Date Awarded
2006
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.)
Department
Physics
Advisor
Eugene R Tracy
Abstract
The high throughput capabilities of protein mass fingerprints measurements have made mass spectrometry one of the standard tools for proteomic research, such as biomarker discovery. However, the analysis of large raw data sets produced by the time-of-flight (TOF) spectrometers creates a bottleneck in the discovery process. One specific challenge is the preprocessing and identification of mass peaks corresponding to important biological molecules. The accuracy of mass assignment is another limitation when comparing mass fingerprints with databases.;We have developed an automated peak picking algorithm based on a maximum likelihood approach that effectively and efficiently detects peaks in a time-of-flight secondary ion mass spectrum. This approach produces maximum likelihood estimates of peak positions and amplitudes, and simultaneously develops estimates of the uncertainties in each of these quantities. We demonstrate that a Poisson process is involved for time-of-flight secondary ion mass spectrometry (TOF-SIMS) and the algorithm takes the character of the Poisson noise into account.;Though this peak picking algorithm was initially developed for TOF-SIMS spectra, it can be extended to other types of TOF spectra as soon as the correct noise characteristics are considered. We have developed a peak alignment procedure that aligns peaks in different spectra. This is a crucial step for multivariate analysis. Multivariate analysis is often used to distill useful information from complex spectra.;We have designed a TOF-SIMS experiment that consists of various mixtures of three bio-molecules as a model for more complicated biomarker discovery. The peak picking algorithm is applied to the collected spectra. The algorithm detects peaks in the spectra repeatably and accurately. We also show that there are patterns in the spectra of pure biomolecules samples. Furthermore, we show it is possible to infer the concentration ratios in the mixture samples by checking the strength of the patterns.
DOI
https://dx.doi.org/doi:10.21220/s2-h4q6-pb96
Rights
© The Author
Recommended Citation
Chen, Haijian, "Automated peak identification for time -of -flight mass spectroscopy" (2006). Dissertations, Theses, and Masters Projects. William & Mary. Paper 1539623489.
https://dx.doi.org/doi:10.21220/s2-h4q6-pb96
