Improved quality control processing of peptide-centric LC-MS proteomics data.

ABSTRACT:

Motivation

In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimate

SUBMITTER: Matzke MM 

PROVIDER: S-EPMC3187650 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature