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Updated: Jan 5, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Strategy for Intercorrelation Identification between Metabolome and Microbiome.

Dandan Liang¹, Mengci Li^1,2, Runmin Wei³

¹Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine , Shanghai Jiao Tong University Affiliated Sixth People's Hospital , Shanghai 200233 , China.

Analytical Chemistry

|October 23, 2019

Summary

This summary is machine-generated.

We developed Generalized coRrelation analysis for Metabolome and Microbiome (GRaMM), a novel strategy to uncover complex microbe-metabolite associations. GRaMM effectively identifies linear and nonlinear correlations, accounting for confounders in multi-omics data.

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Published on: May 27, 2014

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

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Published on: November 10, 2023

Area of Science:

Microbiome research
Metabolomics
Systems biology
Bioinformatics

Background:

The interplay between the microbiome and metabolome is crucial in health and disease.
Existing methods for analyzing microbe-metabolite associations have limitations.
There is a need for robust strategies to analyze complex multi-omics data.

Purpose of the Study:

To introduce Generalized coRrelation analysis for Metabolome and Microbiome (GRaMM), a novel computational strategy.
To enable the discovery of intercorrelations between the metabolome and microbiome.
To provide a comprehensive tool for analyzing complex biological data.

Main Methods:

GRaMM integrates linear regression, Maximum Information Coefficient (MIC), Metabolic Confounding Effect Elimination (MCEE), and Centered Log-Ratio (CLR) transformation.
The strategy involves four sequential steps: data preprocessing, correlation type identification, data correction, and p-value correction.
Performance evaluation included accuracy, sensitivity, specificity, and false positive rates using simulated and real-world datasets.

Main Results:

GRaMM demonstrated superior performance compared to three other methods in identifying microbe-metabolite associations.
The strategy effectively handles linear and nonlinear correlations and adjusts for confounding factors.
Evaluations confirmed the accuracy, sensitivity, and specificity of GRaMM.

Conclusions:

GRaMM is the first strategy specifically designed for intercorrelation analysis between metabolites and microbes.
The developed Matlab function and R package are freely available for academic use.
GRaMM offers a powerful and versatile tool for microbiome and metabolome research.