GCxGC-MS Data Classification and Alignment

By Dabao Zhang, Min Zhang, Jason Catlin

Align and classify GCxGC-MS data

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Version 3.1 - published on 06 Jan 2015

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Abstract

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.

  • Step 1: Calculate shift/alignment coefficients
  • Step 2: Apply alignment coefficients to the GCxGC chromotograph generated at each M/Z value in the M/Z classification
  • Step 3: Pool the results

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.

Dataset Collection # datasets dataset size execution time
Colon Cancer Study
TumorSerum - Female Patients
2 800MB 4 min
Colon Cancer Study
Tumor Serum - 2 Age Groups
10 800MB 17 min
Colon Cancer Study
Control vs. Tumor Serum
36 800MB 61 min
Cureline1 Study
Control and Cancer Samples
20 1GB 80 min
Huang Study
All FA Samples
11 50MB-120MB 12 min

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.

Cite this work

Researchers should cite this work as follows:

  • Dabao Zhang; Min Zhang; Jason Catlin (2015), "GCxGC-MS Data Classification and Alignment," https://ccehub.org/resources/GC2MSClass.

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