DocumentCode :
3542614
Title :
Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data
Author :
Tekwe, Carmen D. ; Dabney, Alan R. ; Carroll, Raymond J.
Author_Institution :
Dept. of Stat., Texas A & M Univ., College Station, TX, USA
fYear :
2011
fDate :
4-6 Dec. 2011
Firstpage :
97
Lastpage :
100
Abstract :
Protein abundance in quantitative proteomics is often based on observed spectral features derived from LC-MS experiments. Peak intensities are largely non-Normal in distribution. Furthermore, LC-MS data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model, accelerated failure time model with the Weibull distribution were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated data set.
Keywords :
Weibull distribution; biology computing; proteins; proteomics; LC-MS proteomics data; Weibull distribution; observed spectral features; protein abundance; quantitative analysis; standard statistical techniques; statistical operating characteristics; survival analysis methodology; Data models; Diabetes; Peptides; Proteins; Proteomics; Weibull distribution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genomic Signal Processing and Statistics (GENSIPS), 2011 IEEE International Workshop on
Conference_Location :
San Antonio, TX
ISSN :
2150-3001
Print_ISBN :
978-1-4673-0491-7
Electronic_ISBN :
2150-3001
Type :
conf
DOI :
10.1109/GENSiPS.2011.6169453
Filename :
6169453
Link To Document :
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