GCxGC-MS Data Classification and Alignment
Posted 11 Jun, 2008 in Tools
Available Versions
- 2.0 (published)
Supporting Documents
- Statistical Modeling of OMIC Data (PPT, 692 Kb)
- Getting Started (PDF, 1.05 Mb)
- Input Datasets (PDF, 185.81 Kb)
This tool is closed source.
| Version | 2.0 - published on 02 Dec. 2008 | ||||||||||||||||||||||||
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| Contributor(s) | Dabao Zhang, Min Zhang, Jason Catlin |
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| At a glance | Align and classify GCxGC-MS data | ||||||||||||||||||||||||
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| Description | gc2ms is an OMIC data pre-processing tool for the classification and alignment of raw GCxGC MS data. The tool reads retention times, intensities and mass charge (M/Z) data from netCDF format files, and outputs correctly aligned peak intensity data for classified M/Z values. gc2ms uses the two-dimensional Correlation Optimized Warping (COW) algorithm, with curve matching, centering and rescaling to identify alignment parameters. The parameters are refined iteratively based on detected patterns.
The classified, aligned spectrum can then be used as input to further analysis, such as biomarker identification and classification. The gc2msclass tool processes massive datasets -- input datasets can be 1GB or larger -- and users can select dozens of datasets to align during a single gc2msclass run. The table below gives execution times for the classification and alignment of sample dataset collections from the cceHUB repository.
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| credits | The GCxGC MS alignment algorithm and software were developed by Professors Min Zhang and Dabao Zhang, Department of Statistics at Purdue University. The gc2ms tool was integrated into cceHUB by Jason Catlin. |
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| Cite this work | If you reference this work in a publication, please cite as follows: |
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