Quadratic variance models for adaptively preprocessing SELDI-TOF mass spectrometry data

Background: Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI) is a proteomics tool for biomarker discovery and other high throughput applications. Previous studies have identified various areas for improvement in preprocessing algorithms used for protein peak detection. Bottom-up approaches to preprocessing that emphasize modeling SELDI data acquisition are promising avenues of research to find the needed improvements in reproducibility. Results: We studied the properties of the SELDI detector intensity response to matrix only runs. The intensity fluctuations and noise observed can be characterized by a natural exponential family with quadratic variance function (NEF-QVF) class of distributions. These include as special cases many common distributions arising in practice (e.g.- normal, Poisson). Taking this model into account, we present a modified Antoniadis-Sapatinas wavelet denoising algorithm as the core of our preprocessing program, implemented...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Vincent A. Emanuele II, Brian M. Gurbaxani
• Keywords: Biomarker Discovery, BMCBI 2010, Preprocessing, SELDI