Integrated Analysis of Gene Expression and Protein–Protein Interaction with Tensor Decomposition

Abstract

Integration of gene expression (GE) and protein–protein interaction (PPI) is not straightforward because the former is provided as a matrix, whereas the latter is provided as a network. In many cases, genes processed with GE analysis are refined further based on a PPI network or vice versa. This is hardly regarded as a true integration of GE and PPI. To address this problem, we proposed a tensor decomposition (TD)-based method that can integrate GE and PPI prior to any analyses where PPI is also formatted as a matrix to which singular value decomposition (SVD) is applied. Integrated analyses with TD improved the coincidence between vectors attributed to samples and class labels over 27 cancer types retrieved from The Cancer Genome Atlas Program (TCGA) toward five class labels. Enrichment using genes selected with this strategy was also improved with the integration using TD. The PPI network associated with the information on the strength of the PPI can improve the performance than PPI that stores only if the interaction exists in individual pairs. In addition, even restricting genes to the intersection of GE and PPI can improve coincidence and enrichment.

Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction

“…We do not have to have any drug or disease specific information (e.g., differential expression between healthy controls and patients) and we can identify druggene-disease information in fully data-driven manner. Recently, we applied TD based unsupervised FE [3, 4] to integrate PPI with gene expression of cancers [5]. Then we have found that integrated analysis could enhance the coincidence with clinical labels.…”