Sciweavers

Share
BMCBI
2010

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

10 years 4 months ago
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...
Vincent A. Emanuele II, Brian M. Gurbaxani
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Vincent A. Emanuele II, Brian M. Gurbaxani
Comments (0)
books