title: Studying the effects of correlation on protein selection in proteomics data.
abstract: Recently, Efron (2007) provided methods for assessing the effect of correlation on false discovery rate (FDR) in large-scale testing problems in the context of microarray data. Although FDR procedure does not require independence of the tests, existence of correlation grossly under- or overestimates the number of critical genes. Here, we briefly review Efron's method and apply it to a relatively smaller spectrometry proteomics data. We show that even here the correlation can affect the FDR values and the number of proteins declared as critical.
authors.affiliation: Department of Mathematics, Hampton University, Hampton, VA, USA.
authors.forename: Savita
authors.initials: S
authors.lastname: Venkataramani
authors.forename: Dayanand N
authors.initials: DN
authors.lastname: Naik
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D013058:Mass Spectrometry; D040081:Protein Array Analysis; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000706:statistics & numerical data; D013223:Statistics as Topic
chemical_list: D011506:Proteins