A statistical model to identify differentially expressed proteins in 2D PAGE gels.

Journal Article

Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments, show that the likelihood model has higher statistical power than standard statistical approaches to detect differentially expressed proteins. An R package, Slider (Statistical Likelihood model for Identifying Differential Expression in R), is freely available at http://www.cebl.auckland.ac.nz/slider.php.

Full Text

Duke Authors

Cited Authors

  • Wu, SH; Black, MA; North, RA; Atkinson, KR; Rodrigo, AG

Published Date

  • September 2009

Published In

Volume / Issue

  • 5 / 9

Start / End Page

  • e1000509 -

PubMed ID

  • 19763172

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1000509

Language

  • eng

Conference Location

  • United States