INDEED: R package for network based differential expression analysis

With recent advancement of omics technologies, fueled by decreased cost and increased number of available datasets, computational methods for differential expression analysis are sought to identify disease-associated biomolecules. Conventional differential expression analysis methods (e.g. student's t-test, ANOVA) focus on assessing mean and variance of biomolecules in each biological group. On the other hand, network-based approaches take into account the interactions between biomolecules in choosing differentially expressed ones. These interactions are typically evaluated by correlation methods that tend to generate over-complicated networks due to many seemingly indirect associations. In this paper, we introduce a new R/Bioconductor package INDEED that allows users to construct a sparse network based on partial correlation, and to identify biomolecules that have significant changes both at individual expression and pairwise interaction levels. We applied INDEED for analysis of two omic datasets acquired in a cancer biomarker discovery study to help rank disease-associated biomolecules. We believe biomolecules selected by INDEED lead to improved sensitivity and specificity in detecting disease status compared to those selected by conventional statistical methods. Also, INDEED's framework is amenable to further expansion to integrate networks from multi-omic studies, thereby allowing selection of reliable disease-associated biomolecules or disease biomarkers.