Supervised semi-automated data analysis software for gas chromatography / differential mobility spectrometry (GC/DMS) metabolomics applications
- 332 Downloads
Modern differential mobility spectrometers (DMS) produce complex and multi-dimensional data streams that allow for near-real-time or post-hoc chemical detection for a variety of applications. An active area of interest for this technology is metabolite monitoring for biological applications, and these data sets regularly have unique technical and data analysis end user requirements. While there are initial publications on how investigators have individually processed and analyzed their DMS metabolomic data, there are no user-ready commercial or open source software packages that are easily used for this purpose. We have created custom software uniquely suited to analyze gas chromatograph / differential mobility spectrometry (GC/DMS) data from biological sources. Here we explain the implementation of the software, describe the user features that are available, and provide an example of how this software functions using a previously-published data set. The software is compatible with many commercial or home-made DMS systems. Because the software is versatile, it can also potentially be used for other similarly structured data sets, such as GC/GC and other IMS modalities.
KeywordsDifferential mobility spectrometry (DMS) Field asymmetric ion mobility spectrometry (FAIMS) Principal component analysis (PCA) Partial least squares regression (PLS) Data analysis Software
Partial funding for this study was provided by: the National Science Foundation (NSF) grant #1255915 [CED], the California Citrus Research Board grant #5100-143 and #1500-159 [CED], The Hartwell Foundation [CED] and the United States Department of the Army grant W15P7T-12-C-A005 [CED], National Institutes of Health (NIH) grant number #1U01EB022003-01 and NIH grant #UL1 TR000002 [CED]. Student support was partially provided by the US Department of Veterans Affairs, Post-9/11 GI-Bill [DJP], and the National Science Foundation grant #1343479 Veteran’s Research Supplement [DJP]. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies.
The authors would like to thank members of their research group for Beta testing the software and suggested improvements: Raquel Cumeras, Mitchell M. McCartney, Sierra Spitulski, and Yuriy Zrodnikov.
- 4.Krylov EV, Coy SL, Vandermey J, Schneider BB, Covey TR, Nazarov EG (2010) Selection and generation of waveforms for differential mobility spectrometry. Rev Sci Instrum:81. doi: 10.1063/1.3284507
- 5.Manard M, Weeks S, Kyle K (2008) Monitoring/verification using DMS: TATP example 2008 I.E. conference on technologies for homeland security, vols 1 and 2:226–230 doi: 10.1109/ths.2008.4534454
- 14.Lu Y, Chen P, Harrington PB (2009) Comparison of differential mobility spectrometry and mass spectrometry for gas chromatographic detection of ignitable liquids from fire debris using projected difference resolution. Anal Bioanal Chem 394:2061–2067. doi: 10.1007/s00216-009-2786-9 CrossRefGoogle Scholar
- 20.Arasaradnam RP et al. (2014a) Detection of colorectal cancer (CRC) by urinary volatile organic compound analysis. PLoS One 9. doi: 10.1371/journal.pone.0108750
- 21.Arasaradnam RP et al. (2014b) Differentiating coeliac disease from irritable bowel syndrome by urinary volatile organic compound analysis - a pilot study. PLoS One:9. doi: 10.1371/journal.pone.0107312
- 25.Schivo M et al. (2013) A mobile instrumentation platform to distinguish airway disorders. J Breath Res:7. doi: 10.1088/1752-7155/7/1/017113
- 27.Peirano DJ, Aksenov AA, Pasamontes A, Davis CE (2013) Chapter 18: approaches for establishing methodologies in metabolomic studies for clinical diagnostics. In: Agah A (ed) Medical applications of artificial intelligence. CRC Press, Taylor Francis Group, pp. 279–304Google Scholar