An automated Pearson's correlation change classification (APC3) approach for GC/MS metabonomic data using total ion
Bhaskaran David Prakash1, Kesavan Esuvaranathan, Paul C Ho
1Department of Pharmacy, National University of Singapore, Blk S4, 18 Science Drive 4, S(117543), Singapore.
The Analyst
|March 27, 2013
Summary
A new Pearson's correlation change classification (APC3) method offers fast and reliable analysis of GC/MS data. This computationally efficient approach provides comparable accuracy to other methods while being easier to use and less sensitive to outliers.
Area of Science:
- Analytical Chemistry
- Chemometrics
- Metabolomics
Background:
- Gas Chromatography-Mass Spectrometry (GC/MS) is crucial for analyzing complex mixtures.
- Accurate classification of GC/MS data is essential for identifying subtle changes.
- Existing classification methods can be computationally intensive and sensitive to outliers.
Purpose of the Study:
- To introduce a fully automated and computationally efficient Pearson's correlation change classification (APC3) approach.
- To evaluate the performance of APC3 compared to other classification methods.
- To demonstrate the broad applicability of APC3 to various metabonomic data.
Main Methods:
- Development of the automated Pearson's correlation change classification (APC3) algorithm.
- Utilizing only Total Ion Chromatogram (TIC) intensities from GC/MS data.
- Comparison with other dimensional reduction and classification techniques.
Main Results:
- APC3 achieved an average accuracy and AUC of 0.89 ± 0.08, comparable to existing methods.
- APC3 demonstrated 3.9 to 7 times faster processing speeds.
- The method showed low susceptibility to outliers and ease of use.
Conclusions:
- APC3 offers a computationally efficient and robust alternative for GC/MS data classification.
- The reliance on TIC data allows for potential application to LC/MS and NMR spectra.
- A RapidMiner implementation of APC3 is available for broader adoption.
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