Alumnus Dipak Dey Seminar

Friday, March 30, 2007 
01:30 PM in BRNG 1238
Professor Dipak Dey
Head Department of Statistics, University of Connecticut

A Semiparametric Bayesian Approach for the Development of Metabonomic Profile


The discovery and validation of biomarkers is an important step towards the development of criteria for early diagnosis of disease status. Recently electro spray ionization (ESI) and matrix assisted laser desorption (MALDI) time-of-flight (TOF) mass spectrometry have been used to identify biomarkers both in Proteomics and Metabonomics studies. Data sets generated from mass spectrometers in such studies are generally very large in size and thus require the use of sophisticated statistical techniques to glean useful information. Most of the recent attempts to process this data are to model each compound's intensity either discretely by positional (mass to charge ratio) clustering or through each compounds' own intensity distribution. The existing literature suggests that due to inherent calibration variability of the mass spectrometer, these discrete approaches lack generality and reproducibility to be successfully used outside the realm of respective studies. Traditionally, different data processing steps involving noise removal, background elimination, m/z alignment, are generally carried out separately resulting in unsatisfactory propagation of signals to the final model. It is more intuitive to develop models for patterns rather than discrete points following the basic principle of ''borrowing strength'' for such a scenario. In the present study a novel semi-parametric approach has been developed to distinguish urinary metabolic profiles in a group of traumatic patients from those of a control group consisting of normal individuals. Data sets obtained from the replicates of a single subject were used to develop a functional profile through Dirichlet mixture of beta processes. This functional profile is flexible enough to accommodate variability of the instrument and the inherent variability of each individual, thus simultaneously addressing different sources of systematic error. To address instrument variability, all data sets have been analyzed in replicates, an important issue ignored by most of the studies of similar kind in the past. We have proposed different models by prescribing different choices of centering function to capture non-standard shape of the profiles. Different model comparisons were performed to select the best model for each subject. The inherent assumption that traumatic individuals will show irregular patterns in their profile was checked through an intensity function compared with normal individuals. The m/z values in the window of the irregular pattern are then further recommended for biomarker discovery associated with trauma.

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