Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.
All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.
Proteomics Laboratory, Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA. david.friedman@vanderbilt.edu
David B
DB
Friedman
D016480:Helicobacter pylori / Q000378:metabolism; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D040901:Proteomics / Q000379:methods*; D059629:Signal-To-Noise Ratio; D057825:Two-Dimensional Difference Gel Electrophoresis / Q000379:methods*