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Bioinformatics Advance Access published online on June 24, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth372
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received February 13, 2004
Revised June 9, 2004
Accepted June 15, 2004

Article

Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens

Glen A. Satten 1*, Somnath Datta 2, Hercules Moura 1, Adrian R. Woolfitt 1, Maria da G. Carvalho 3, George M. Carlone 3, Barun K. De 3, Antonis Pavlopoulos 1, John R. Barr 1

1 Division of Laboratory Science, National Center for Environmental Health, Centers for Disease Control and Prevention, Chamblee, GA 30341
2 Department of Statistics, University of Georgia, Athens, GA 30602
3 Division of Bacterial and Mycotic Diseases, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333

* To whom correspondence should be addressed. E-mail: gsatten{at}cdc.gov.


  Abstract

Motivation: Application of mass spectrometry in proteomics is a breakthrough in high throughput analyses. Early applications have focused on protein expression profiles to differentiate amongst various types of tissue samples (e.g., normal vs. tumor). Here our goal is to use mass spectra to differentiate bacterial species using whole-organism samples. The raw spectra are similar to spectra of tissue samples, raising some of the same statistical issues (e.g., non-uniform baselines and higher noise associated with higher baseline), but are substantially noisier. As a result, new preprocessing procedures are required before these spectra can be used for statistical classification.

Results: In this report, we introduce novel preprocessing steps that can be used with any mass spectra. These comprise a standardization step and a denoising step. The noise level for each spectrum is determined using only data from that spectrum. Only spectral features that exceed a threshold defined by the noise level are subsequently used for classification. Using this approach, we trained the Random Forest program to classify 240 mass spectra into four bacterial types. The method resulted in zero prediction errors in the training samples and in two test data sets having 240 and 300 spectra, respectively.

Availability: Fortran code for standardization and denoising are available from the authors upon request.

Supplementary information: http://www.stat.uga.edu/~datta/Massspec/supp.html.


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