On the beta-binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics

• DOI: 10.1093/bioinformatics/btp677
• Publication Date: 2009-12-10T01:46:35Z
• Authors: Thang V. Pham; Sander R. Piersma; Marc Warmoes; Connie R. Jimenez
• Keywords: False Discovery Rate; Binomial distribution; Sample (material); Multiple comparisons problem

Abstract Motivation: Spectral count data generated from label-free tandem mass spectrometry-based proteomic experiments can be used to quantify protein's abundances reliably. Comparing spectral different sample groups such as control and disease is an essential step in statistical analysis for the determination of altered protein level biomarker discovery. The Fisher's exact test, G-test, t-test local-pooled-error technique (LPE) are commonly differential data. However, our initial two cancer studies show that current methods unable declare at 95% confidence a number markers have been judged on basis biology numbers. A shortcoming these tests they do not take into account within- between-sample variations together. Hence, aim improve upon existing techniques by incorporating both variations. Result: We propose use beta-binomial distribution test significance expressed counts proteomics. naturally normalizes total count. Experimental results performs favorably comparison with other several datasets terms true detection rate false positive rate. In addition, it applied one or more replicates, multiple condition comparisons. Finally, we implemented software package parameter estimation models associated tests. Availability implementation: R freely available download http://www.oncoproteomics.nl/. Contact: [e-mail] Supplementary information: Bioinformatics online.