Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data

作者：Borisov Nicolas^*; Suntsova Maria; Sorokin Maxim; Garazha Andrew; Kovalchuk Olga; Aliper Alexander; Ilnitskaya Elena; Lezhnina Ksenia; Korzinkin Mikhail; Tkachev Victor; Saenko Vyacheslav; Saenko Yury; Sokov Dmitry G; Gaifullin Nurshat M; Kashintsev Kirill; Shirokorad Valery; Shabalina Irina; Zhavoronkov Alex; Mishra Bhubaneswar; Cantor Charles R; Buzdin Anton

来源：Cell Cycle, 2017, 16(19): 1810-1823.

DOI：10.1080/15384101.2017.1361068

• bioinformatics
• gene expression
• transcriptome
• proteome
• microarray hybridization
• next-generation sequencing
• mass spectrometry
• signaling pathways
• pathway activation strength
• cross-platform analysis

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.

• 出版日期2017