CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
November 10th, 2023
We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.
Metabolomics data analysis is a multi-step process that leverages many specialized software tools. This data analysis can be divided into three major steps, data processing and quality control, statistical analysis, and biological data interpretation. The tools described in this protocol are designed to enable the latter step in the analysis.
In the last decade, metabolomics has emerged as anomic science due to advances in analytical technologies such as gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry. These techniques allow the simultaneous measurements of hundreds to thousands of small molecule metabolites creating complex multidimensional data sets. Metabolomics data analysis presents several challenges for pathway based enrichment approaches.
First, a significant number of metabolites cannot be mapped onto metabolic pathways. Further, pathway coverage of secondary and lipid metabolism are not sufficient. Therefore, alternative approaches are needed for the biological interpretation of the data.
Data-driven network analysis techniques can help overcome challenges associated with knowledge-based pathway enrichment analysis for metabolomics data. For example, correlation networks can help derive relationships among both known and unknown metabolites, and can thus facilitate annotations of the unknowns.
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Overview
This article addresses the challenges of biological data interpretation in metabolomics, a field that has gained traction due to advanced analytical technologies. It introduces two tools, CorrelationCalculator and Filigree, designed to enhance data-driven network construction and analysis of metabolomics data.
Key Study Components
Research Area
- Metabolomics
- Data analysis techniques
- Network construction
Background
- Metabolomics involves analyzing complex data sets created by measuring small molecule metabolites.
- Current methods face challenges in mapping metabolites to metabolic pathways.
- There is a need for alternative biological interpretation approaches.
Methods Used
- Data-driven network analysis
- Correlation networks for relationships among metabolites
- Statistical and clustering methods for network enrichment
Main Results
- CorrelationCalculator aids in constructing a single interaction network of metabolites.
- Filigree facilitates building a differential network and supports network clustering and enrichment analysis.
- The tools help overcome limitations of traditional pathway enrichment analysis.
Conclusions
- The study demonstrates effective strategies for tackling the complexities of metabolomics data analysis.
- The introduction of these tools is highly relevant for advancing biological research in metabolomics.
