@article{7d31f328c4a74075880decd251bb4c21,
title = "Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data",
abstract = "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 biologic 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 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.",
keywords = "bioinformatics, cross-platform analysis, gene expression, mass spectrometry, microarray hybridization, next-generation sequencing, pathway activation strength, proteome, signaling pathways, transcriptome",
author = "Nicolas Borisov and Maria Suntsova and Maxim Sorokin and Andrew Garazha and Olga Kovalchuk and Alexander Aliper and Elena Ilnitskaya and Ksenia Lezhnina and Mikhail Korzinkin and Victor Tkachev and Vyacheslav Saenko and Yury Saenko and Sokov, {Dmitry G.} and Gaifullin, {Nurshat M.} and Kirill Kashintsev and Valery Shirokorad and Irina Shabalina and Alex Zhavoronkov and Bhubaneswar Mishra and Cantor, {Charles R.} and Anton Buzdin",
note = "Funding Information: aCentre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre “Kurchatov Institute”, Moscow, Russia; bDepartment of R&D, First Oncology Research and Advisory Center, Moscow, Russia; cDepartment of R&D, Center for Biogerontology and Regenerative Medicine, Moscow, Russia; dLaboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia; eGroup for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia; fDepartment of R&D, OmicsWay Corporation, Walnut, CA, USA; gDepartment of Biological Sciences, University of Lethbridge, Lethbridge, AB, Canada; hTechnological Research Institute S.P. Kapitsa, Ulyanovsk State University, Ulyanovsk, Russia; iChemotherapy Department, Moscow 1st Oncological Hospital, Moscow, Russia; jFaculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, Russia; kDepartment of Oncology, Russian Medical Postgraduate Academy, Moscow, Russia; lChemotherapy Department, Moscow Oncological Hospital 62, Stepanovskoye, Russia; mFaculty of Mathematics and Information Technologies, Petrozavodsk State University, Petrozavodsk, Russia; nCourant Institute, New York University, New York, NY, USA; oDepartment of Biomedical Engineering, Boston University, Boston, MA, USA Funding Information: The work was supported by the internal research grant of National Research Centre “Kurchatov Institute”, Moscow, Russia, as well as by the Presidium of the Russian Academy of Sciences program “Biodiversity”. The authors thank the First Oncology Research and Advisory Center (Moscow, Russia) for the support in preparation of this manuscript. We would like to thank Alex Kim and ASUS for equipment and support of this research.",
year = "2017",
month = oct,
day = "2",
doi = "10.1080/15384101.2017.1361068",
language = "English (US)",
volume = "16",
pages = "1810--1823",
journal = "Cell Cycle",
issn = "1538-4101",
publisher = "Landes Bioscience",
number = "19",
}