Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data
Birkner Merrill D.,
Hubbard Alan E.,
J. van der Laan Mark,
Skibola Christine F.,
Hegedus Christine M. and
Smith Martyn T.
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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
J. van der Laan Mark: 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
Statistical Applications in Genetics and Molecular Biology, 2006, vol. 5, issue 1, 1-24
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.
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