Journal article
A computational framework to integrate high-throughput '-omics' datasets for the identification of potential mechanistic links
University of Copenhagen1
Clinical Microbiomics A/S2
Max Delbrück Center for Molecular Medicine in the Helmholtz Association3
Integrative Systems Biology, Department of Bio and Health Informatics, Technical University of Denmark4
Department of Bio and Health Informatics, Technical University of Denmark5
Metagenomics, Department of Bio and Health Informatics, Technical University of Denmark6
European Molecular Biology Laboratory7
Örebro University8
EMBL Heidelberg9
King's College London10
...and 0 moreWe recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration.
Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge.
The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various '-omics' readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings.
Although we originally developed the framework for a human metabolome-microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in ~1 h on a standard PC.
Language: | English |
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Year: | 2018 |
Pages: | 2781-2800 |
ISSN: | 17542189 and 17502799 |
Types: | Journal article |
DOI: | 10.1038/s41596-018-0064-z |
ORCIDs: | 0000-0001-9609-7377 , 0000-0002-2066-7895 , 0000-0001-8748-3831 , 0000-0003-0316-5866 , 0000-0002-3321-3972 and 0000-0002-2627-833X |