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 meth

SUBMITTER: Borisov N 

PROVIDER: S-EPMC5628641 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature