Modularity Analysis of Bipartite Networks and Multivariate ANOVA for Identification of Differentially Expressed Proteins in a Mouse Model of Down Syndrome

Down Syndrome (DS) is one of the most common disorders caused by the presence of an extra copy of chromosome 21. It has been shown that the expression of various genes located on chromosomes other than the extra 21 chromosomes is affected in DS. Given the practical and ethical difficulties in human tissue studies, the Ts65Dn mouse model has been widely used in DS research. In this study, we propose a pipeline composed of a supervised learning approach, modularity analysis of a bipartite network, and multivariate analysis of variance (MANOVA), for identification of differentially expressed proteins (DEP) among different classes of mice models. The proposed pipeline is tested using the expression levels of 77 proteins in eight different classes of mice models. The data includes the protein expression measurements for 34 trisomic Ts65Dn and 38 control mice. Each group is broken up into four classes based on either being stimulated for learning or not, each injected with memantine or saline. The previously proposed approaches have been unable to identify DEP among all of the eight classes simultaneously. Here, we show that our proposed pipeline can successfully identify the set of proteins expressed differently among all the eight classes. The findings of this study can inform the study of learning responses to different treatments and protein-treatment associations in DS. Also, the proposed pipeline can be adopted to identify DEP in DS or other diseases and health conditions, which can consequently inform the development of improved personalized treatment and management strategies.