Additional file 2: Table S1. General information of three real datasets downloaded from TCGA. Table S2. Top 20 rules identified from BRCA mRNA dataset. Table S3. Top 20 rules identified from BRCA DNA methylation. Table S4. Top 20 rules identified from ESCA mRNA dataset. Table S5. Top 20 rules identified from ESCA DNA methylation dataset. Table S6. Top 20 rules identified from LUAD mRNA dataset. Table S7. Top 20 rules identified from LUAD DNA methylation dataset. Table S8. Top 20 rules identified from the combined BRCA mRNA and DNA methylation datasets. Table S9. Top 20 rules identified from the combined ESCA mRNA and DNA methylation datasets. Table S10. Top 20 rules identified from the combined LUAD mRNA and DNA methylation datasets.

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

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

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

Tables S5–S8. The tables highlight the cases in which INF performs similarly or better than both juxt and rSNF. Each sheet represents one classifier (sheet S5: RF; sheet S6: RF KBest; sheet S7: LSVM; sheet S8: LSVM KBest). The information is coded as follows. Bold: INF performs better than both juxt and rSNF in terms of MCC CV, MCC val and Nfeat. Black (not bold): MCC INF − MCC juxt < 0.1 and MCC INF − MCC rSNF <0.1. Light gray: failed run or INF performs worse in either CV or validation. (XLSX 15.7 kb)

Table S2. Information relative to the match of aCGH samples and RNA-Seq/microarray samples included in the study. (XLSX 9.23 kb)

The files contain supplementary tables for PhD thesis titled "Causal Gene Prioritization Across Diverse Diseases Through Multi-Omic Data Integration" by Mine Koprulu. Further details about the analyses performed can be found within the methods and results section of the relevant chapters in the thesis.

Supplementary tables for Chapter 2 mainly include quality control measures, annotations and summary statistics from the rare variant analysis for body fat distribution.

Supplementary tables for Chapter 3 mainly include results from the genomic analysis of measurements from a recent antibody-based proteomics platform and the clinical relevance of the identified pQTLs.

Supplementary tables for Chapter 4 mainly include genome-wide characterization and phenotypic consequences of protein quantitative trait loci through a large-scale international meta-analysis.

Supplementary tables for Chapter 5 mainly include summary statistics and results from systematic investigation of proteogenomic variation between sexes and its relevance for human diseases.

The integration of multi-omic data sets can provide unique information about molecular processes in a cell. Despite the development of many tools to extract information from such data sets, there are limited strategies to systematically extract mechanistic hypotheses from them. We here present COSMOS (Causal Oriented Search of Multi-Omic Space), a method that integrates cell signaling pathways, transcriptional, and metabolics data sets. COSMOS leverages extensive prior knowledge of interactions between biomolecules with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS can provide mechanistic explanations for experimental observations across multiple omic data sets. We applied COSMOS to a dataset comprising transcriptomic, phosphoproteomic, and metabolomic data from nine renal cell carcinoma patients comparing healthy non affected kidney tissue and kidney cancer. We used COSMOS to generate novel hypotheses such as the impact of CDK7 on nucleoside metabolism and its influence on citrulline production, that we validated experimentally. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omic studies.

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

Table S1. Clinical characteristics of the patients included in the study. (XLSX 27.9 kb)

Glioblastoma multiforme (GBM) is one of the most aggressive types of cancer and exhibits profound genetic and epigenetic heterogeneity, making the development of an effective treatment a major challenge. The recent incorporation of molecular features into the diagnosis of GBM patients has led to an improved categorisation into various tumour subtypes with different prognoses and disease management. In this work, we have exploited the benefits of genome-wide multi-omic approaches to identify potential molecular vulnerabilities existing in GBM patients. Integration of gene expression and DNA methylation data from both bulk GBM and patient-derived GBM stem cell lines has revealed the presence of major sources of GBM variability, pinpointing subtype-specific tumour vulnerabilities amenable to pharmacological interventions. In this sense, inhibition of the AP1, SMAD3 and RUNX1 / RUNX2 pathways, in combination or not with the chemotherapeutic agent temozolomide, led to the subtype-specific impairment of tumour growth, particularly in the context of the aggressive, mesenchymal-like subtype. These results emphasize the involvement of these molecular pathways in the development of GBM and have potential implications for the development of personalized therapeutic approaches.

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.

Global Multi-omics Data Integration AI Market is segmented by Application (Healthcare_Pharmaceuticals_IT_Biotechnology_Research & Development), Type (AI-Powered Multi-omics Analysis_Personalized Medicine_Data-Driven Biomarker Discovery_Gene Expression Modeling_Clinical Data Integration), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.

Table S9. The table provides the assignment of the 145 subset patients into the two groups G1 and G2 identified with the deep learning approach and characterized by distinct survival curves. For clarity we report also the TR/TS assignment. (XLSX 7.46 kb)

Triple negative breast cancer is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with normal epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFbeta signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling.

Multi-Omics Market Outlook

According to our latest research, the global multi-omics market size was valued at USD 4.82 billion in 2024, reflecting a robust surge in adoption across various life sciences sectors. The market is experiencing a strong growth momentum, driven by technological advancements and expanding clinical applications. The market is forecasted to reach USD 17.6 billion by 2033, growing at a remarkable CAGR of 15.5% during the period from 2025 to 2033. This rapid growth is attributed to the increasing integration of multi-omics approaches in drug discovery, diagnostics, and personalized medicine, as well as the rising prevalence of complex diseases that require comprehensive molecular profiling.

The growth of the multi-omics market is significantly propelled by the convergence of high-throughput technologies and the growing demand for holistic biological insights. Multi-omics integrates genomics, proteomics, transcriptomics, metabolomics, and epigenomics, enabling researchers to unravel the complexities of biological systems at multiple molecular levels. This comprehensive approach is particularly crucial for understanding multifactorial diseases such as cancer, diabetes, and neurodegenerative disorders. As pharmaceutical and biotechnology companies intensify their focus on precision medicine and targeted therapies, the demand for multi-omics solutions continues to rise. Additionally, the decreasing costs of sequencing and mass spectrometry technologies have made multi-omics analyses more accessible to a broader range of research institutions and clinical laboratories, further accelerating market expansion.

Another pivotal growth factor is the increasing investment in multi-omics research by governments and private organizations worldwide. Strategic funding initiatives and public-private partnerships are fostering the development of advanced bioinformatics tools and analytical platforms that can efficiently handle the vast and complex datasets generated by multi-omics studies. The application of artificial intelligence and machine learning in data integration and interpretation is also enhancing the value proposition of multi-omics, enabling more accurate biomarker discovery and disease pathway elucidation. Furthermore, the growing trend of collaborative research projects and the establishment of multi-omics consortia are fostering innovation and accelerating the translation of omics research into clinical and agricultural applications.

The expanding application of multi-omics in personalized medicine is a major catalyst for market growth. Personalized medicine relies on detailed molecular profiling to tailor treatment strategies to individual patient characteristics, and multi-omics provides the comprehensive data required for such customization. Hospitals and clinics are increasingly adopting multi-omics approaches for diagnostic and prognostic purposes, particularly in oncology and rare genetic diseases. This shift is supported by regulatory agencies that are updating guidelines to incorporate multi-omics data in clinical decision-making. The integration of multi-omics into routine clinical practice is expected to drive substantial demand for advanced analytical platforms and bioinformatics solutions, further fueling market growth through 2033.

The advent of Multi-Omics Data Integration SaaS solutions is revolutionizing the way researchers and clinicians approach complex biological data. These Software as a Service platforms offer seamless integration and analysis of diverse omics datasets, providing a unified view of molecular interactions and pathways. By leveraging cloud-based infrastructures, Multi-Omics Data Integration SaaS enables real-time data processing and collaboration across geographical boundaries, enhancing the efficiency and scalability of multi-omics research. This technological advancement is particularly beneficial for institutions with limited computational resources, as it reduces the need for extensive in-house infrastructure. As a result, more organizations are adopting these SaaS solutions to accelerate their research and development processes, ultimately contributing to the rapid growth of the multi-omics market.

From a regional perspective, North America currently dominates the multi-omics market, owing to its advanced healthcare infrastructure, significant researc

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.

The uploaded tar file contains anonymized and reduced test data for the paper "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset". (doi: https://doi.org/10.1101/2024.06.11.598306; https://github.com/mwess/miit)

Dataset description:
- 9 serial histology sections with the following stains: (HES, HE, HES, HES, HES, MTS, IHC, IHC, HES)
- Sections are indexed in the following way (due to some sections not being part of this project): 1,2,3,6,7,8,9,10,11
- Each serial section contains:
- landmarks with matching labels across all sections.
- semi-manually generated tissue masks
- Section 2 contain spatial transcriptomics data.
- Sections 6 and 7 contain imzml data that were generated with MALDI-MSI in positive ion mode (section 6) and negative ion mode (section 7) and additional histology annotations.
- MALDI-MSI is reduced. The positive ion data contains only intensities and spectra for spermine. The negative ion mode data contains only intensities and spectra for citrate and zinc.
- ST data contains only locations of spots and scalefactors. (I.e. no count data is included.). Barcode ids are randomly generated.
- In addition, for each ST spot histopathological annotations and GSEA scores for the Citrate-Spermine Secretion gene signature are provided.

Abbreviations:

- HES = Hematoxylin-Erythrosine-Saffron
- HE = Hematoxylin-Eosin
- MTS = Masson's Trichrome Staining
- IHC = Immunohistochemistry
- ST = Spatial Transcriptomics, here refers to Visium10X arrays.
- MSI = Mass Spectrometry Imaging.