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Abstract

Biology

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published: November 10th, 2023

DOI:

10.3791/65512

1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 2Taubman Health Sciences Library, University of Michigan, Ann Arbor, 3Department of Statistics, University of Florida

Abstract

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

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Keywords Metabolomics Data Analysis

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