Integrative Analysis of Omics Big Data

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Opportunities and challenges of 5G network technology toward precision medicine

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

Molecular signature of neutrophil extracellular trap mediating disease module in idiopathic inflammatory myopathy

The rarity and heterogeneity of idiopathic inflammatory myopathy (IIM) pose challenges for researching IIM in affected individuals. We analyzed integrated transcriptomic datasets obtained using muscle tissues from patients with five distinct IIM subtypes to investigate the shared and distinctive cellular and molecular characteristics. A transcriptomic dataset of muscle tissues from normal controls (n = 105) and patients with dermatomyositis (n = 89), polymyositis (n = 33), inclusion body myositis (n = 121), immune-mediated necrotizing myositis (n = 75), and anti-synthetase syndrome (n = 18) was used for differential gene-expression analysis, functional-enrichment analysis, gene set-enrichment analysis, disease-module identification, and kernel-based diffusion scoring. Damage-associated molecular pattern-associated pathways and neutrophil-mediated immunity were significantly enriched across different IIM subtypes, although their activities varied. Interferons-signaling pathways were differentially activated across all five IIM subtypes. In particular, neutrophil extracellular trap (NET) formation was significantly activated and correlated with Fcγ R-mediated signaling pathways. NET formation-associated genes were key for establishing disease modules, and FCGRs, C1QA, and SERPINE1 markedly perturbed the disease modules. Integrated transcriptomic analysis of muscle tissues identified NETs as key components of neutrophil-mediated immunity involved in the pathogenesis of IIM subtypes and, thus, has therapeutically targetable value.

Multi-omics of extracellular vesicles: An integrative representation of functional mediators and perspectives on lung disease study

Extracellular vesicles are secreted by almost all cell types. EVs include a broader component known as exosomes that participate in cell-cell and tissue-tissue communication via carrying diverse biological signals from one cell type or tissue to another. EVs play roles as communication messengers of the intercellular network to mediate different physiological activities or pathological changes. In particular, most EVs are natural carriers of functional cargo such as DNA, RNA, and proteins, and thus they are relevant to advancing personalized targeted therapies in clinical practice. For the application of EVs, novel bioinformatic models and methods based on high-throughput technologies and multi-omics data are required to provide a deeper understanding of their biological and biomedical characteristics. These include qualitative and quantitative representation for identifying cargo markers, local cellular communication inference for tracing the origin and production of EVs, and distant organ communication reconstruction for targeting the influential microenvironment and transferable activators. Thus, this perspective paper introduces EVs in the context of multi-omics and provides an integrative bioinformatic viewpoint of the state of current research on EVs and their applications.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

A precision medicine approach to stress testing using metabolomics and microribonucleic acids

Both transcriptomics and metabolomics hold promise for identifying acute coronary syndrome (ACS) but they have not been used in combination, nor have dynamic changes in levels been assessed as a diagnostic tool. We assessed integrated analysis of peripheral blood miRNA and metabolite analytes to distinguish patients with myocardial ischemia on cardiac stress testing. We isolated and quantified miRNA and metabolites before and after stress testing from seven patients with myocardial ischemia and 1:1 matched controls. The combined miRNA and metabolomic data were analyzed jointly in a supervised, dimension-reducing discriminant analysis. We implemented a baseline model (T0) and a stress-delta model. This novel integrative analysis of the baseline levels of metabolites and miRNA expression showed modest performance for distinguishing cases from controls. The stress-delta model showed worse performance. This pilot study shows potential for an integrated precision medicine approach to cardiac stress testing.

Deep latent space fusion for adaptive representation of heterogeneous multi-omics data

The integration of multi-omics data makes it possible to understand complex biological organisms at the system level. Numerous integration approaches have been developed by assuming a common underlying data space. Due to the noise and heterogeneity of biological data, the performance of these approaches is greatly affected. In this work, we propose a novel deep neural network architecture, named Deep Latent Space Fusion (DLSF), which integrates the multi-omics data by learning consistent manifold in the sample latent space for disease subtypes identification. DLSF is built upon a cycle autoencoder with a shared self-expressive layer, which can naturally and adaptively merge nonlinear features at each omics level into one unified sample manifold and produce adaptive representation of heterogeneous samples at the multi-omics level. We have assessed DLSF on various biological and biomedical datasets to validate its effectiveness. DLSF can efficiently and accurately capture the intrinsic manifold of the sample structures or sample clusters compared with other state-of-the-art methods, and DLSF yielded more significant outcomes for biological significance, survival prognosis and clinical relevance in application of cancer study in The Cancer Genome Atlas. Notably, as a deep case study, we determined a new molecular subtype of kidney renal clear cell carcinoma that may benefit immunotherapy in the viewpoint of multi-omics, and we further found potential subtype-specific biomarkers from multiple omics data, which were validated by independent datasets. In addition, we applied DLSF to identify potential therapeutic agents of different molecular subtypes of chronic lymphocytic leukemia, demonstrating the scalability of DLSF in diverse omics data types and application scenarios.

Tools in Pharmacogenomics Biomarker Identification for Cancer Patients

The understanding of the biological differences which underlie the inter-individual variability in drug response improved the efficacy of cancer therapy in the era of precision medicine. In fact molecularly targeted drugs and immunotherapy represent a revolution in cancer treatment. The identification of genetic predictive and/or prognostic biomarkers linked to drug pharmacokinetics (PK) and pharmacodynamics (PD) is allowed by the development of high-throughput omics tools for detecting and understanding biological differences among individuals, in order to improve drug efficacy and minimize risk of toxicity. Personalized medicine in cancer treatment reduces costs of the healthcare system. Unfortunately, pharmacogenomics biomarkers discovery is influenced by complexity, need of high-quality evidence, and a validation process for regulatory purposes. This chapter is focused on the critic analysis of presently available pharmacogenomics tools for discovering or testing genetic polymorphic variants in drug metabolizing enzyme to be introduced in clinical practice for the prospective stratification of cancer patients.

Multi-Omics for the Understanding of Brain Diseases

Brain diseases, including both neurodegenerative diseases and mental disorders, represent the third largest healthcare problem in developed countries, after cardiovascular disorders and cancer [...].

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Perspectives in systems nephrology

Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.

Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review

For a long time, gut bacteria have been recognized for their important roles in the occurrence and progression of gastrointestinal diseases like colorectal cancer, and the ever-increasing amounts of microbiome data combined with other high-quality clinical and imaging datasets are leading the study of gastrointestinal diseases into an era of biomedical big data. The "omics" technologies used for microbiome analysis continuously evolve, and the machine learning or artificial intelligence technologies are key to extract the relevant information from microbiome data. This review intends to provide a focused summary of recent research and applications of microbiome big data and to discuss the use of artificial intelligence to combat gastrointestinal diseases.

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.

Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identifies Candidate Genes Associated With Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a type of scarring lung disease characterized by a chronic, progressive, and irreversible decline in lung function. The genetic basis of IPF remains elusive. A transcriptome-wide association study (TWAS) of IPF was performed by FUSION using gene expression weights of three tissues combined with a large-scale genome-wide association study (GWAS) dataset, totally involving 2,668 IPF cases and 8,591 controls. Significant genes identified by TWAS were then subjected to gene ontology (GO) and pathway enrichment analysis. The overlapped GO terms and pathways between enrichment analysis of TWAS significant genes and differentially expressed genes (DEGs) from the genome-wide mRNA expression profiling of IPF were also identified. For TWAS significant genes, protein–protein interaction (PPI) network and clustering modules analyses were further conducted using STRING and Cytoscape. Overall, TWAS identified a group of candidate genes for IPF under the Bonferroni corrected P value threshold (0.05/14929 = 3.35 × 10^–6), such as DSP (P_TWAS = 1.35 × 10^–29 for lung tissue), MUC5B (P_TWAS = 1.09 × 10^–28 for lung tissue), and TOLLIP (P_TWAS = 1.41 × 10^–15 for whole blood). Pathway enrichment analysis identified multiple candidate pathways, such as herpes simplex infection (P value = 7.93 × 10^–5) and antigen processing and presentation (P value = 6.55 × 10^–5). 38 common GO terms and 8 KEGG pathways shared by enrichment analysis of TWAS significant genes and DEGs were identified. In the PPI network, 14 genes (DYNLL1, DYNC1LI1, DYNLL2, HLA-DRB5, HLA-DPB1, HLA-DQB2, HLA-DQA2, HLA-DQB1, HLA-DRB1, POLR2L, CENPP, CENPK, NUP133, and NUP107) were simultaneously detected by hub gene and module analysis. In conclusion, through integrative analysis of TWAS and mRNA expression profiles, we identified multiple novel candidate genes, GO terms and pathways for IPF, which contributes to the understanding of the genetic mechanism of IPF.

From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating and fatal neurodegenerative disorder, caused by the degeneration of upper and lower motor neurons for which there is no truly effective cure. The lack of successful treatments can be well explained by the complex and heterogeneous nature of ALS, with patients displaying widely distinct clinical features and progression patterns, and distinct molecular mechanisms underlying the phenotypic heterogeneity. Thus, stratifying ALS patients into consistent and clinically relevant subgroups can be of great value for the development of new precision diagnostics and targeted therapeutics for ALS patients. In the last years, the use and integration of high-throughput "omics" approaches have dramatically changed our thinking about ALS, improving our understanding of the complex molecular architecture of ALS, distinguishing distinct patient subtypes and providing a rational foundation for the discovery of biomarkers and new individualized treatments. In this review, we discuss the most significant contributions of omics technologies in unraveling the biological heterogeneity of ALS, highlighting how these approaches are revealing diagnostic, prognostic and therapeutic targets for future personalized interventions.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

On fusion methods for knowledge discovery from multi-omics datasets

Recent years have witnessed the tendency of measuring a biological sample on multiple omics scales for a comprehensive understanding of how biological activities on varying levels are perturbed by genetic variants, environments, and their interactions. This new trend raises substantial challenges to data integration and fusion, of which the latter is a specific type of integration that applies a uniform method in a scalable manner, to solve biological problems which the multi-omics measurements target. Fusion-based analysis has advanced rapidly in the past decade, thanks to application drivers and theoretical breakthroughs in mathematics, statistics, and computer science. We will briefly address these methods from methodological and mathematical perspectives and categorize them into three types of approaches: data fusion (a narrowed definition as compared to the general data fusion concept), model fusion, and mixed fusion. We will demonstrate at least one typical example in each specific category to exemplify the characteristics, principles, and applications of the methods in general, as well as discuss the gaps and potential issues for future studies.

Using big data to promote precision oral health in the context of a learning healthcare system

There has been a call for evidence-based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of up-to-date evidence, the limited integrative analytical data sets, and the slow translations to routine care delivery. Overcoming these issues requires knowledge discovery pipelines based on big data and health analytics, intelligent integrative informatics approaches, and learning health systems. This article examines how this can be accomplished by utilizing big data. These data can be gathered from four major streams: patients, clinical data, biological data, and normative data sets. All these must then be uniformly combined for analysis and modelling and the meaningful findings can be implemented clinically. By executing data capture cycles and integrating the subsequent findings, practitioners are able to improve public oral health and care delivery.

Dynamically characterizing individual clinical change by the steady state of disease-associated pathway

Background

Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes.

Results

Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states.

Conclusions

Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.

Integrative Metabolomic and Metallomic Analysis in a Case-Control Cohort With Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disease with a complex etiology. Several factors are known to contribute to the disease onset and its progression. However, the complete underlying mechanisms are still escaping our understanding. To evaluate possible correlations between metabolites and metallomic data, in this research, we combined a control study measured using two different platforms. For the different data sources, we applied a Block Sparse Partial Least Square Discriminant Analysis (Block-sPLS-DA) model that allows for proving their relation, which in turn uncovers alternative influencing factors that remain hidden otherwise. We found two groups of variables that trace a strong relationship between metallomic and metabolomic parameters for disease development. The results confirmed that the redox active metals iron (Fe) and copper (Cu) together with fatty acids are the major influencing factors for the PD. Additionally, the metabolic waste product p-cresol sulfate and the trace element nickel (Ni) showed up as potentially important factors in PD. In summary, the data integration of different types of measurements emphasized the results of both stand-alone measurements providing a new comprehensive set of information and interactions, on PD disease, between different variables sources.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

Characterizing the Personalized Microbiota Dynamics for Disease Classification by Individual-Specific Edge-Network Analysis

Environmental factors such as the gut microbiome are thought to play an important role in the development and treatment of many diseases. But our understanding of microbiota compositional dynamics is still unclear and incomplete because the intestinal microbial community is an easily-changed ecosystem. It is urgently required to understand the large variations among individuals. These variations, however, will be an asset rather than a limitation to personalized medicine. For a proof-of-concept study on individual-specific disease classification based on microbiota compositional dynamics, we implemented an adjusted individual-specific edge-network analysis (iENA) method to address a limited number of samples from one individual, and compared it to the temporal 16S rRNA (ribosomal RNA) gene sequencing data from individuals in a challenge study. Our identified individual-specific OTU markers or their combined markers are consistent with previously reported markers, and the predictive score based on them can perform a better AUROC than the previous 0.83 and achieve about 90% accuracy on predicting whether an individual developed diarrhea [i.e., were symptomatic (Sx)] or not. In addition, iENA also showed satisfactory efficiency on another dataset about bacterial vaginosis (BV). All these results suggest that the combination of high-throughput microbiome experiments and computational systems biology approaches can efficiently recommend potential candidate species in the defense against various pathogens for precision medicine.

High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis

Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

High-throughput technologies have been used to generate a large amount of omics data. In the past, single-level analysis has been extensively conducted where the omics measurements at different levels, including mRNA, microRNA, CNV and DNA methylation, are analyzed separately. As the molecular complexity of disease etiology exists at all different levels, integrative analysis offers an effective way to borrow strength across multi-level omics data and can be more powerful than single level analysis. In this article, we focus on reviewing existing multi-omics integration studies by paying special attention to variable selection methods. We first summarize published reviews on integrating multi-level omics data. Next, after a brief overview on variable selection methods, we review existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively. The strength and limitations of the methods are discussed in detail. No existing integration method can dominate the rest. The computation aspects are also investigated. The review concludes with possible limitations and future directions for multi-level omics data integration.