Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet

Motivation: Peaks are the key information in Mass Spectrometry (MS) which has been increasingly used to discover diseases related proteomic patterns. Peak detection is an essential step for MS based proteomic data analysis. Recently, several peak detection algorithms have been proposed with good performance. However, in these algorithms, there are three major deficiencies: 1) because the noise is often removed as much as possible, the true signal could also be removed; 2) baseline removal step may get rid of true peaks and create new false peaks; 3) in peak quantification step, a threshold of signal-to-noise ratio (SNR) is usually used to remove false peaks, however, noise estimations in SNR calculation are often inaccurate in either time or wavelet domain. In this paper, we propose new algorithms to solve these problems. First, we use bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising step. Second, without baseline removal, zero-cross...

• Type: Journal
• Year: 2010
• Where: BIOINFORMATICS
• Authors: Nha Nguyen, Heng Huang, Soontorn Oraintara, An P. N. Vo
• Keywords: BIOINFORMATICS 2007, BIOINFORMATICS 2010, False Peaks, Step, True Peaks