TY - JOUR T1 - Data Aggregation at the Level of Molecular Pathways Improves Stability of Experimental Transcriptomic and Proteomic Data JF - bioRxiv DO - 10.1101/076620 SP - 076620 AU - Nicolas Borisov AU - Maria Suntsova AU - Andrew Garazha AU - Ksenia Lezhnina AU - Olga Kovalchuk AU - Alexander Aliper AU - Elena Ilnitskaya AU - Maxim Sorokin AU - Mihkail Korzinkin AU - Vyacheslav Saenko AU - Yury Saenko AU - Dmitry G. Sokov AU - Nurshat M. Gaifullin AU - Kirill Kashintsev AU - Valery Shirokorad AU - Irina Shabalina AU - Alex Zhavoronkov AU - Bhubaneswar Mishra AU - Charles R. Cantor AU - Anton Buzdin Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/09/21/076620.abstract N2 - High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biological samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the five alternative methods, also evaluated by their capacity to retain meaningful features of biological samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses. ER -