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Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data

Author

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  • Birkner Merrill D.

    (Division of Biostatistics, School of Public Health, University of California, Berkeley)

  • Hubbard Alan E.

    (Division of Biostatistics, School of Public Health, University of California, Berkeley)

  • van der Laan Mark J.

    (Division of Biostatistics, School of Public Health, University of California, Berkeley)

  • Skibola Christine F.

    (Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley)

  • Hegedus Christine M.

    (Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley)

  • Smith Martyn T.

    (Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley)

Abstract

A new data filtering method for SELDI-TOF MS proteomic spectra data is described. We examined technical repeats (2 per subject) of intensity versus m/z (mass/charge) of bone marrow cell lysate for two groups of childhood leukemia patients: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). As others have noted, the type of data processing as well as experimental variability can have a disproportionate impact on the list of ``interesting'' proteins (see Baggerly et al. (2004)). We propose a list of processing and multiple testing techniques to correct for 1) background drift; 2) filtering using smooth regression and cross-validated bandwidth selection; 3) peak finding; and 4) methods to correct for multiple testing (van der Laan et al. (2005)). The result is a list of proteins (indexed by m/z) where average expression is significantly different among disease (or treatment, etc.) groups. The procedures are intended to provide a sensible and statistically driven algorithm, which we argue provides a list of proteins that have a significant difference in expression. Given no sources of unmeasured bias (such as confounding of experimental conditions with disease status), proteins found to be statistically significant using this technique have a low probability of being false positives.

Suggested Citation

  • Birkner Merrill D. & Hubbard Alan E. & van der Laan Mark J. & Skibola Christine F. & Hegedus Christine M. & Smith Martyn T., 2006. "Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-24, April.
    • Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:11
    DOI: 10.2202/1544-6115.1198
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    References listed on IDEAS

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    1. van der Laan Mark J. & Dudoit Sandrine & Pollard Katherine S., 2004. "Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-27, June.
    2. Mark van der Laan & Sandrine Dudoit & Katherine Pollard, 2004. "Multiple Testing. Part III. Procedures for Control of the Generalized Family-Wise Error Rate and Proportion of False Positives," U.C. Berkeley Division of Biostatistics Working Paper Series 1140, Berkeley Electronic Press.
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