timeOmics: an R package for longitudinal multi-omics data integration

Abstract Motivation Multi-omics data integration enables the global analysis of biological systems and discovery of new biological insights. Multi-omics experimental designs have been further extended with a longitudinal dimension to study dynamic relationships between molecules. However, methods that integrate longitudinal multi-omics data are still in their infancy. Results We introduce the R package timeOmics, a generic analytical framework for the integration of longitudinal multi-omics data. The framework includes pre-processing, modeling and clustering to identify molecular features strongly associated with time. We illustrate this framework in a case study to detect seasonal patterns of mRNA, metabolites, gut taxa and clinical variables in patients with diabetes mellitus from the integrative Human Microbiome Project. Availabilityand implementation timeOmics is available on Bioconductor and github.com/abodein/timeOmics. Supplementary information Supplementary data are available at Bioinformatics online.

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multiomics: A user-friendly multi-omics data harmonisation R pipeline

F1000Research ◽

10.12688/f1000research.53453.1 ◽

2021 ◽

Vol 10 ◽

pp. 538

Author(s):

Tyrone Chen ◽

Al J Abadi ◽

Kim-Anh Lê Cao ◽

Sonika Tyagi

Keyword(s):

Data Integration ◽

Case Studies ◽

R Package ◽

Heterogeneous Data ◽

General Purpose ◽

Omics Data ◽

Experimental Conditions ◽

Seamless Integration ◽

Data Object ◽

Omics Data Integration

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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0311 - Effect of ammonia on the dynamics of anaerobic digestion microbiome: omics data integration in a time-course context

10.26226/morressier.5b5199beb1b87b000ecee94b ◽

2018 ◽

Author(s):

Olivier Chapleur

Keyword(s):

Anaerobic Digestion ◽

Data Integration ◽

Time Course ◽

Omics Data ◽

Omics Data Integration

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Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

Molecular Omics ◽

10.1039/d1mo00117e ◽

2021 ◽

Author(s):

Kevin Chappell ◽

Kanishka Manna ◽

Charity L. Washam ◽

Stefan Graw ◽

Duah Alkam ◽

...

Keyword(s):

Breast Cancer ◽

Data Integration ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Biological Pathways ◽

Omics Data ◽

Insight Into ◽

Omics Data Integration

Multi-omics data integration of triple negative breast cancer (TNBC) provides insight into biological pathways.

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MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

Scientific Reports ◽

10.1038/s41598-021-81200-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mario Zanfardino ◽

Rossana Castaldo ◽

Katia Pane ◽

Ornella Affinito ◽

Marco Aiello ◽

...

Keyword(s):

User Interface ◽

Data Integration ◽

Graphical User Interface ◽

Data Science ◽

Heterogeneous Data ◽

Biological Information ◽

Omics Data ◽

Correlation Clustering ◽

Downstream Analysis ◽

Omics Data Integration

AbstractAnalysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

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