Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data |
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| Authors: | Nicolas Borisov Maria Suntsova Maxim Sorokin Andrew Garazha Olga Kovalchuk Alexander Aliper |
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| Affiliation: | 1. Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre “Kurchatov Institute”, Moscow, Russia;2. Department of R&3. D, First Oncology Research and Advisory Center, Moscow, Russia;4. Department of R&5. D, Center for Biogerontology and Regenerative Medicine, Moscow, Russia;6. Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia;7. Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia;8. D, OmicsWay Corporation, Walnut, CA, USA;9. Department of Biological Sciences, University of Lethbridge, Lethbridge, AB, Canada;10. Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia |
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| 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. |
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