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A simplified approach for analysis of SELDI-TOF mass spectrometry data.
Yan SD, Vernon SD, Unger ER, Karem KLA simplified approach for analysis of SELDI-TOF mass spectrometry data
Applied Genomics and Proteomics 2003; 2:71-77.
Summary
As part of our CFS molecular epidemiology effort, we have initiated a program to study protein expression profiles of CFS patients. While the measure of messenger RNA (mRNA) levels (transcription) represents the genetic potential of a cell, most attributes of biological systems stem from synthesis of proteins (translation). Our current effort utilizes mass spectrometry to profile the total protein composition of blood to attempt to define protein patterns that discriminate CFS cases from controls. The method we use, surface-enhanced laser desorption ionization (SELDI) utilizes a retention chromatography chip to capture specific families of proteins from a whole blood sample. This surface enhancement facilitates fractionation of blood samples to allow detection of specific classes of proteins. Although SELDI has the potential to produce protein profiles that contain patterns capable of distinguishing markers of CFS, a single analysis may identify more than 15,000 proteins and the data analysis required to discern the differentiating patterns poses a major challenge. This first publication from our proteomics program describes a simplified approach to the analysis of SELDI data. In this study we analyzed two publicly available data sets designed to search for serum biomarkers of ovarian and prostatic cancer. The methods we used yielded complementary information that can be used to quickly summarize SELDI data, classify samples, and identify proteins with the greatest potential as biomarkers.
Abstract
This study demonstrates the feasibility of a simplified approach to evaluating SELDI-TOF (surface enhanced laser desorption ionization time of flight) mass spectrometry data by analyzing two publicly available datasets. The three-dimensional representation of multidimensional scaling (MDS) allowed visualization of group separation based on all intensities at all m/z values. To discover m/z regions accounting for group segregation, we used three statistical tools (the parametric t test and separation factor as well as the nonparametric permutation test) to reduce data complexity and result in a simplified m/z profile emphasizing group discrimination. We incorporated the separation factor into a non-iterative supervised algorithm for classification. Increasing the separation factor threshold improved both sensitivity and specificity of classification. MDS and statistical tests yield complementary information that can be used to quickly scan or summarize SELDI-TOF data, classify samples and identify m/z regions containing proteins with the greatest potential as biomarkers.
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