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Wu, Cen, Fei Zhou, Jie Ren, Xiaoxi Li, Yu Jiang, and Shuangge Ma. "A Selective Review of Multi-Level Omics Data Integration Using Variable Selection." High-Throughput 8, no. 1 (January 18, 2019): 4. http://dx.doi.org/10.3390/ht8010004.
Повний текст джерелаАнотація:
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.
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Sathyanarayanan, Anita, Rohit Gupta, Erik W. Thompson, Dale R. Nyholt, Denis C. Bauer, and Shivashankar H. Nagaraj. "A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping." Briefings in Bioinformatics 21, no. 6 (November 27, 2019): 1920–36. http://dx.doi.org/10.1093/bib/bbz121.
Повний текст джерелаАнотація:
Abstract Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.
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Subramanian, Indhupriya, Srikant Verma, Shiva Kumar, Abhay Jere, and Krishanpal Anamika. "Multi-omics Data Integration, Interpretation, and Its Application." Bioinformatics and Biology Insights 14 (January 2020): 117793221989905. http://dx.doi.org/10.1177/1177932219899051.
Повний текст джерелаАнотація:
To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.
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Li, Chuan-Xing, Craig E. Wheelock, C. Magnus Sköld, and Åsa M. Wheelock. "Integration of multi-omics datasets enables molecular classification of COPD." European Respiratory Journal 51, no. 5 (March 15, 2018): 1701930. http://dx.doi.org/10.1183/13993003.01930-2017.
Повний текст джерелаАнотація:
Chronic obstructive pulmonary disease (COPD) is an umbrella diagnosis caused by a multitude of underlying mechanisms, and molecular sub-phenotyping is needed to develop molecular diagnostic/prognostic tools and efficacious treatments.The objective of these studies was to investigate whether multi-omics integration improves the accuracy of molecular classification of COPD in small cohorts.Nine omics data blocks (comprising mRNA, micro RNA, proteomes and metabolomes) collected from several anatomical locations from 52 female subjects were integrated by similarity network fusion (SNF). Multi-omics integration significantly improved the accuracy of group classification of COPD patients from healthy never-smokers and from smokers with normal spirometry, reducing required group sizes from n=30 to n=6 at 95% power. Seven different combinations of four to seven omics platforms achieved >95% accuracy.For the first time, a quantitative relationship between multi-omics data integration and accuracy of data-driven classification power has been demonstrated across nine omics data blocks. Integrating five to seven omics data blocks enabled 100% correct classification of COPD diagnosis with groups as small as n=6 individuals, despite strong confounding effects of current smoking. These results can serve as guidelines for the design of future systems-based multi-omics investigations, with indications that integrating five to six data blocks from several molecular levels and anatomical locations suffices to facilitate unsupervised molecular classification in small cohorts.
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Bodein, Antoine, Marie-Pier Scott-Boyer, Olivier Perin, Kim-Anh Lê Cao, and Arnaud Droit. "Interpretation of network-based integration from multi-omics longitudinal data." Nucleic Acids Research 50, no. 5 (December 9, 2021): e27-e27. http://dx.doi.org/10.1093/nar/gkab1200.
Повний текст джерелаАнотація:
Abstract Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.
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Colomé-Tatché, M., and F. J. Theis. "Statistical single cell multi-omics integration." Current Opinion in Systems Biology 7 (February 2018): 54–59. http://dx.doi.org/10.1016/j.coisb.2018.01.003.
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Duan, Ran, Lin Gao, Yong Gao, Yuxuan Hu, Han Xu, Mingfeng Huang, Kuo Song, et al. "Evaluation and comparison of multi-omics data integration methods for cancer subtyping." PLOS Computational Biology 17, no. 8 (August 12, 2021): e1009224. http://dx.doi.org/10.1371/journal.pcbi.1009224.
Повний текст джерелаАнотація:
Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.
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Pinu, Farhana R., David J. Beale, Amy M. Paten, Konstantinos Kouremenos, Sanjay Swarup, Horst J. Schirra, and David Wishart. "Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community." Metabolites 9, no. 4 (April 18, 2019): 76. http://dx.doi.org/10.3390/metabo9040076.
Повний текст джерелаАнотація:
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.
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ElKarami, Bashier, Abedalrhman Alkhateeb, Hazem Qattous, Lujain Alshomali, and Behnam Shahrrava. "Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network." Cancer Informatics 21 (January 2022): 117693512211242. http://dx.doi.org/10.1177/11769351221124205.
Повний текст джерелаАнотація:
Introduction: Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Various promising integrative approaches have been utilized to analyze multi-omics data for biomedical applications, including disease prediction and disease subtypes, biomarker prediction, and others. Methods: In this paper, we introduce a multi-omics data integration method that is constructed using the combination of gene similarity network (GSN) based on uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients. Results: The model proposed near perfection with accuracy above 99% with all other performance measurements at the same level. The proposed model outperformed the state-of-art iSOM-GSN model that constructs the GSN map based on the self-organizing map. Conclusion: The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SOM. The proposed model can also be applied to build a multi-omics prediction model for other types of cancer.
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Rappoport, Nimrod, and Ron Shamir. "NEMO: cancer subtyping by integration of partial multi-omic data." Bioinformatics 35, no. 18 (January 30, 2019): 3348–56. http://dx.doi.org/10.1093/bioinformatics/btz058.
Повний текст джерелаАнотація:
Abstract Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. Availability and implementation Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. Supplementary information Supplementary data are available at Bioinformatics online.
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Singh, Amrit, Casey P. Shannon, Benoît Gautier, Florian Rohart, Michaël Vacher, Scott J. Tebbutt, and Kim-Anh Lê Cao. "DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays." Bioinformatics 35, no. 17 (January 18, 2019): 3055–62. http://dx.doi.org/10.1093/bioinformatics/bty1054.
Повний текст джерелаАнотація:
Abstract Motivation In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. Results Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites. Availability and implementation DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters’ choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. Supplementary information Supplementary data are available at Bioinformatics online.
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Zhu, Yingkun, Dengpan Bu, and Lu Ma. "Integration of Multiplied Omics, a Step Forward in Systematic Dairy Research." Metabolites 12, no. 3 (March 4, 2022): 225. http://dx.doi.org/10.3390/metabo12030225.
Повний текст джерелаАнотація:
Due to their unique multi-gastric digestion system highly adapted for rumination, dairy livestock has complicated physiology different from monogastric animals. However, the microbiome-based mechanism of the digestion system is congenial for biology approaches. Different omics and their integration have been widely applied in the dairy sciences since the previous decade for investigating their physiology, pathology, and the development of feed and management protocols. The rumen microbiome can digest dietary components into utilizable sugars, proteins, and volatile fatty acids, contributing to the energy intake and feed efficiency of dairy animals, which has become one target of the basis for omics applications in dairy science. Rumen, liver, and mammary gland are also frequently targeted in omics because of their crucial impact on dairy animals’ energy metabolism, production performance, and health status. The application of omics has made outstanding contributions to a more profound understanding of the physiology, etiology, and optimizing the management strategy of dairy animals, while the multi-omics method could draw information of different levels and organs together, providing an unprecedented broad scope on traits of dairy animals. This article reviewed recent omics and multi-omics researches on physiology, feeding, and pathology on dairy animals and also performed the potential of multi-omics on systematic dairy research.
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Khorraminezhad, Leila, Mickael Leclercq, Arnaud Droit, Jean-François Bilodeau, and Iwona Rudkowska. "Statistical and Machine-Learning Analyses in Nutritional Genomics Studies." Nutrients 12, no. 10 (October 14, 2020): 3140. http://dx.doi.org/10.3390/nu12103140.
Повний текст джерелаАнотація:
Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.
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Cao, Kai, Xiangqi Bai, Yiguang Hong, and Lin Wan. "Unsupervised topological alignment for single-cell multi-omics integration." Bioinformatics 36, Supplement_1 (July 1, 2020): i48—i56. http://dx.doi.org/10.1093/bioinformatics/btaa443.
Повний текст джерелаАнотація:
Abstract Motivation Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. Results In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex non-linear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. Availability and implementation UnionCom software is available at https://github.com/caokai1073/UnionCom. Supplementary information Supplementary data are available at Bioinformatics online.
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Sharifi-Noghabi, Hossein, Olga Zolotareva, Colin C. Collins, and Martin Ester. "MOLI: multi-omics late integration with deep neural networks for drug response prediction." Bioinformatics 35, no. 14 (July 2019): i501—i509. http://dx.doi.org/10.1093/bioinformatics/btz318.
Повний текст джерелаАнотація:
Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.
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Eicher, Tara, Garrett Kinnebrew, Andrew Patt, Kyle Spencer, Kevin Ying, Qin Ma, Raghu Machiraju, and Ewy A. Mathé. "Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources." Metabolites 10, no. 5 (May 15, 2020): 202. http://dx.doi.org/10.3390/metabo10050202.
Повний текст джерелаАнотація:
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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Zarayeneh, Neda, Euiseong Ko, Jung Hun Oh, Sang Suh, Chunyu Liu, Jean Gao, Donghyun Kim, and Mingon Kang. "Integration of multi-omics data for integrative gene regulatory network inference." International Journal of Data Mining and Bioinformatics 18, no. 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.087178.
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Kang, Mingon, Donghyun Kim, Jean Gao, Chunyu Liu, Sang Suh, Jung Hun Oh, Neda Zarayeneh, and Euiseong Ko. "Integration of multi-omics data for integrative gene regulatory network inference." International Journal of Data Mining and Bioinformatics 18, no. 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.10008266.
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Pierre-Jean, Morgane, Jean-François Deleuze, Edith Le Floch, and Florence Mauger. "Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration." Briefings in Bioinformatics 21, no. 6 (December 3, 2019): 2011–30. http://dx.doi.org/10.1093/bib/bbz138.
Повний текст джерелаАнотація:
Abstract Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.
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Adossa, Nigatu, Sofia Khan, Kalle T. Rytkönen, and Laura L. Elo. "Computational strategies for single-cell multi-omics integration." Computational and Structural Biotechnology Journal 19 (2021): 2588–96. http://dx.doi.org/10.1016/j.csbj.2021.04.060.
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Charmpi, Konstantina, Manopriya Chokkalingam, Ronja Johnen, and Andreas Beyer. "Optimizing network propagation for multi-omics data integration." PLOS Computational Biology 17, no. 11 (November 11, 2021): e1009161. http://dx.doi.org/10.1371/journal.pcbi.1009161.
Повний текст джерелаАнотація:
Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a ‘topology bias’ caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.
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Moon, Sehwan, and Hyunju Lee. "JDSNMF: Joint Deep Semi-Non-Negative Matrix Factorization for Learning Integrative Representation of Molecular Signals in Alzheimer’s Disease." Journal of Personalized Medicine 11, no. 8 (July 21, 2021): 686. http://dx.doi.org/10.3390/jpm11080686.
Повний текст джерелаАнотація:
High dimensional multi-omics data integration can enhance our understanding of the complex biological interactions in human diseases. However, most studies involving unsupervised integration of multi-omics data focus on linear integration methods. In this study, we propose a joint deep semi-non-negative matrix factorization (JDSNMF) model, which uses a hierarchical non-linear feature extraction approach that can capture shared latent features from the complex multi-omics data. The extracted latent features obtained from JDSNMF enabled a variety of downstream tasks, including prediction of disease and module analysis. The proposed model is applicable not only to sample-matched multiple data (e.g., multi-omics data from one cohort) but also to feature-matched multiple data (e.g., omics data from multiple cohorts), and therefore it can be flexibly applied to various cases. We demonstrate the capabilities of JDSNMF using sample-matched simulated data and feature-matched multi-omics data from Alzheimer’s disease cohorts, evaluating the feature extraction performance in the context of classification. In a test application, we identify AD- and age-related modules from the latent matrices using an explainable artificial intelligence and regression model. These results show that the JDSNMF model is effective in identifying latent features having a complex interplay of potential biological signatures.
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Chen, Tyrone, Al J. Abadi, Kim-Anh Lê Cao, and Sonika Tyagi. "multiomics: A user-friendly multi-omics data harmonisation R pipeline." F1000Research 10 (July 6, 2021): 538. http://dx.doi.org/10.12688/f1000research.53453.1.
Повний текст джерелаАнотація:
Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.
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Li, Peng, and Bo Sun. "Integration of Multi-Omics Data to Identify Cancer Biomarkers." Journal of Information Technology Research 15, no. 1 (January 2022): 1–15. http://dx.doi.org/10.4018/jitr.2022010105.
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A novel method for integrating multi-omics data, including gene expression, copy number variation, DNA methylation, and miRNA data, is proposed to identify biomarkers of cancer prognosis. First, survival analysis was performed for these four types of omics data to obtain survival-related genes. Next, survival-related genes detected in at least two types of omics data were selected as candidate genes. The four types of omics data only composed of candidate genes were subjected to dimension reduction using an autoencoder to obtain a one-dimensional data representation. The mRMR algorithm was used to screen for key genes. This method was applied to lung squamous cell carcinoma and 20 cancer-related genes were identified. Gene function analysis revealed that the genes were related to cancer. Using survival analysis, the genes were verified to distinguish between high- and low-risk groups. These results indicate that the genes can be used as biomarkers for cancer.
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Misra, Biswapriya B., Carl Langefeld, Michael Olivier, and Laura A. Cox. "Integrated omics: tools, advances and future approaches." Journal of Molecular Endocrinology 62, no. 1 (January 2019): R21—R45. http://dx.doi.org/10.1530/jme-18-0055.
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With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics or shortened to just ‘omics’, the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving. The ultimate goal is toward the holistic realization of a ‘systems biology’ understanding of the biological question. Commonly used approaches are currently limited by the 3 i’s – integration, interpretation and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.
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Xu, Chao, Ji-Gang Zhang, Dongdong Lin, Lan Zhang, Hui Shen, and Hong-Wen Deng. "A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease." G3 Genes|Genomes|Genetics 7, no. 7 (July 1, 2017): 2271–79. http://dx.doi.org/10.1534/g3.117.042408.
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Abstract Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi)genomic factors from diverse omics data; and (iii) Until present, the combination of more than two omics datasets has been poorly explored. Current integration approaches are not sufficient to address all of these challenges together. We proposed a novel integrative analysis framework by incorporating sparse model, multivariate analysis, Gaussian graphical model, and network analysis to address these three challenges simultaneously. Based on this strategy, we performed a systemic analysis for glioblastoma multiforme (GBM) integrating genome-wide gene expression, DNA methylation, and miRNA expression data. We identified three regulatory modules of genomic factors associated with GBM survival time and revealed a global regulatory pattern for GBM by combining the three modules, with respect to the common regulatory factors. Our method can not only identify disease-associated dysregulated genomic factors from different omics, but more importantly, it can incorporate the information from publicly accessible databases and omics datasets to infer a comprehensive interaction map of all these dysregulated genomic factors. Our work represents an innovative approach to enhance our understanding of molecular genomic mechanisms underlying human complex diseases.
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Kim, Dokyoon, Je-Gun Joung, Kyung-Ah Sohn, Hyunjung Shin, Yu Rang Park, Marylyn D. Ritchie, and Ju Han Kim. "Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction." Journal of the American Medical Informatics Association 22, no. 1 (July 7, 2014): 109–20. http://dx.doi.org/10.1136/amiajnl-2013-002481.
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Abstract Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Methods Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Results Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Conclusions Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies.
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Takahashi, Satoshi, Ken Asada, Ken Takasawa, Ryo Shimoyama, Akira Sakai, Amina Bolatkan, Norio Shinkai, et al. "Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data." Biomolecules 10, no. 10 (October 19, 2020): 1460. http://dx.doi.org/10.3390/biom10101460.
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Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.
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Vlachavas, Efstathios Iason, Jonas Bohn, Frank Ückert, and Sylvia Nürnberg. "A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research." International Journal of Molecular Sciences 22, no. 6 (March 10, 2021): 2822. http://dx.doi.org/10.3390/ijms22062822.
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Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.
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Shi, W. Jenny, Yonghua Zhuang, Pamela H. Russell, Brian D. Hobbs, Margaret M. Parker, Peter J. Castaldi, Pratyaydipta Rudra, et al. "Unsupervised discovery of phenotype-specific multi-omics networks." Bioinformatics 35, no. 21 (April 8, 2019): 4336–43. http://dx.doi.org/10.1093/bioinformatics/btz226.
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Abstract Motivation Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. Results We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA–mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA–mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. Availability and implementation The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Lancaster, Samuel M., Akshay Sanghi, Si Wu, and Michael P. Snyder. "A Customizable Analysis Flow in Integrative Multi-Omics." Biomolecules 10, no. 12 (November 27, 2020): 1606. http://dx.doi.org/10.3390/biom10121606.
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The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.
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Ye, Qing, and Nancy Lan Guo. "Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets." Cells 12, no. 1 (December 26, 2022): 101. http://dx.doi.org/10.3390/cells12010101.
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There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.
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Cai, Zhaoxiang, Rebecca C. Poulos, Jia Liu, and Qing Zhong. "Machine learning for multi-omics data integration in cancer." iScience 25, no. 2 (February 2022): 103798. http://dx.doi.org/10.1016/j.isci.2022.103798.
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Tini, Giulia, Luca Marchetti, Corrado Priami, and Marie-Pier Scott-Boyer. "Multi-omics integration—a comparison of unsupervised clustering methodologies." Briefings in Bioinformatics 20, no. 4 (December 18, 2017): 1269–79. http://dx.doi.org/10.1093/bib/bbx167.
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Abstract With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.
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Wang, Qing, Kaicen Wang, Wenrui Wu, Eleni Giannoulatou, Joshua W. K. Ho, and Lanjuan Li. "Host and microbiome multi-omics integration: applications and methodologies." Biophysical Reviews 11, no. 1 (January 9, 2019): 55–65. http://dx.doi.org/10.1007/s12551-018-0491-7.
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Zhang, Xiaoyu, Yuting Xing, Kai Sun, and Yike Guo. "OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data." Cancers 13, no. 12 (June 18, 2021): 3047. http://dx.doi.org/10.3390/cancers13123047.
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High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called “the curse of dimensionality” in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.
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Madrid-Márquez, Laura, Cristina Rubio-Escudero, Beatriz Pontes, Antonio González-Pérez, José C. Riquelme, and Maria E. Sáez. "MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation." Applied Sciences 12, no. 8 (April 14, 2022): 3987. http://dx.doi.org/10.3390/app12083987.
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Background and Objectives: The burst of high-throughput omics technologies has given rise to a new era in systems biology, offering an unprecedented scenario for deriving meaningful biological knowledge through the integration of different layers of information. Methods: We have developed a new software tool, MOMIC, that guides the user through the application of different analysis on a wide range of omic data, from the independent single-omics analysis to the combination of heterogeneous data at different molecular levels. Results: The proposed pipeline is developed as a collection of Jupyter notebooks, easily editable, reproducible and well documented. It can be modified to accommodate new analysis workflows and data types. It is accessible via momic.us.es, and as a docker project available at github that can be locally installed. Conclusions: MOMIC offers a complete analysis environment for analysing and integrating multi-omics data in a single, easy-to-use platform.
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Hansen, Pernille Bjarup, Anja Karine Ruud, Gustavo de los Campos, Marta Malinowska, Istvan Nagy, Simon Fiil Svane, Kristian Thorup-Kristensen, Jens Due Jensen, Lene Krusell, and Torben Asp. "Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare." Plants 11, no. 17 (August 24, 2022): 2190. http://dx.doi.org/10.3390/plants11172190.
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Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72–0.91) than with genomic models alone (0.55–0.86). The correlation between predictions and phenotypes varied from 0.17–0.28 for control plants and 0.23–0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.
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Kim, Minseung, and Ilias Tagkopoulos. "Data integration and predictive modeling methods for multi-omics datasets." Molecular Omics 14, no. 1 (2018): 8–25. http://dx.doi.org/10.1039/c7mo00051k.
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Ding, Jessica, Montgomery Blencowe, Thien Nghiem, Sung-min Ha, Yen-Wei Chen, Gaoyan Li, and Xia Yang. "Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics." Nucleic Acids Research 49, W1 (May 28, 2021): W375—W387. http://dx.doi.org/10.1093/nar/gkab405.
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Abstract The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.
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Shin, Tae Hwan, Saraswathy Nithiyanandam, Da Yeon Lee, Do Hyeon Kwon, Ji Su Hwang, Seok Gi Kim, Yong Eun Jang, et al. "Analysis of Nanotoxicity with Integrated Omics and Mechanobiology." Nanomaterials 11, no. 9 (September 13, 2021): 2385. http://dx.doi.org/10.3390/nano11092385.
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Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.
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Zhou, Guangyan, Jessica Ewald, and Jianguo Xia. "OmicsAnalyst: a comprehensive web-based platform for visual analytics of multi-omics data." Nucleic Acids Research 49, W1 (May 21, 2021): W476—W482. http://dx.doi.org/10.1093/nar/gkab394.
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Abstract Data analysis and interpretation remain a critical bottleneck in current multi-omics studies. Here, we introduce OmicsAnalyst, a user-friendly, web-based platform that allows users to perform a wide range of well-established data-driven approaches for multi-omics integration, and visually explore their results in a clear and meaningful manner. To help navigate complex landscapes of multi-omics analysis, these approaches are organized into three visual analytics tracks: (i) the correlation network analysis track, where users choose among univariate and multivariate methods to identify important features and explore their relationships in 2D or 3D networks; (ii) the cluster heatmap analysis track, where users apply several cutting-edge multi-view clustering algorithms and explore their results via interactive heatmaps; and (iii) the dimension reduction analysis track, where users choose among several recent multivariate techniques to reveal global data structures, and explore corresponding scores, loadings and biplots in interactive 3D scatter plots. The three visual analytics tracks are equipped with comprehensive options for parameter customization, view customization and targeted analysis. OmicsAnalyst lowers the access barriers to many well-established methods for multi-omics integration via novel visual analytics. It is freely available at https://www.omicsanalyst.ca.
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Fiorentino, Giuseppe, Roberto Visintainer, Enrico Domenici, Mario Lauria, and Luca Marchetti. "MOUSSE: Multi-Omics Using Subject-Specific SignaturEs." Cancers 13, no. 14 (July 8, 2021): 3423. http://dx.doi.org/10.3390/cancers13143423.
Повний текст джерелаАнотація:
High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.
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Cao, Zhi-Jie, and Ge Gao. "Multi-omics single-cell data integration and regulatory inference with graph-linked embedding." Nature Biotechnology, May 2, 2022. http://dx.doi.org/10.1038/s41587-022-01284-4.
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AbstractDespite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE.
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Yu, Catherine T., Brittany N. Chao, Rolando Barajas, Majda Haznadar, Padma Maruvada, Holly L. Nicastro, Sharon A. Ross, Mukesh Verma, Scott Rogers, and Krista A. Zanetti. "An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data." Metabolomics 18, no. 5 (April 30, 2022). http://dx.doi.org/10.1007/s11306-022-01878-8.
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Abstract Background Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment. Aim of review To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data. Key scientific concepts of review We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.
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Agamah, Francis E., Jumamurat R. Bayjanov, Anna Niehues, Kelechi F. Njoku, Michelle Skelton, Gaston K. Mazandu, Thomas H. A. Ederveen, Nicola Mulder, Emile R. Chimusa, and Peter A. C. 't Hoen. "Computational approaches for network-based integrative multi-omics analysis." Frontiers in Molecular Biosciences 9 (November 14, 2022). http://dx.doi.org/10.3389/fmolb.2022.967205.
Повний текст джерелаАнотація:
Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.
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Kang, Mingon, Euiseong Ko, and Tesfaye B. Mersha. "A roadmap for multi-omics data integration using deep learning." Briefings in Bioinformatics 23, no. 1 (November 12, 2021). http://dx.doi.org/10.1093/bib/bbab454.
Повний текст джерелаАнотація:
Abstract High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.
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Yang, Ying, Sha Tian, Yushan Qiu, Pu Zhao, and Quan Zou. "MDICC: novel method for multi-omics data integration and cancer subtype identification." Briefings in Bioinformatics, April 18, 2022. http://dx.doi.org/10.1093/bib/bbac132.
Повний текст джерелаАнотація:
Abstract Each type of cancer usually has several subtypes with distinct clinical implications, and therefore the discovery of cancer subtypes is an important and urgent task in disease diagnosis and therapy. Using single-omics data to predict cancer subtypes is difficult because genomes are dysregulated and complicated by multiple molecular mechanisms, and therefore linking cancer genomes to cancer phenotypes is not an easy task. Using multi-omics data to effectively predict cancer subtypes is an area of much interest; however, integrating multi-omics data is challenging. Here, we propose a novel method of multi-omics data integration for clustering to identify cancer subtypes (MDICC) that integrates new affinity matrix and network fusion methods. Our experimental results show the effectiveness and generalization of the proposed MDICC model in identifying cancer subtypes, and its performance was better than those of currently available state-of-the-art clustering methods. Furthermore, the survival analysis demonstrates that MDICC delivered comparable or even better results than many typical integrative methods.
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Tsagiopoulou, Maria, Nikolaos Pechlivanis, Maria Christina Maniou, and Fotis Psomopoulos. "InterTADs: integration of multi-omics data on topologically associated domains, application to chronic lymphocytic leukemia." NAR Genomics and Bioinformatics 4, no. 1 (January 14, 2022). http://dx.doi.org/10.1093/nargab/lqab121.
Повний текст джерелаАнотація:
ABSTRACT The integration of multi-omics data can greatly facilitate the advancement of research in Life Sciences by highlighting new interactions. However, there is currently no widespread procedure for meaningful multi-omics data integration. Here, we present a robust framework, called InterTADs, for integrating multi-omics data derived from the same sample, and considering the chromatin configuration of the genome, i.e. the topologically associating domains (TADs). Following the integration process, statistical analysis highlights the differences between the groups of interest (normal versus cancer cells) relating to (i) independent and (ii) integrated events through TADs. Finally, enrichment analysis using KEGG database, Gene Ontology and transcription factor binding sites and visualization approaches are available. We applied InterTADs to multi-omics datasets from 135 patients with chronic lymphocytic leukemia (CLL) and found that the integration through TADs resulted in a dramatic reduction of heterogeneity compared to individual events. Significant differences for individual events and on TADs level were identified between patients differing in the somatic hypermutation status of the clonotypic immunoglobulin genes, the core biological stratifier in CLL, attesting to the biomedical relevance of InterTADs. In conclusion, our approach suggests a new perspective towards analyzing multi-omics data, by offering reasonable execution time, biological benchmarking and potentially contributing to pattern discovery through TADs.
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Yang, Yaodong, Mumtaz Ali Saand, Liyun Huang, Walid Badawy Abdelaal, Jun Zhang, Yi Wu, Jing Li, Muzafar Hussain Sirohi, and Fuyou Wang. "Applications of Multi-Omics Technologies for Crop Improvement." Frontiers in Plant Science 12 (September 3, 2021). http://dx.doi.org/10.3389/fpls.2021.563953.
Повний текст джерелаАнотація:
Multiple “omics” approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the “phenotype to genotype” and “genotype to phenotype” concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.