title: Differences in protein-protein association networks for lung adenocarcinoma: A retrospective study.
abstract: Various methods to determine the connectivity scores between groups of proteins associated with lung adenocarcinoma are examined. Proteins act together to perform a wide range of functions within biological processes. Hence, identification of key proteins and their interactions within protein networks can provide invaluable information on disease mechanisms. Differential network analysis provides a means of identifying differences in the interactions among proteins between two networks. We use connectivity scores based on the method of partial least squares to quantify the strength of the interactions between each pair of proteins. These scores are then used to perform permutation-based statistical tests. This examines if there are significant differences between the network connectivity scores for individual proteins or classes of proteins. The expression data from a study on lung adenocarcinoma is used in this study. Connectivity scores are computed for a group of 109 subjects who were in the complete remission and as well as for a group of 51 subjects whose cancer had progressed. The distributions of the connectivity scores are similar for the two networks yet subtle but statistically significant differences have been identified and their impact discussed.
authors.affiliation: Louisville Collegiate School and Department of Mathematics, University of Louisville.
authors.forename: Anisha
authors.initials: A
authors.lastname: Datta
authors.affiliation: Department of Bioinformatics and Biostatistics, University of Louisville.
authors.forename: Sinjini
authors.initials: S
authors.lastname: Sikdar
authors.affiliation: Department of Mathematics, University of Louisville, Louisville.
authors.forename: Ryan
authors.initials: R
authors.lastname: Gill
keywords: association networks; expression data; lung adenocarcinoma; networks; protein-protein; protein-protein interaction