本计算框架源于分析肠道菌群如何影响糖尿病人血液循环系统中代谢物的研究成果,由 R 脚本编写,在 PC 机上即可运行;

Nature Protocols [IF:11.334]

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



2018-10-31, Article

Abstract & Authors:展开

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.

First Authors:
Helle Krogh Pedersen

Correspondence Authors:
Oluf Pedersen,Henrik Bjørn Nielsen

All Authors:
Helle Krogh Pedersen,Sofia K Forslund,Valborg Guðmundsdóttir,Anders Østergaard Petersen,Falk Hildebrand,Tuulia Hyotylainen,Trine Nielsen,Torben Hansen,Peer Bork,S Dusko Ehrlich,Søren Brunak,Matej Oresic,Oluf Pedersen,Henrik Bjørn Nielsen