecasted market size of USD 4.06 billion by 2033. This significant growth is primarily attributed to the increasing integration of multi-omics approaches in life sciences research, enabling comprehensive analysis and visualization of complex biological datasets. As per our latest research, the accelerating demand for high-throughput data analysis tools and the widespread adoption of precision medicine are key growth drivers fueling this dynamic market.

The rapid expansion of the multi-omics data visualization platforms market is fundamentally underpinned by technological advancements in sequencing and analytical tools. The evolution of next-generation sequencing (NGS), mass spectrometry, and other high-throughput omics platforms has resulted in the generation of massive and complex datasets. This, in turn, has created an urgent need for advanced visualization solutions capable of integrating, analyzing, and rendering diverse data types in a user-friendly manner. The increasing demand for holistic biological insights—spanning genomics, proteomics, transcriptomics, metabolomics, and epigenomics—necessitates platforms that can seamlessly aggregate and visually interpret multi-layered data, facilitating novel discoveries in areas such as disease mechanisms, biomarker identification, and therapeutic target validation. The convergence of artificial intelligence and machine learning with data visualization is further enhancing the analytical power and predictive capabilities of these platforms, making them indispensable for researchers and clinicians alike.

Another significant growth factor for the multi-omics data visualization platforms market is the surge in personalized medicine initiatives worldwide. Healthcare providers and life sciences organizations are increasingly leveraging multi-omics data to tailor treatments to individual patient profiles, thereby improving clinical outcomes and reducing adverse effects. This paradigm shift towards personalized healthcare is driving investments in data integration and visualization technologies that can handle the complexity and scale of multi-omics datasets. Pharmaceutical and biotechnology companies are particularly active in adopting these platforms to accelerate drug discovery and development, optimize clinical trial design, and identify patient subgroups with greater precision. As regulatory agencies emphasize data transparency and reproducibility, robust visualization tools are becoming critical for ensuring compliance and facilitating communication of research findings.

Furthermore, the growing collaboration between academic institutions, research organizations, and industry players is catalyzing the adoption of multi-omics data visualization platforms. Government funding initiatives and public-private partnerships are supporting the development of integrated bioinformatics infrastructures, fostering innovation in data analysis and visualization. The increasing prevalence of chronic diseases, such as cancer and cardiovascular disorders, is also fueling demand for comprehensive multi-omics approaches to unravel complex disease etiologies and identify novel therapeutic strategies. As the multi-omics ecosystem expands, the need for scalable, interoperable, and user-centric visualization platforms is expected to intensify, driving sustained market growth over the forecast period.

Regionally, North America continues to dominate the multi-omics data visualization platforms market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading biotechnology and pharmaceutical companies, coupled with advanced healthcare infrastructure and substantial investments in omics research, positions North America as a key growth engine. Europe is witnessing rapid adoption due to supportive government policies and a vibrant research community, while Asia Pacific is emerging as a high-growth region, propelled by increasing R&D activities and expanding healthcare expenditure. The market landscape in Latin America and the Middle East & Africa remains nascent but is expected to gain traction as awareness and access to advanced omics technologies improve.<

Multi-Omics Data Integration Platforms Market Outlook

According to our latest research, the Global Multi-Omics Data Integration Platforms market size was valued at $1.25 billion in 2024 and is projected to reach $5.67 billion by 2033, expanding at a robust CAGR of 18.7% during the forecast period of 2025–2033. The primary driver for this remarkable growth is the accelerating adoption of personalized and precision medicine, which relies heavily on the integration of diverse omics datasets—such as genomics, proteomics, transcriptomics, and metabolomics—to derive actionable insights for disease diagnosis, treatment planning, and drug development. As healthcare and life sciences organizations strive to harness the power of big data for advanced analytics, the demand for scalable, interoperable, and user-friendly multi-omics data integration platforms is expected to surge across the globe.

Regional Outlook

North America currently dominates the Multi-Omics Data Integration Platforms market, accounting for over 42% of the global revenue share in 2024. This leadership is attributed to the region’s mature healthcare infrastructure, substantial investments in life sciences research, and widespread adoption of advanced data analytics technologies. The presence of major pharmaceutical and biotechnology companies, coupled with robust collaborations between academic research institutes and industry, further fuels market growth. Additionally, favorable government policies, such as the Precision Medicine Initiative in the United States, have accelerated the integration of multi-omics data into clinical and research workflows. These factors, combined with a high concentration of skilled bioinformaticians and data scientists, have solidified North America’s position as the epicenter of innovation and commercialization in this market.

The Asia Pacific region is poised to be the fastest-growing market, with a projected CAGR of 22.4% from 2025 to 2033. This rapid expansion is driven by increasing government funding for genomics and biotechnology research, rising awareness of precision medicine, and the proliferation of next-generation sequencing technologies. Countries such as China, Japan, and South Korea are making significant investments in healthcare digitization and are establishing large-scale population genomics projects. Strategic partnerships between local academic institutions and global platform providers are also catalyzing adoption. Moreover, the growing burden of chronic diseases and an expanding base of clinical trials in the region are creating a fertile environment for the deployment of multi-omics data integration solutions.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing multi-omics data integration platforms, albeit at a slower pace due to infrastructural and regulatory challenges. The adoption rate is hampered by limited access to high-throughput sequencing technologies, a shortage of skilled professionals, and constrained healthcare budgets. However, localized demand is rising, particularly in urban centers and research hubs, where there is increasing recognition of the value of integrated omics data in improving clinical diagnostics and agricultural productivity. Policy reforms aimed at fostering innovation, coupled with international collaborations and capacity-building initiatives, are expected to gradually overcome these barriers and unlock new growth opportunities in these regions over the next decade.

Report Scope

Multi-Omics Data Visualization Platforms Market Outlook

According to our latest research, the global Multi-Omics Data Visualization Platforms market size in 2024 is estimated at USD 1.42 billion, demonstrating a robust foundation for this rapidly evolving sector. The market is expected to grow at a CAGR of 13.7% during the forecast period, reaching a projected value of USD 4.18 billion by 2033. This exceptional growth trajectory is primarily driven by the increasing integration of multi-omics technologies in biomedical research, the escalating demand for precision medicine, and the expanding applications of omics data analytics in drug discovery and clinical diagnostics. As per the latest research, industry stakeholders are investing heavily in advanced visualization tools to address the growing complexity of multi-dimensional biological datasets.

The surge in adoption of multi-omics data visualization platforms is underpinned by the exponential growth of biological data generated from high-throughput sequencing technologies. Researchers and clinicians now face the challenge of analyzing and interpreting vast, heterogeneous datasets encompassing genomics, proteomics, transcriptomics, metabolomics, and epigenomics. The need for intuitive, scalable, and interactive visualization platforms has become paramount to enable meaningful insights from these complex data layers. Furthermore, the integration of artificial intelligence and machine learning algorithms within these platforms is enhancing data interpretation, pattern recognition, and predictive analytics, thereby accelerating the pace of biomedical discoveries. The convergence of these technological advancements is fueling the widespread adoption of multi-omics data visualization platforms across the globe.

Another significant growth factor is the rapid advancement of personalized medicine and precision healthcare initiatives. Multi-omics data visualization platforms play a crucial role in translating multi-layered biological information into actionable clinical insights, supporting the development of targeted therapies and individualized treatment strategies. Pharmaceutical and biotechnology companies are leveraging these platforms to streamline drug discovery processes, identify novel biomarkers, and optimize clinical trial designs. The growing focus on patient-centric care, coupled with the increasing prevalence of chronic diseases and cancer, is amplifying the demand for comprehensive multi-omics analysis and visualization solutions. As a result, the market is witnessing increased collaborations between technology providers, research institutes, and healthcare organizations to develop next-generation visualization tools tailored for clinical and translational research.

The expansion of multi-omics data visualization platforms is also being propelled by government initiatives and funding for omics research, particularly in developed regions such as North America and Europe. Strategic investments in life sciences infrastructure, coupled with the establishment of national genomics and precision medicine programs, are fostering a conducive environment for market growth. Additionally, the rising adoption of cloud-based solutions and the proliferation of open-source visualization tools are democratizing access to advanced analytics, enabling smaller research labs and academic institutions to participate in cutting-edge multi-omics research. The global market landscape is further shaped by ongoing efforts to standardize data formats, enhance interoperability, and ensure data security and privacy, which are critical for large-scale multi-omics data integration and visualization.

From a regional perspective, North America is expected to maintain its dominant position in the multi-omics data visualization platforms market, driven by the presence of leading technology vendors, well-established research infrastructure, and favorable regulatory frameworks. Europe is anticipated to witness substantial growth, supported by collaborative research initiatives and increasing investments in precision medicine. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by expanding healthcare infrastructure, rising R&D expenditures, and growing awareness of omics technologies. Latin America and the Middle East & Africa are also poised for steady growth, albeit at a slower pace, as these regions gradually adopt advanced omics research methodologies and visualization solutions.

Multi-omics has the promise to provide a detailed molecular picture for biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimum structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to associate with a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30°C and 37°C, and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37°C.

The global multiomics market size exceeded USD 2.81 billion in 2025 and is set to expand at a CAGR of over 15.3%, surpassing USD 11.67 billion revenue by 2035, impelled by the emergence of artificial intelligence and cloud computing in multiomics data analysis in the Multiomics Market.

Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. A genome-scale metabolic network for the RAW 264.7 cell line was constructed to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation were identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. This study demonstrates that the role of metabolism in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors. This submission corresponds to the metabolomics data from this study.

Spatial Multi-Omics Data Integration Software Market Outlook

According to our latest research, the global spatial multi-omics data integration software market size reached USD 392.5 million in 2024, demonstrating robust growth fueled by increasing adoption of multi-omics technologies in biomedical research and clinical practice. The market is projected to expand at a remarkable CAGR of 13.7% during the forecast period, with the value expected to reach approximately USD 1,162.8 million by 2033. This accelerated growth is primarily driven by the surging demand for integrated data solutions to unravel complex biological mechanisms, enhance drug discovery, and enable precision medicine initiatives. As per our latest research, the marketÂ’s momentum is underpinned by technological advancements, rising R&D investments, and the growing prevalence of chronic diseases necessitating advanced diagnostic and therapeutic strategies.

One of the primary growth factors propelling the spatial multi-omics data integration software market is the increasing need for comprehensive biological insights at the cellular and tissue levels. The convergence of genomics, transcriptomics, proteomics, metabolomics, and epigenomics data enables researchers and clinicians to capture a multidimensional view of biological systems. This holistic approach is essential for understanding disease heterogeneity, tumor microenvironments, and cellular interactions, particularly in oncology and immunology. The rapid evolution of spatial omics technologies, coupled with the availability of high-throughput sequencing platforms, has generated massive datasets that require sophisticated integration and analysis tools. Consequently, the demand for advanced software solutions capable of harmonizing and interpreting complex multi-omics data is experiencing a significant uptick across both academic and industrial settings.

Another critical driver for the market is the accelerating pace of drug discovery and development, which increasingly relies on spatial multi-omics data integration to identify novel therapeutic targets and biomarkers. Pharmaceutical and biotechnology companies are leveraging these software platforms to streamline the drug development pipeline, reduce attrition rates, and personalize treatment regimens based on patient-specific molecular profiles. The integration of spatial and multi-omics data enhances the ability to predict drug responses, monitor disease progression, and assess therapeutic efficacy in real time. Furthermore, collaborations between software providers, academic institutions, and life science companies are fostering the development of user-friendly, scalable, and interoperable solutions that cater to the evolving needs of end users. This collaborative ecosystem is expected to sustain market growth by facilitating knowledge transfer, standardization, and innovation.

The rising adoption of personalized medicine and precision diagnostics is further fueling the spatial multi-omics data integration software market. As healthcare systems worldwide shift toward individualized care paradigms, there is a growing emphasis on leveraging multi-layered molecular data to inform clinical decision-making. Spatial multi-omics integration software enables clinicians to correlate genetic, transcriptomic, proteomic, and metabolic alterations with spatial context, thereby improving the accuracy of disease classification, prognosis, and therapeutic selection. This paradigm shift is particularly evident in oncology, neurology, and rare disease management, where spatially resolved molecular insights can guide targeted interventions. The increasing prevalence of chronic diseases, aging populations, and the need for early disease detection are expected to drive sustained investments in multi-omics data integration capabilities across healthcare and research institutions.

Regionally, North America continues to dominate the spatial multi-omics data integration software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of leading life science companies, advanced healthcare infrastructure, and substantial government funding for multi-omics research. Europe follows closely, benefiting from strong academic networks and growing investments in precision medicine initiatives. The Asia Pacific region is emerging as a high-growth market, driven by expanding genomics research, increasing healthcare expenditure, and rising awareness of the benefits of integrated omics analyse

Additional file 1. Horizontal integration analysis script. R script for performing horizontal integration as presented in the paper.

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

These data sets are the pre-processed versions of the multi-omics data sets used in the benchmark study presented in the paper "Benchmark study of feature selection strategies for multi-omics data" by Yingxia Li, Ulrich Mansmann, Shangming Du, and Roman Hornung. The outcome feature is "TP53_mutation" in each data set, where "1" / "0" indicates the presence / absence of a TP53 mutation in the respective patients. The remaining features are clinical and omics features, where the suffix "_clinical" indicates clinical features, the suffix "_cnv" copy number variation features, the suffix "_mirna" miRNA features, the suffix "_mutation" mutation features, and the suffix " _rna" RNA features. Note that while predicting the outcome feature TP53 yes vs. no is not meaningful contextually, TP53 mutations have been found to be associated with poor clinical outcomes in cancer patients [1]. Against this background, TP53 can be used as a surrogate for a phenotypic outcome. Thus, these data sets are meant for testing machine learning or statistical procedures, they may not be useful for biological analysis.