Washington University in St. Louis

The Patti Lab
Metabolomics to elucidate novel biochemical mechanisms of disease
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Warpgroup: Increased Precision of Metabolomic Data Processing by Consensus Integration Bound Analysis

Mahieu NG, Spalding JL, Patti GJ
Warpgroup: Increased Precision of Metabolomic Data Processing by Consensus Integration Bound Analysis
Bioinformatics, 32(2), 268-275, 2016
doi:10.1093/bioinformatics/btv564

Motivation: Current informatic techniques for processing raw, chromatography/mass spectrometry data break down under several common, non-ideal conditions. Importantly, hydrophilic liquid interaction chromatography (a key separation technology for metabolomics) produces data which are especially challenging to process. We identify three critical points of failure in current informatic workflows: compound specific drift, integration region variance, and naive missing value imputation. We implement the Warpgroup algorithm to address these challenges.

Results: Warpgroup adds peak subregion detection, consensus integration bound detection, and intelligent missing value imputation steps to the conventional informatic workflow. When compared to the conventional workflow, Warpgroup made major improvements to the processed data. The coefficient of variation for replicate injections of a complex Escherichia Coli extract were halved (a reduction of 19%). Integration regions across samples were much more robust. Additionally, many signals lost by the conventional workflow were “rescued” by the Warpgroup refinement, thereby resulting in greater analyte coverage in the processed data.

Availability and Implementation: Warpgroup is an open source R package available on GitHub at github.com/nathanielmahieu/warpgroup. The package includes example data and XCMS compatibility wrappers for ease of use.

Software Information Page

Please see the Warpgroup software page.

Washington University, Departments of Chemistry, Genetics, and Medicine. Saint Louis, Missouri 63110 USA