A hybrid approach to protein differential expression in mass spectrometry-based proteomics. Academic Article uri icon


  • MOTIVATION: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. RESULTS: We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of 'presence/absence,' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. 'one-state' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate. AVAILABILITY: All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.

author list (cited authors)

  • Wang, X., Anderson, G. A., Smith, R. D., & Dabney, A. R

citation count

  • 24

complete list of authors

  • Wang, Xuan||Anderson, Gordon A||Smith, Richard D||Dabney, Alan R

publication date

  • January 2012