Investigation of Association Estimators in Network Inference Algorithms on Breast Cancer Proteomic Data

Abstract

In this study, association estimators applied in the network inference methods used to determine disease-related molecular interactions using breast cancer, which is the most common type of cancer in women, proteomic data were examined and hub genes in the gene-gene interaction network related to the disease were identified. Proteomic data of 901 breast cancer patients were generated using reverse phase protein array provided by The Cancer Proteome Atlas (TCPA) as a data set. Correlations and mutual information (MI) based estimators used in the literature were compared in the study, and WGCNA and minet R packages were used. As a result, it is seen that the MI based shrink estimator method has more successful results than the correlation-based adjacency function used in the estimation of biological networks in the WGCNA package. Achievement rates have ranged from 0.67 to 1.00 in the shrink estimation, with adjacency functions ranging from 0.33 to 0.86 for different module counts. In addition, hub genes and inferenced networks of successful results arc presented for the review of biologists.