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ProSpect: An R Package for Analyzing SELDI Measurements Identifying Protein Biomarkers

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Computational Life Sciences (CompLife 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3695))

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Abstract

Protein expression profiling is a multidisciplinary research field which promises success for early cancer detection and monitoring of this widespread disease. The surface enhanced laser desorption and ionization (SELDI) is a mass spectrometry method and one of two widely used techniques for protein biomarker discovery in cancer research. There are several algorithms for signal detection in mass spectra but they are known to have poor specificity and sensitivity. Scientists have to review the analyzed mass spectra manually which is time consuming and error prone. Therefore, algorithms with improved specificity are urgently needed. We aimed to develop a peak detection method with much better specificity than the standard methods.

The proposed peak algorithm is divided into three steps: (1) data import and preparation, (2) signal detection by using an Analysis of Variance (ANOVA) and the required F-statistics, and (3) classification of the computed peak cluster as significant based on the false discovery rate (FDR) specified by the user.

The proposed method offers a significantly reduced preprocessing time of SELDI spectra, especially for large studies.

The developed algorithms are implemented in R and available as open source packages ProSpect, rsmooth, and ProSpectGUI. The software implementation aims a high error tolerance and an easy handling for user which are unfamiliar with the statistical software R. Furthermore, the modular software design allows the simple extension and adaptation of the available code basis in the further development of the software.

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References

  1. http://www.r-project.org

  2. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.Ā B 57, 289ā€“300 (1995)

    MathSciNetĀ  Google ScholarĀ 

  3. Coombes, K.R., Fritsche, H.A., Clarke, C., Chen, J.-N., Baggerly, K.A., Morris, J.S., Xiao, L.-C., Hung, M.-C., Kuerer, H.M.: Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clin Chem.Ā 49(10), 1615ā€“1623 (2003)

    ArticleĀ  Google ScholarĀ 

  4. Etzioni, R., Urban, N., Ramsey, S., McIntosh, M., Schwartz, S., Reid, B., Radich, J., Anderson, G., Hartwell, L.: The case for early detection. Nat. Rev. CancerĀ 3(4), 243ā€“252 (2003)

    ArticleĀ  Google ScholarĀ 

  5. Fung, E., Diamond, D., Simonsesn, A.H., Weinberger, S.R.: The use of SELDI ProteinChip array technology in renal disease research. Methods Mol. Med.Ā 86, 295ā€“312 (2003)

    Google ScholarĀ 

  6. Fung, E.T., Enderwick, C.: ProteinChip clinical proteomics: computational challenges and solutions. BiotechniquesĀ (suppl.) 34(8), 40ā€“41 (2002)

    Google ScholarĀ 

  7. Klose, J.: Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues. A novel approach to testing for induced point mutations in mammals. HumangenetikĀ 26(3), 231ā€“243 (1975)

    Google ScholarĀ 

  8. Li, L., Tang, H., Wu, Z., Gong, J., Gruidl, M., Zou, J., Tockman, M., Clark, R.A.: Data mining techniques for cancer detection using serum proteomic profiling. Artif. Intell. Med.Ā 32(2), 71ā€“83 (2004)

    ArticleĀ  Google ScholarĀ 

  9. Liao, J., Lin, Y., Selvanayagam, Z.E., Shih, W.J.: A mixture model for estimating the local false discovery rate in DNA microarray analysis. BioinformaticsĀ 20(16), 2694ā€“2701 (2004)

    ArticleĀ  Google ScholarĀ 

  10. MacNeil, J.S.: Better biomarkers for the diagnostics labyrinth. Genome Technol., 24ā€“33 (2004)

    Google ScholarĀ 

  11. Molist, R., Gerbault-Seureau, M., Klijanienko, J., Vielh, P., Dutrillaux, B.: Potential rapid assessment of breast cancer prognosis using induced chromosome condensation performed on cytological specimens. Lab InvestĀ 84(4), 433ā€“439 (2004)

    ArticleĀ  Google ScholarĀ 

  12. Oā€™Farrell, P.H.: High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem.Ā 250(10), 4007ā€“4021 (1975)

    Google ScholarĀ 

  13. Pawitan, Y.: In All Likelihood: Statistical Modelling and Interence Using Likelihood. Oxford University Press, Oxford (2001)

    Google ScholarĀ 

  14. Sullivan Pepe, M., Etzioni, R., Feng, Z., Potter, J.D., Thompson, M.L., Thornquist, M., Winget, M., Yasui, Y.: Phases of biomarker development for early detection of cancer. J. Natl. Cancer Inst.Ā 93(14), 1054ā€“1061 (2001)

    ArticleĀ  Google ScholarĀ 

  15. Perou, C.M., SĆørlie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Rees, C.A., Pollack, J.R., Ross, D.T., Johnsen, H., Akslen, L.A., Fluge, O., Pergamenschikov, A., Williams, C., Zhu, S.X., LĆønning, P.E., BĆørresen-Dale, A.L., Brown, P.O., Botstein, D.: Molecular portraits of human breast tumours. NatureĀ 406(6797), 747ā€“752 (2000)

    ArticleĀ  Google ScholarĀ 

  16. Reiner, A., Yekutieli, D., Benjamini, Y.: Identifying differentially expressed genes using false discovery rate controlling procedures. BioinformaticsĀ 19(3), 368ā€“375 (2003)

    ArticleĀ  Google ScholarĀ 

  17. Resing, K.A., Ahn, N.G.: Proteomics strategies for protein identification. FEBS Lett.Ā 579(4), 885ā€“889 (2005)

    ArticleĀ  Google ScholarĀ 

  18. Shiwa, M., Nishimura, Y., Wakatabe, R., Fukawa, A., Arikuni, H., Ota, H., Kato, Y., Yamori, T.: Rapid discovery and identification of a tissue-specific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform. Biochem. Biophys. Res. Commun.Ā 309(1), 18ā€“25 (2003)

    ArticleĀ  Google ScholarĀ 

  19. Somiari, R.I., Somiari, S., Russell, S., Shriver, C.D.: Proteomics of breast carcinoma. J. Chromatogr B Analyt Technol Biomed Life Sci.Ā 815(1-2), 215ā€“225 (2005)

    ArticleĀ  Google ScholarĀ 

  20. Tan, C.-S., Ploner, A., Quandt, A., Lehtiƶ, J., Pawitan, Y.: Signal detection of seldi measurements for identifying protein biomarkers. Biostatistics (submitted February 2005)

    Google ScholarĀ 

  21. Weatherly, D.B., Atwood, J.A., Minning, T.A., Cavola, C., Tarleton, R.L., Orlando, R.: A heuristic method for assigning a false discovery rate for protein identifications from mascot database search results. Mol. Cell Proteomics ( February 2005)

    Google ScholarĀ 

  22. Xiao, Z., Prieto, D., Conrads, T.P., Veenstra, T.D., Issaq, H.J.: Proteomic patterns: their potential for disease diagnosis. Mol. Cell Endocrinol.Ā 230(1-2), 95ā€“106 (2005)

    ArticleĀ  Google ScholarĀ 

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Quandt, A., Ploner, A., Tan, C.S., Lehtiƶ, J., Pawitan, Y. (2005). ProSpect: An R Package for Analyzing SELDI Measurements Identifying Protein Biomarkers. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_13

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  • DOI: https://doi.org/10.1007/11560500_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29104-6

  • Online ISBN: 978-3-540-31726-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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