Studying the effects of correlation on protein selection in proteomics data
Related Experiment Video
Updated: Jun 23, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
Published on: November 10, 2023
Savita Venkataramani1, Dayanand N Naik
1Department of Mathematics, Hampton University, Hampton, VA, USA.
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.
Related Concept Videos
Proteomics
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-protein Interfaces
Conservation of Protein Domains Over Different Proteins
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

