title: Issues of processing and multiple testing of SELDI-TOF MS proteomic data.
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.
authors.affiliation: Division of Biostatistics, School of Public Health, University of California, Berkeley, USA. mbirkner@stat.berkeley.edu
authors.forename: Merrill D
authors.initials: MD
authors.lastname: Birkner
authors.forename: Alan E
authors.initials: AE
authors.lastname: Hubbard
authors.forename: Mark J
authors.initials: MJ
authors.lastname: van der Laan
authors.forename: Christine F
authors.initials: CF
authors.lastname: Skibola
authors.forename: Christine M
authors.initials: CM
authors.lastname: Hegedus
authors.forename: Martyn T
authors.initials: MT
authors.lastname: Smith
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D001854:Bone Marrow Cells / Q000378:metabolism; D002648:Child; D003627:Data Interpretation, Statistical; D006801:Humans; D007951:Leukemia, Myeloid / Q000378:metabolism*; D009363:Neoplasm Proteins / Q000378:metabolism*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000378:metabolism*; D011336:Probability; D040901:Proteomics / Q000379:methods*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D009363:Neoplasm Proteins