Beating the Noise: New Statistical Methods for Detecting Signals in MALDI-TOF Spectra Below Noise Level
Background: The computer-assisted detection of small molecules by mass spectrometry in biological samples provides a snapshot of thousands of peptides, protein fragments and proteins in biological samples. This new analytical technology has the potential to identify disease associated proteomic patterns in blood serum. However, the presently available bioinformatic tools are not sensitive enough to identify clinically important low abundant proteins as hormons or tumor markers with only low blood concentrations.
Aim: Find, analyze and compare serum proteom patterns in groups of human subjects having different properties such as disease status with a new workflow to enhance sensitivity and specificity.
Problems: Mass data acquired from high-throughput platforms frequently are blurred and noisy. This complicates the reliable identification of peaks in general and very small peaks even below noise level in particular. However, this statement is only valid for single or few spectra. If the algorithm has access to a large number of spectra (e.g. N > 1000), new possibilities arise, one of such being a statistical approach.
Approach: Apply signal preprocessing steps followed by statistical analyses of the blurred data and the region below the typical noise threshold to identify signals usually hidden below this “barrier”.
Results: A new analysis workflow has been developed that is able to accurately identify, analyze and determine peaks and their parameters even below noise level which other tools can not detect. A Comparison to commercial software has clearly proven this gain in sensitivity. These additional peaks can be used in subsequent steps to build better peak patterns for proteomic pattern analysis. We belive that this new approach will foster identification of new biomarkers having not been detectable by most algorithms currently available.
KeywordsGaussian Mixture Model Peak Detection Sporadic Breast Cancer Proteomic Pattern Peak Detection Algorithm
Unable to display preview. Download preview PDF.
- 2.Becker, S., Cazares, L.H., Watson, P., Lynch, H., Semmes, O.J., Drake, R.R., Laronga, C.: Surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) differentiation of serum protein profiles of BRCA-1 and sporadic breast cancer. Ann. Surg. Oncol. 11(10), 907–914 (2004)CrossRefGoogle Scholar
- 3.Baumann, S., Ceglarek, U., Fiedler, G.M., Lembcke, J., Leichtle, A., Thiery, J.: Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clin. Chem. 51(6), 973–980 (2005)CrossRefGoogle Scholar
- 4.Hortin, G.L.: The MALDI TOF Mass Spectrometric View of the Plasma Proteome and Peptidome. Clin. Chem. (April 2006)Google Scholar
- 6.Sauve, A.C., Speed, T.P.: Normalization, baseline correction and alignment of high-throughput mass spectrometry data. In: Proceedings Gensips 2004 (2004)Google Scholar
- 7.Gröpl, C., Hildebrandt, A., Kohlbacher, O., Lange, E., Lövenich, S., Sturm, M.: OpenMS - Software for Mass Spectrometry. In: MBI Workshop on Computational Proteomics and Mass Spectrometry 2005, Ohio State University (2005)Google Scholar
- 8.Mazet, V., Brie, D., Idier, J.: Baseline spectrum estimation using half-quadratic minimization. In: Proceedings of the European Signal Processing Conference, Vienna, Autriche (September 2004)Google Scholar
- 10.Liu, Q., Krishnapuram, B., Pratapa, P., Liao, X., Hartemink, A., Carin, L.: Identification of differentially expressed proteins using maldi-tof mass spectra. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1323–1327 (November 2003)Google Scholar
- 11.Louis, A.K., Maass, P., Rieder, A.: Wavelets: Theorie und Anwendungen. In: Teubner, B.G., Stuttgart (eds.), 2nd edn. (1998)Google Scholar
- 12.Nason, G.P., Silverman, B.W.: The stationary wavelet transform and some statistical applications. Lecture Notes in Statistics, vol. 103, pp. 281–300 (1995)Google Scholar
- 15.Norris, J.L., Cornett, D.S., Mobley, J.A., Schwartz, S.A., Roder, H., Caprioli, R.M.: Preparing maldi mass spectra for statistical analysis: A practical approach. In: Proceedings of the 53rd ASMS Conference on Mass Spectrometry and Allied Topics, San Antonio, TX (June 2005)Google Scholar
- 16.Fung, E.T., Enderwick, C.: ProteinChip clinical proteomics: computational challenges and solutions. Biotechniques (Suppl. 34–8), 40–41 (March 2002)Google Scholar
- 18.McDonough, R.N., Whale, A.D.: Detection of Signals in Noise, 2nd edn. Academic Press, San Diego (1995)Google Scholar
- 21.Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.: Representation and discrimination based on gaussian mixture model probability densities - practices and algorithms. Technical Report 95. Lappeenranta University of Technology, Department of Information Technology (2005)Google Scholar
- 27.Aldous, D.J.: Exchangeability and related topics. Lecture Notes in Math - Ecole d’ete de probabilites de Saint-Flour, vol. 1117. Springer, Berlin (1983)Google Scholar