Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data
KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/597597
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AbstractMOTIVATION: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics 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 and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. RESULTS: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. AVAILABILITY: The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. CONTACT: email@example.com.
CitationTekwe CD, Carroll RJ, Dabney AR (2012) Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data. Bioinformatics 28: 1998–2003. Available: http://dx.doi.org/10.1093/bioinformatics/bts306.
SponsorsC.D.T. was supported by a postdoctoral training grant from the National Cancer Institute (R25T - 090301). R.J.C. was supported by a grant from the National Cancer Institute (R27-CA057030). This publication is based in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
PublisherOxford University Press (OUP)
PubMed Central IDPMC3400956
CollectionsPublications Acknowledging KAUST Support
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