Sensitive and Specific Peak Detection for SELDI-TOF Mass Spectrometry Using a Wavelet/Neural-Network Based Approach
Abstract
SELDI-TOF mass spectrometer’s compact size and automated, high throughput design have been attractive to clinical researchers, and the platform has seen steady-use in biomarker studies. Despite new algorithms and preprocessing pipelines that have been developed to address reproducibility issues, visual inspection of the results of SELDI spectra preprocessing by the best algorithms still shows miscalled peaks and systematic sources of error. This suggests that there continues to be problems with SELDI preprocessing. In this work, we study the preprocessing of SELDI in detail and introduce improvements. While many algorithms, including the vendor supplied software, can identify peak clusters of specific mass (or m/z) in groups of spectra with high specificity and low false discover rate (FDR), the algorithms tend to underperform estimating the exact prevalence and intensity of peaks in those clusters. Thus group differences that at first appear very strong are shown, after careful and laborious hand inspection of the spectra, to be less than significant. Here we introduce a wavelet/neural network based algorithm which mimics what a team of expert, human users would call for peaks in each of several hundred spectra in a typical SELDI clinical study. The wavelet denoising part of the algorithm optimally smoothes themore »
- Authors:
-
- Centers for Disease Control and Prevention (CDC), Atlanta, GA (United States)
- Publication Date:
- Research Org.:
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Cancer Institute (NCI); Centers for Disease Control and Prevention (CDC)
- OSTI Identifier:
- 1904922
- Grant/Contract Number:
- SC0014664
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 11; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; neural networks; preprocessing; algorithms; breast cancer; wavelet transforms; computer software; reproducibility; visual inspection
Citation Formats
Emanuele II, Vincent A., Panicker, Gitika, Gurbaxani, Brian M., Lin, Jin-Mann S., and Unger, Elizabeth R. Sensitive and Specific Peak Detection for SELDI-TOF Mass Spectrometry Using a Wavelet/Neural-Network Based Approach. United States: N. p., 2012.
Web. doi:10.1371/journal.pone.0048103.
Emanuele II, Vincent A., Panicker, Gitika, Gurbaxani, Brian M., Lin, Jin-Mann S., & Unger, Elizabeth R. Sensitive and Specific Peak Detection for SELDI-TOF Mass Spectrometry Using a Wavelet/Neural-Network Based Approach. United States. https://doi.org/10.1371/journal.pone.0048103
Emanuele II, Vincent A., Panicker, Gitika, Gurbaxani, Brian M., Lin, Jin-Mann S., and Unger, Elizabeth R. Mon .
"Sensitive and Specific Peak Detection for SELDI-TOF Mass Spectrometry Using a Wavelet/Neural-Network Based Approach". United States. https://doi.org/10.1371/journal.pone.0048103. https://www.osti.gov/servlets/purl/1904922.
@article{osti_1904922,
title = {Sensitive and Specific Peak Detection for SELDI-TOF Mass Spectrometry Using a Wavelet/Neural-Network Based Approach},
author = {Emanuele II, Vincent A. and Panicker, Gitika and Gurbaxani, Brian M. and Lin, Jin-Mann S. and Unger, Elizabeth R.},
abstractNote = {SELDI-TOF mass spectrometer’s compact size and automated, high throughput design have been attractive to clinical researchers, and the platform has seen steady-use in biomarker studies. Despite new algorithms and preprocessing pipelines that have been developed to address reproducibility issues, visual inspection of the results of SELDI spectra preprocessing by the best algorithms still shows miscalled peaks and systematic sources of error. This suggests that there continues to be problems with SELDI preprocessing. In this work, we study the preprocessing of SELDI in detail and introduce improvements. While many algorithms, including the vendor supplied software, can identify peak clusters of specific mass (or m/z) in groups of spectra with high specificity and low false discover rate (FDR), the algorithms tend to underperform estimating the exact prevalence and intensity of peaks in those clusters. Thus group differences that at first appear very strong are shown, after careful and laborious hand inspection of the spectra, to be less than significant. Here we introduce a wavelet/neural network based algorithm which mimics what a team of expert, human users would call for peaks in each of several hundred spectra in a typical SELDI clinical study. The wavelet denoising part of the algorithm optimally smoothes the signal in each spectrum according to an improved suite of signal processing algorithms previously reported (the LibSELDI toolbox under development). The neural network part of the algorithm combines those results with the raw signal and a training dataset of expertly called peaks, to call peaks in a test set of spectra with approximately 95% accuracy. The new method was applied to data collected from a study of cervical mucus for the early detection of cervical cancer in HPV infected women. The method shows promise in addressing the ongoing SELDI reproducibility issues.},
doi = {10.1371/journal.pone.0048103},
journal = {PLoS ONE},
number = 11,
volume = 7,
place = {United States},
year = {Mon Nov 12 00:00:00 EST 2012},
month = {Mon Nov 12 00:00:00 EST 2012}
}
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Epidemiologic and viral factors associated with cervical neoplasia in HPV-16-positive women
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Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform
journal, November 2005
- Coombes, Kevin R.; Tsavachidis, Spiridon; Morris, Jeffrey S.
- PROTEOMICS, Vol. 5, Issue 16
Benchmarking currently available SELDI-TOF MS preprocessing techniques
journal, April 2009
- Emanuele, Vincent A.; Gurbaxani, Brian M.
- PROTEOMICS, Vol. 9, Issue 7
Characterization of the Human Cervical Mucous Proteome
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Effect of storage temperatures on the stability of cytokines in cervical mucous
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Optimization of SELDI-TOF protein profiling for analysis of cervical mucous
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A Comparative Simulation Study of Wavelet Shrinkage Estimators for Poisson Counts
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Quadratic variance models for adaptively preprocessing SELDI-TOF mass spectrometry data
journal, October 2010
- Emanuele, Vincent A.; Gurbaxani, Brian M.
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