{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T04:08:17Z","timestamp":1745208497510},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"15","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: 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\u2013normal 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\u2013Smirnov and Wilcoxon\u2013Mann\u2013Whitney 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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Contact: \u00a0ctekwe@stat.tamu.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts306","type":"journal-article","created":{"date-parts":[[2012,5,25]],"date-time":"2012-05-25T01:19:20Z","timestamp":1337908760000},"page":"1998-2003","source":"Crossref","is-referenced-by-count":16,"title":["Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data"],"prefix":"10.1093","volume":"28","author":[{"given":"Carmen D.","family":"Tekwe","sequence":"first","affiliation":[{"name":"Department of Statistics, 3143 TAMU, College Station, TX 77843-3143, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raymond J.","family":"Carroll","sequence":"additional","affiliation":[{"name":"Department of Statistics, 3143 TAMU, College Station, TX 77843-3143, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan R.","family":"Dabney","sequence":"additional","affiliation":[{"name":"Department of Statistics, 3143 TAMU, College Station, TX 77843-3143, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2012,5,24]]},"reference":[{"key":"2023012512453804900_B1","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/nature01511","article-title":"Mass spectrometry-based proteomics","volume":"422","author":"Aebersold","year":"2003","journal-title":"Nature"},{"key":"2023012512453804900_B2","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1038\/sj.ijo.0802149","article-title":"Apolipoprotein E kinetics: influence of insulin resistance and type 2 diabetes","volume":"26","author":"Bach-Ngohou","year":"2002","journal-title":"Int. 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