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Issues of processing and multiple testing of SELDI-TOF MS proteomic data.
Merrill D Birkner1, Alan E Hubbard, Mark J van der Laan
1Division of Biostatistics, School of Public Health, University of California, Berkeley, USA. mbirkner@stat.berkeley.edu
Summary
This study introduces a novel data filtering method for Surface-Enhanced Laser Desorption/Ionization-Time of Flight Mass Spectrometry (SELDI-TOF MS) proteomic spectra. The new algorithm enhances the identification of differentially expressed proteins in childhood leukemia subtypes.
Area of Science:
- Biochemistry
- Proteomics
- Bioinformatics
Background:
- Proteomic data analysis, particularly from SELDI-TOF MS, is sensitive to data processing and experimental variability.
- Identifying differentially expressed proteins requires robust statistical methods to minimize false positives.
Purpose of the Study:
- To develop and present a new, statistically driven data filtering method for SELDI-TOF MS proteomic spectra.
- To improve the accuracy and reliability of identifying proteins with significantly different expression levels between biological groups, such as disease subtypes.
Main Methods:
- The proposed method incorporates several techniques: background drift correction, smooth regression with cross-validated bandwidth selection for filtering, peak finding algorithms, and multiple testing correction.
- Applied to bone marrow cell lysate from childhood acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) patients.
Main Results:
- The developed algorithm yields a list of proteins (identified by mass-to-charge ratio, m/z) exhibiting significantly different average expression between disease groups.
- The methodology aims to provide a sensible and statistically sound approach to protein expression analysis.
Conclusions:
- The described data filtering and multiple testing procedures offer a reliable method for identifying differentially expressed proteins in proteomic studies.
- When applied without confounding biases, this technique is expected to yield a low false positive rate, enhancing the discovery of disease-specific protein biomarkers.