NDLI: Application of the Cox Proportional Hazards Model for the Quantitative Analysis of LC-MS Proteomics Data

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Application of the Cox Proportional Hazards Model for the Quantitative Analysis of LC-MS Proteomics Data

Content Provider	Semantic Scholar
Author	Arreola, Ivan Han, David
Copyright Year	2019
Abstract	Along with quantitative, analytical genomics, proteomics continues to be a growing field for determining the gene and cellular functions at the protein level. As the liquid chromatography mass spectrometry (LCMS) experiments produce protein peak intensities data, statistical and computational techniques are required to conduct quantitative, analytical proteomics. The LCMS proteomics data often have large quantities of missing peak intensities due to censoring of the low-abundance spectral features. Because of this, the observed peak intensities from the LC-MS method are all positive, skewed, and often left-censored. The classical survival analysis methods are ideal to detect differentially expressed proteins among different groups. These methods include the non-parametric rank sum (RS) tests such as the KolmogorovSmirnov (KS) and Wilcoxon-Mann-Whitney (WMW) tests, parametric survival models such as the accelerated failure time (AFT) model with popular lifetime distributions; log-normal (LN), log-logistic (LL), and Weibull (W) for modeling the peak intensity data. As an alternative approach, here we propose the Cox proportional hazards (PH) method, a popular semi-parametric model for modeling survival data. The proposed regression-based method allows for leniency on the hazard function by alleviating the requirements of distribution-specific hazard functions. With the hopes of gaining more insightful biological information for cellular functions at the protein level, the statistical properties of each method are investigated through a simulation study and an application to the Type I diabetes dataset.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://research.utsa.edu/UG_Journal/files/vol5/JURSW.5.Arreola.2.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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