The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide range of omic data analyses specifically tailored to biomedical research. These include genome and epigenome wide association studies and differential gene expression analyses. OmicSHIELD is designed to support both meta- and mega-analysis, so that it offers a wide range of capabilities for different analysis designs. We present a series of use cases illustrating some examples of how the software addresses real-world analyses of omic data.

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.

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.

Some datasets for the SAOD (Statistical Analysis of Omics Data) course (Aix-Marseille Université, D. Puthier). The Homo_sapiens.GRCh38.110.chr.tsv was produced using the following command: gtftk retrieve -r 110 gtftk convert_ensembl -i Homo_sapiens.GRCh38.110.chr.gtf.gz | gtftk nb_exons | gtftk feature_size -t mature_rna | gtftk feature_size -t transcript -k tx_genomic_size | gtftk exon_sizes | gtftk intron_sizes | gtftk select_by_key -t | gtftk tabulate -k '*' -u -x > Homo_sapiens.GRCh38.110.chr.tsv

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.

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.1

Multi-Omics Data Integration SaaS Market Outlook

According to the latest research, the global Multi-Omics Data Integration SaaS market size reached USD 1.42 billion in 2024, reflecting a robust momentum driven by technological advancements and increasing adoption across life sciences. The market is expected to expand at a CAGR of 17.6% during the forecast period, with projections indicating a value of USD 6.13 billion by 2033. This remarkable growth is primarily fueled by the rising demand for integrated omics solutions in drug discovery, precision medicine, and clinical diagnostics, as organizations seek to leverage data-driven insights for improved outcomes and operational efficiencies.

A key driver behind the expansion of the Multi-Omics Data Integration SaaS market is the surging volume and complexity of biological data generated through next-generation sequencing (NGS) and high-throughput omics technologies. Researchers and clinical practitioners are increasingly reliant on advanced SaaS platforms to unify genomics, proteomics, transcriptomics, and metabolomics data for comprehensive analysis. The integration of these diverse datasets enables a holistic understanding of biological systems, facilitating breakthroughs in disease characterization, biomarker discovery, and therapeutic target identification. As the need for cross-omics data analysis intensifies, SaaS-based solutions offer scalable, flexible, and cost-effective approaches, eliminating the constraints of traditional on-premises infrastructures.

Another significant growth factor is the ongoing digital transformation in healthcare and life sciences, which has accelerated the adoption of cloud-based platforms for data management and analytics. SaaS solutions for multi-omics data integration provide seamless collaboration, secure data sharing, and real-time analytics, empowering interdisciplinary teams to drive innovation at scale. The COVID-19 pandemic further underscored the importance of rapid data integration and remote accessibility, catalyzing investments in digital infrastructure and cloud-native applications. As regulatory frameworks evolve to support data privacy and interoperability, organizations are increasingly confident in leveraging SaaS platforms for sensitive multi-omics research and clinical workflows.

The emergence of artificial intelligence (AI) and machine learning (ML) technologies is also transforming the Multi-Omics Data Integration SaaS market. By harnessing advanced algorithms, SaaS platforms can automate complex data integration, normalization, and interpretation tasks, uncovering hidden patterns and actionable insights from vast multi-omics datasets. This capability is particularly valuable in precision medicine, where individualized patient profiles require sophisticated analytics to inform diagnosis, prognosis, and treatment selection. As AI-powered multi-omics platforms become more accessible and user-friendly, their adoption is expected to proliferate across academic, clinical, and commercial settings, further propelling market growth.

From a regional perspective, North America currently dominates the global Multi-Omics Data Integration SaaS market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s advanced healthcare infrastructure, significant R&D investments, and a strong presence of leading SaaS providers. Europe and Asia Pacific are also experiencing rapid growth, driven by expanding genomics research initiatives, government funding, and increasing collaborations between academic institutions and industry stakeholders. As emerging markets in Latin America and the Middle East & Africa invest in digital health infrastructure, the global footprint of multi-omics SaaS solutions is expected to broaden, fostering greater accessibility and innovation worldwide.

Component Analysis

The Component segment of the Multi-Omics Data Integration SaaS market is bifurcated into software and services, each playing a pivotal role in enabling seamless integration and analysis of multi-omics datasets. Software solutions form the backbone of this segment, offering robust platforms for data ingestion, harmonization, visualization, and advanced analytics. These solutions are designed to handle the complexity and heterogeneity of omics data, providing researchers with intuitive interfaces and customizable workflows. The increasing sophistication of analytical tools, including AI-

Cloud-Based Multi-Omics Data Warehouse Market Outlook

According to our latest research, the global market size for the Cloud-Based Multi-Omics Data Warehouse Market reached USD 2.47 billion in 2024. The market is witnessing a robust expansion, registering a CAGR of 18.2% from 2025 to 2033, and is forecasted to achieve a value of USD 12.55 billion by 2033. This remarkable growth is primarily driven by the escalating adoption of cloud technologies in life sciences and healthcare, combined with the surging demand for integrated omics data analysis to accelerate drug discovery and personalized medicine initiatives.

The rapid proliferation of high-throughput sequencing technologies and the exponential rise in multi-omics data generation are pivotal growth factors for the Cloud-Based Multi-Omics Data Warehouse Market. As research organizations and healthcare providers increasingly focus on precision medicine, the need for scalable, secure, and interoperable platforms to store, manage, and analyze diverse datasets is more critical than ever. Cloud-based solutions offer unparalleled scalability and computational power, enabling seamless integration and real-time analysis of genomics, proteomics, transcriptomics, metabolomics, and epigenomics data. This capability is essential for uncovering novel biomarkers, understanding disease mechanisms, and tailoring therapeutic interventions, thereby fueling market expansion.

Another significant driver is the growing collaboration between pharmaceutical companies, academic institutions, and technology providers to develop advanced analytics platforms. These partnerships are fostering the development of comprehensive multi-omics data warehouses that support artificial intelligence (AI) and machine learning (ML) algorithms for predictive analytics and hypothesis generation. The increasing emphasis on reducing time-to-market for new drugs and improving clinical outcomes is compelling stakeholders to invest in cloud-based multi-omics solutions. Additionally, the adoption of regulatory-compliant cloud infrastructures is mitigating concerns related to data privacy and security, further accelerating market adoption across regulated sectors such as healthcare and pharmaceuticals.

The market is also benefiting from the rising prevalence of chronic diseases and the subsequent demand for personalized healthcare solutions. Multi-omics data integration enables clinicians to make informed decisions regarding disease diagnosis, prognosis, and treatment selection. Cloud-based platforms facilitate the aggregation and harmonization of large-scale omics datasets from diverse sources, supporting translational research and clinical applications. Furthermore, advancements in data interoperability standards and API-driven architectures are enhancing the accessibility and usability of multi-omics data warehouses, making them indispensable tools for researchers and clinicians alike.

Regionally, North America continues to dominate the Cloud-Based Multi-Omics Data Warehouse Market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of leading biotechnology firms, robust healthcare infrastructure, and significant investments in genomics research are key contributors to North America's leadership. Europe is witnessing steady growth owing to supportive regulatory frameworks and increasing funding for omics research. Meanwhile, Asia Pacific is emerging as a lucrative market, driven by expanding healthcare digitization, government initiatives to promote precision medicine, and rising adoption of cloud computing in research and clinical settings.

Component Analysis

The Cloud-Based Multi-Omics Data Warehouse Market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses platforms and tools designed for data integration, management, analysis, and visualization of multi-omics datasets. These solutions are engineered to

The biomarkers for thyroid cancer are still not known properly. For treating thyroid cancer these biomarkers can by be targeted specifically. Through this project, we identified and used bioinformatics tools to find biomarkers associated with thyroid cancer. Gene Expression Omnibus database (GEO) was used to find dataset related with thyroid cancer. Their expression profiles were downloaded. Four dataset GSE3467, GSE3678, GSE33630, and GSE53157 were identified from GEO database. The dataset GSE3467 contains nine thyroid tumor samples and nine normal thyroid tissue samples. The GSE3678 contains seven thyroid tumor samples and seven normal thyroid tissue samples. The GSE53157 contains twenty four thyroid tumor samples and three normal thyroid samples. The GSE33630 contains sixty thyroid tumor samples and forty five normal thyroid samples. These four datasets were analyzed individually and were integrated at the end to find the common genes among these four datasets. The microarray analysis of the datasets were performed using excel. T.Test analysis were performed for all the four datasets individually on a separate excel sheet. The data was normalized by converting normal value into log scale. Differential expression analysis of all the four datasets were done to identify differentially expresses genes (DEGs). Only upregulated genes were taken into account. Principal component analysis (PCA) of all the four dataset were performed using the raw data. The PCA analysis were performed using T-BioInfo server and the scatterplots were prepared using excel. RStudio was used to match the gene symbols with the corresponding probe ids using left join function. Inner join function in R was used to find integrated genes between the four datasets. Heatmaps of all the four datasets were performed using RStudio. To find number of intersection of Differentially expressed genes, an upset plot was prepared using RStudio. 74 genes with their corresponding probe ids were found to be common among all the four datasets. These genes are common to at least two datasets. These 74 common genes were analyzed using Database for Annotation, Visualization, and Integrated Discovery (DAVID), to study their Gene onotology (GO) functional annotations and pathways. According to the GO functional annotations result, most of the integrated upregulated genes were involved in protein binding, plasma membrane and integral component of membrane. Most common pathway include Extracellular matrix organization, Neutrophil degranulation, TGF-beta signaling pathway and Epithelial to mesenchymal transition in colorectal cancer. These 74 genes were introduced to STRING database to find protein-protein interactions between the genes. Interactions between the nodes were downloaded from STRING database and introduced to Sytoscape. Sytoscape analysis explained that only 19 genes showed protein-protein interactions between each other. Disease free survival analysis of the 13 genes that were common to three datasets were done using GEPIA. Boxplots of these 13 genes were also prepared using GEPIA. This showed that these differentially expressed genes showed different expression in normal thyroid tissue and thyroid tumor samples. Hence these 13 genes common to 3 datasets can be used as potential biomarkers for thyroid cancer. Among these 13 genes, four genes are implicated in cancer/cell proliferation can be probable target for treatment options.

Metabolomics and proteomics generate large, complex datasets that reflect the state of a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through integration of these data remains challenging. Here we show that connections between these omic layers can be discovered through a combination of machine learning and model interpretation. We find that SHAP values connecting proteins to metabolites are valid experimentally, and reveal also largely new connections. Further, clustering the magnitudes of protein control over all metabolites enabled prediction of gene five gene functions, each of which was validated experimentally. We accurately predicted that two uncharacterized genes in yeast modulate mitochondrial translation, YJR120W and YLD157C.We also predict and validate functions for several incompletely characterized genes, including SDH9, ISC1, and FMP52. Our work demonstrates that multi-omic analysis with machine learning (MIMaL) is a new lens that reveals new insight from multi-omic data that would not be possible using any omic layer alone.

The human single-cell multi-omics market is experiencing significant growth, driven by advancements in technologies enabling simultaneous analysis of multiple omics data types (genomics, transcriptomics, proteomics, etc.) from individual cells. This allows researchers to gain unprecedented insights into cellular heterogeneity and complex biological processes, fueling applications across diverse fields like drug discovery, disease diagnostics, and personalized medicine. The market's expansion is further propelled by increasing research funding in single-cell technologies, coupled with the growing adoption of these techniques in academic and pharmaceutical settings. A projected Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033 suggests a substantial market expansion over the forecast period. This robust growth, however, faces some challenges. High initial investment costs for equipment and specialized expertise remain a barrier for entry for some laboratories. Furthermore, the complexity of data analysis and interpretation requires specialized bioinformatics expertise and robust computational infrastructure. Despite these restraints, the potential of single-cell multi-omics to revolutionize biological research and clinical applications is driving substantial investment and innovation, paving the way for wider accessibility and adoption in the coming years. While the market size for 2025 is not explicitly provided, we can reasonably estimate it based on the mentioned CAGR and the typical growth trajectory of emerging technologies in the life sciences sector. Considering similar markets and their growth rates, a starting market size of approximately $500 million in 2025 appears plausible. This figure, coupled with a 15% CAGR, projects substantial growth over the next decade, leading to a market exceeding $2 billion by 2033. Key players like 10x Genomics, Illumina (implied by market trends), and Fluidigm Corporation are significantly contributing to this growth by constantly developing innovative technologies and expanding their product portfolios. The strategic partnerships between these companies and research institutions further accelerate the market expansion. The regional distribution is likely to be skewed towards North America and Europe initially, but expanding rapidly into Asia-Pacific and other regions as the technology matures and becomes more cost-effective.

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 market’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 analyses.

## 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.

Single Cell Multi-Omics Market Outlook

As of 2023, the global single cell multi-omics market size is valued at approximately USD 2.5 billion, with a robust projected CAGR of 20.1% forecasted to propel the market to USD 9.8 billion by 2032. This remarkable growth is driven by several key factors, including technological advancements in single-cell analysis techniques, increased funding for omics research, and a growing emphasis on personalized medicine. The market is experiencing a surge in demand as researchers and healthcare providers seek more precise and comprehensive insights into cellular behavior, disease mechanisms, and therapeutic responses. The integration of multi-omics data at a single-cell level offers unparalleled resolution and depth, enabling a transformative understanding of complex biological systems.

One of the primary growth drivers of the single cell multi-omics market is the rapid advancement of technology, particularly in sequencing and analytical tools. Innovations in microfluidics, next-generation sequencing, and enhanced bioinformatics capabilities have significantly lowered the cost and increased the efficiency of single-cell analysis. These technological advancements allow researchers to dissect the heterogeneity of cellular populations with unprecedented precision, facilitating breakthroughs in understanding disease pathology and developing targeted therapeutics. Moreover, the continuous evolution of these technologies fosters their adoption across various fields, further expanding the market's scope and application.

Another significant factor contributing to market growth is the escalating demand for personalized medicine. As the healthcare industry shifts towards more individualized treatment approaches, the need for comprehensive insights at a cellular level becomes paramount. Single cell multi-omics provides a holistic view of cellular function by integrating genomic, transcriptomic, proteomic, and metabolomic data. This integrated approach not only enhances the understanding of disease mechanisms but also aids in the development of personalized therapeutic strategies, thereby driving the adoption of single cell multi-omics in clinical settings. The ability to tailor treatments based on unique cellular profiles is expected to significantly boost market demand over the forecast period.

Additionally, increasing funding and investments in life sciences research is acting as a catalyst for the growth of the single cell multi-omics market. Governments, academic institutions, and private entities are investing heavily in omics research to unlock new scientific insights and address pressing healthcare challenges. This influx of funding is facilitating the establishment of state-of-the-art research facilities and fostering collaborations between academic institutions and industry players. The enhanced research infrastructure and collaborative efforts are expected to accelerate scientific discoveries and propel the market's expansion, as researchers strive to unravel the complexities of biological systems at a single-cell level.

From a regional perspective, North America currently dominates the single cell multi-omics market, owing to its robust research infrastructure, presence of leading biotechnology firms, and substantial government funding for genomics and precision medicine initiatives. However, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, driven by increasing investments in healthcare research, the rising prevalence of chronic diseases, and the burgeoning biotechnology sector. European countries are also witnessing a growing adoption of single cell multi-omics technologies, supported by collaborative research initiatives and favorable regulatory frameworks. These regional dynamics underscore the diverse growth opportunities within the global market, as stakeholders capitalize on regional strengths and address specific healthcare needs.

Technology Analysis

The technology segment within the single cell multi-omics market is predominantly categorized into single cell genomics, single cell transcriptomics, single cell proteomics, and single cell metabolomics. Each of these sub-segments plays a crucial role in providing comprehensive insights into cellular functions and interactions. Single cell genomics, which involves the analysis of DNA at a single-cell level, has become a cornerstone technology in this market. It enables researchers to investigate genetic variations, mutations, and chromosomal aberrations with unprecedented accuracy. This technology is pivotal in advancing our understanding of genetic predisposit

Pathway Multi-Omics Simulated Data

These are synthetic variations of the TCGA COADREAD data set (original data available at http://linkedomics.org/data_download/TCGA-COADREAD/). This data set is used as a comprehensive benchmark data set to compare multi-omics tools in the manuscript "pathwayMultiomics: An R package for efficient integrative analysis of multi-omics datasets with matched or un-matched samples".

There are 100 sets (stored as 100 sub-folders, the first 50 in "pt1" and the second 50 in "pt2") of random modifications to centred and scaled copy number, gene expression, and proteomics data saved as compressed data files for the R programming language. These data sets are stored in subfolders labelled "sim001", "sim002", ..., "sim100". Each folder contains the following contents: 1) "indicatorMatricesXXX_ls.RDS" is a list of simple triplet matrices showing which genes (in which pathways) and which samples received the synthetic treatment (where XXX is the simulation run label: 001, 002, ...), (2) "CNV_partitionA_deltaB.RDS" is the synthetically modified copy number variation data (where A represents the proportion of genes in each gene set to receive the synthetic treatment [partition 1 is 20%, 2 is 40%, 3 is 60% and 4 is 80%] and B is the signal strength in units of standard deviations), (3) "RNAseq_partitionA_deltaB.RDS" is the synthetically modified gene expression data (same parameter legend as CNV), and (4) "Prot_partitionA_deltaB.RDS" is the synthetically modified protein expression data (same parameter legend as CNV).

Supplemental Files

The file "cluster_pathway_collection_20201117.gmt" is the collection of gene sets used for the simulation study in Gene Matrix Transpose format. Scripts to create and analyze these data sets available at: https://github.com/TransBioInfoLab/pathwayMultiomics_manuscript_supplement

These tissue-level multi-omic graphical analysis reports are provided as additional data for the manuscript “Temporal dynamics of the multi-omic response to endurance exercise training across tissues” (MoTrPAC Study Group, bioRxiv, 2022). Find the preprint here. Extensive background is included in each report. Briefly, we used a graphical clustering approach to define and visualize the temporal dynamics of molecular analytes regulated by endurance exercise training at multiple training time points in male and female rats across many data types ("omes") and tissues. The objective of these multi-omic reports is to share representations of >34,000 training-regulated molecular features in interactive HTML reports that allow researchers to extract meaningful biology from a complex dataset. Each report presents a summary of the significantly training-regulated features across omes in a specific tissue and the corresponding graphical analysis results, as well as features and pathway enrichment results corresponding to the largest graphical clusters (nodes, edges, and paths) for that tissue. A graphical cluster is a group of training-regulated features that share temporal behavior at some point during the training time course. These multi-omic reports are generated using data and functions available through the MotrpacRatTraining6mo R package. Install this R package to explore the data yourself! Get started with this tutorial. {"references": ["Ignatiadis N, Klaus B, Zaugg JB, Huber W. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods. 2016 Jul;13(7):577-80. doi: 10.1038/nmeth.3885. Epub 2016 May 30. PMID: 27240256; PMCID: PMC4930141.", "Heller R, Yaacoby S, Yekutieli D. repfdr: a tool for replicability analysis for genome-wide association studies. Bioinformatics. 2014 Oct 15;30(20):2971-2. doi: 10.1093/bioinformatics/btu434. Epub 2014 Jul 9. PMID: 25012182.", "Almende B.V. and Contributors, Thieurmel B (2022). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.1.2, https://CRAN.R-project.org/package=visNetwork.", "Gay N, Amar D, Jean Beltran P, MoTrPAC Study Group (2022). MotrpacRatTraining6mo: Analysis of the MoTrPAC endurance exercise training data in 6-month-old rats. R package version 1.5.2, https://motrpac.github.io/MotrpacRatTraining6mo/."]}

This repository contains the input and output files necessary to reproduce the case studies reported in the manuscript "MUUMI: an R package for statistical and network-based meta-analysis for MUlti-omics data Integration". MUUMI is an R package implementing network-based data integration and statistical meta-analysis within a single analytical framework. MUUMI allows the identification of robust molecular signatures through multiple meta-analytic methods, inference and analysis of molecular interactomes and the integration of multiple omics layers. The functionalities of MUUMI are showcased in two case studies in which we analysed 17 transcriptomic datasets on idiopathic pulmonary fibrosis (IPF) from both microarray and RNA-Seq platforms and multi-omics data of THP-1 macrophages exposed to different polarising stimuli. Part of the data reported in this repository derive from the Zenodo entry https://doi.org/10.5281/zenodo.10692129 (Curated and harmonised transcriptomics datasets of interstitial lung disease patients). Other data derive from the following publication: Migliaccio G, Morikka J, del Giudice G, Vaani M, Möbus L, Serra A, Federico A, Greco D. Methylation and transcriptomic profiling reveals short term and long term regulatory responses in polarized macrophages, Comp and Struct Biotech J, 2024(25), 143-152. doi: 10.1016/j.csbj.2024.08.018.

We report here the multi-omics analysis of esthesioneuroblastomas, which is a rare cancer arising from the olfactory bulb. We discovered ENB can be grouped in two subtypes, namely basal and neural ENBs. We also uncovered high rates of mutations (~35%) in basal ENB subgroup. We report here the multi-omics analysis of esthesioneuroblastomas using whole-exome sequencing (n=27), RNA sequencing (n=19) and DNA methylation profiling (n=26).

We present an update of the MaxQuant software for isobaric labeling data and evaluate its performance on benchmark datasets. Impurity correction factors can be applied to labels mixing C- and N-type reporter ions, such as TMT Pro. Application to a single-cell species mixture benchmark shows high accuracy of the impurity-corrected results. TMT data recorded with FAIMS separation can be analyzed directly in MaxQuant without splitting the raw data into separate files per FAIMS voltage. Weighted median normalization, is applied to several datasets, including large-scale human body atlas data. In the benchmark datasets the weighted median normalization either removes or strongly reduces the batch effects between different TMT plexes and results in clustering by biology. In datasets including a reference channel, we find that weighted median normalization performs as well or better when the reference channel is ignored and only the sample channel intensities are used, suggesting that the measurement of a reference channel is unnecessary when using weighted median normalization in MaxQuant. We demonstrate that MaxQuant including the weighted median normalization performs well on multi-notch MS3 data, as well as on phosphorylation data.

Data Summary: Each folder contains MaxQuant output tables used for data analysis with their respectively mqpar files. Please use the MaxQuant version specified in each dataset to open mqpar files. Perseus sessions are provided when Perseus was used for downstream analyses. Please use Perseus version Perseus version 2.1.2 to load the sessions.

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.