Generalized linear and mixed models for label-free shotgun proteomics

Matthew C. Leitch, Indranil Mitra, Rovshan G. Sadygov

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Label-free shotgun proteomics holds great promise, and has already had some great successes in pinpointing which proteins are up or down regulated in certain disease states. However, there are still some pressing issues concerning the statistical analysis of label-free shotgun proteomics, and this field has not enjoyed as much dedication of statistical research towards it as microarray research has. Here we reapply previously used statistical methods, the QSpec and quasi-Poisson, as well as apply the negative binomial distribution to both a control data set and a data set with known differential expression to determine the successes and failure of each of the three methods.

Original languageEnglish (US)
Pages (from-to)89-98
Number of pages10
JournalStatistics and its Interface
Issue number1
StatePublished - 2012


  • Count data
  • FDR
  • Generalized linear models
  • Label-free quantitative proteomics
  • Mixture model
  • Negative binomial model
  • Quasi-Poisson model
  • Spectral count
  • Statistical models
  • p-values

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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