RT Journal Article
SR Electronic
T1 Data Aggregation at the Level of Molecular Pathways Improves Stability of Experimental Transcriptomic and Proteomic Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 076620
DO 10.1101/076620
A1 Nicolas Borisov
A1 Maria Suntsova
A1 Andrew Garazha
A1 Ksenia Lezhnina
A1 Olga Kovalchuk
A1 Alexander Aliper
A1 Elena Ilnitskaya
A1 Maxim Sorokin
A1 Mihkail Korzinkin
A1 Vyacheslav Saenko
A1 Yury Saenko
A1 Dmitry G. Sokov
A1 Nurshat M. Gaifullin
A1 Kirill Kashintsev
A1 Valery Shirokorad
A1 Irina Shabalina
A1 Alex Zhavoronkov
A1 Bhubaneswar Mishra
A1 Charles R. Cantor
A1 Anton Buzdin
YR 2016
UL http://biorxiv.org/content/early/2016/09/21/076620.abstract
AB 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.