scholarly journals PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples

2021 ◽  
Vol 12 ◽  
Author(s):  
Gabriel J. Odom ◽  
Antonio Colaprico ◽  
Tiago C. Silva ◽  
X. Steven Chen ◽  
Lily Wang
Keyword(s):  
R Package ◽  
Integrative Analysis ◽  
Omics Data ◽  
Computationally Efficient ◽  
Data Types ◽  
Analysis Techniques ◽  
Genome Data ◽  
Analysis Strategies ◽  
Matched Samples ◽  
High Dimensional Datasets

Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer’s disease.

scholarly journals A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits

2019 ◽  
Vol 36 (3) ◽  
pp. 842-850 ◽  
Author(s):  
Cheng Peng ◽  
Jun Wang ◽  
Isaac Asante ◽  
Stan Louie ◽  
Ran Jin ◽  
...  
Keyword(s):  
Real Data ◽  
R Package ◽  
Integrative Model ◽  
Supplementary Information ◽  
Phenotypic Traits ◽  
Omics Data ◽  
Data Types ◽  
Specific Effects ◽  
Metabolomic Data ◽  
Future Prediction

Abstract Motivation Epidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups. Results We propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrated model for future prediction of risk subgroups and phenotypes. We apply this approach to two real data applications to highlight the integration of genomic, exposure and metabolomic data. Availability and Implementation The LUCID method is implemented through the LUCIDus R package available on CRAN (https://CRAN.R-project.org/package=LUCIDus). Supplementary information Supplementary materials are available at Bioinformatics online.

scholarly journals Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

2021 ◽  
Author(s):  
Takoua Jendoubi
Keyword(s):  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Analysis Pipeline ◽  
Metabolomics Data ◽  
Computational Tools ◽  
Complex Chemical Reactions ◽  
Living Organisms ◽  
Underlying Mechanisms ◽  
Omics Data Integration

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

scholarly journals MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification

2020 ◽  
Author(s):  
Tongxin Wang ◽  
Wei Shao ◽  
Zhi Huang ◽  
Haixu Tang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Data Classification ◽  
Integrative Analysis ◽  
Biomedical Data ◽  
Omics Data ◽  
Expression Data ◽  
Data Types ◽  
Convolutional Networks ◽  
Wide Range ◽  
Correlation Learning ◽  
Mirna Expression Data

ABSTRACTTo fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis for multiple types of omics data. We present a novel multi-omics integrative method named Multi-Omics gRaph cOnvolutional NETworks (MORONET) for biomedical classification. MORONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MORONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from a wide range of biomedical classification applications using mRNA expression data, DNA methylation data, and miRNA expression data. Furthermore, MORONET is able to identify important biomarkers from different omics data types that are related with the investigated diseases.

scholarly journals Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 184
Author(s):  
Takoua Jendoubi
Keyword(s):  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Analysis Pipeline ◽  
Metabolomics Data ◽  
Computational Tools ◽  
Complex Chemical Reactions ◽  
Living Organisms ◽  
Underlying Mechanisms ◽  
Omics Data Integration

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria—hypothesis, data types, strategies, study design and study focus— to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

scholarly journals integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

2018 ◽  
Author(s):  
Haley R. Eidem ◽  
Jacob Steenwyk ◽  
Jennifer Wisecaver ◽  
John A. Capra ◽  
Patrick Abbot ◽  
...  
Keyword(s):  
Preterm Birth ◽  
Data Integration ◽  
Candidate Genes ◽  
Desirability Function ◽  
R Package ◽  
Biological Research ◽  
Omics Data ◽  
Data Types ◽  
Spontaneous Preterm Birth ◽  
Research Areas

AbstractBackgroundThe integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.MethodsTo better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).ResultsWe developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub (https://github.com/haleyeidem/integRATE).ConclusionsDesirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

scholarly journals The Bioconductor channel in F1000Research

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 217 ◽  
Author(s):  
Wolfgang Huber ◽  
Vincent J. Carey ◽  
Sean Davis ◽  
Kasper Daniel Hansen ◽  
Martin Morgan
Keyword(s):  
Genomic Data ◽  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Know How ◽  
Modelling Techniques ◽  
Task Oriented ◽  
Genome Scale ◽  
A Current ◽  
Scale Data

Bioconductor (bioconductor.org) is a rich source of software and know-how for the integrative analysis of genomic data. The Bioconductor channel in F1000Research provides a forum for task-oriented workflows that each cover a solution to a current, important problem in genome-scale data analysis from end to end, invoking resources from several packages by different authors, often combining multiple `omics data types, and demonstrating integrative analysis and modelling techniques.

scholarly journals MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tongxin Wang ◽  
Wei Shao ◽  
Zhi Huang ◽  
Haixu Tang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Integrative Analysis ◽  
Omics Data ◽  
Patient Classification ◽  
Expression Data ◽  
Data Types ◽  
Mrna Expression Data ◽  
Convolutional Networks ◽  
Correlation Learning ◽  
Integrative Method ◽  
Specific Learning

AbstractTo fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

scholarly journals Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Erica Ponzi ◽  
Magne Thoresen ◽  
Therese Haugdahl Nøst ◽  
Kajsa Møllersen
Keyword(s):  
Lung Cancer ◽  
Biological Process ◽  
Case Control ◽  
Integrative Analysis ◽  
Data Sources ◽  
Omics Data ◽  
Biological Processes ◽  
Blood Samples ◽  
Model Predictions

Abstract Background Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific (“individual”) patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as “shared” or “joint”. In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case–control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case–control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. Results Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. Conclusions In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

scholarly journals An R Package for Divergence Analysis of Omics Data

2019 ◽  
Author(s):  
Wikum Dinalankara ◽  
Qian Ke ◽  
Donald Geman ◽  
Luigi Marchionni
Keyword(s):  
High Throughput Sequencing ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
High Dimensional ◽  
Omics Data ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Ternary Code ◽  
Cancer Genome Atlas ◽  
Level Analysis

AbstractGiven the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with sample high throughput sequencing data from the Cancer Genome Atlas.

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