TY - JOUR AU - Kim, Tae Rim AU - Jeong, Hyun-Hwan AU - Sohn, Kyung-Ah PY - 2019 DA - 2019/07/11 TI - Topological integration of RPPA proteomic data with multi-omics data for survival prediction in breast cancer via pathway activity inference JO - BMC Medical Genomics SP - 94 VL - 12 IS - 5 AB - The analysis of integrated multi-omics data enables the identification of disease-related biomarkers that cannot be identified from a single omics profile. Although protein-level data reflects the cellular status of cancer tissue more directly than gene-level data, past studies have mainly focused on multi-omics integration using gene-level data as opposed to protein-level data. However, the use of protein-level data (such as mass spectrometry) in multi-omics integration has some limitations. For example, the correlation between the characteristics of gene-level data (such as mRNA) and protein-level data is weak, and it is difficult to detect low-abundance signaling proteins that are used to target cancer. The reverse phase protein array (RPPA) is a highly sensitive antibody-based quantification method for signaling proteins. However, the number of protein features in RPPA data is extremely low compared to the number of gene features in gene-level data. In this study, we present a new method for integrating RPPA profiles with RNA-Seq and DNA methylation profiles for survival prediction based on the integrative directed random walk (iDRW) framework proposed in our previous study. In the iDRW framework, each omics profile is merged into a single pathway profile that reflects the topological information of the pathway. In order to address the sparsity of RPPA profiles, we employ the random walk with restart (RWR) approach on the pathway network. SN - 1755-8794 UR - https://doi.org/10.1186/s12920-019-0511-x DO - 10.1186/s12920-019-0511-x ID - Kim2019 ER -