A computational framework to integrate high-throughput "-omics" datasets for the identification of potential mechanistic links

Helle Krogh Pedersen; Sofia K Forslund; Valborg Gudmundsdottir; Anders Østergaard Petersen; Falk Hildebrand; Tuulia Hyötyläinen; Trine Nielsen; Torben Hansen; Peer Bork; S. Dusko Ehrlich; Søren Brunak; Matej Oresic; Oluf Pedersen; Henrik Bjørn Nielsen

doi:10.1038/s41596-018-0064-z

A computational framework to integrate high-throughput "-omics" datasets for the identification of potential mechanistic links

Helle Krogh Pedersen, Sofia K Forslund, Valborg Gudmundsdottir, Anders Østergaard Petersen, Falk Hildebrand, Tuulia Hyötyläinen, Trine Nielsen, Torben Hansen, Peer Bork, S. Dusko Ehrlich, Søren Brunak, Matej Oresic, Oluf Pedersen, Henrik Bjørn Nielsen

Disease Systems Biology Program

21 Citations (Scopus)

Abstract

We 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.

Original language	English
Journal	Nature Protocols
Volume	13
Issue number	12
Pages (from-to)	2781-2800
Number of pages	20
ISSN	1754-2189
DOIs	https://doi.org/10.1038/s41596-018-0064-z
Publication status	Published - 1 Dec 2018

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Pedersen, H. K., Forslund, S. K., Gudmundsdottir, V., Petersen, A. Ø., Hildebrand, F., Hyötyläinen, T., Nielsen, T., Hansen, T., Bork, P., Ehrlich, S. D., Brunak, S., Oresic, M., Pedersen, O., & Nielsen, H. B. (2018). A computational framework to integrate high-throughput "-omics" datasets for the identification of potential mechanistic links. Nature Protocols, 13(12), 2781-2800. https://doi.org/10.1038/s41596-018-0064-z

Pedersen, HK, Forslund, SK, Gudmundsdottir, V, Petersen, AØ, Hildebrand, F, Hyötyläinen, T, Nielsen, T , Hansen, T, Bork, P, Ehrlich, SD, Brunak, S, Oresic, M, Pedersen, O & Nielsen, HB 2018, 'A computational framework to integrate high-throughput "-omics" datasets for the identification of potential mechanistic links', Nature Protocols, vol. 13, no. 12, pp. 2781-2800. https://doi.org/10.1038/s41596-018-0064-z

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AB - We 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.

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A computational framework to integrate high-throughput "-omics" datasets for the identification of potential mechanistic links

Abstract

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