Overcoming the matched-sample bottleneck: an orthogonal approach to integrate omic data

Abstract

MicroRNAs (miRNAs) are small non-coding RNA molecules whose primary function is to regulate the expression of gene products via hybridization to mRNA transcripts, resulting in suppression of translation or mRNA degradation. Although miRNAs have been implicated in complex diseases, including cancer, their impact on distinct biological pathways and phenotypes is largely unknown. Current integration approaches require sample-matched miRNA/mRNA datasets, resulting in limited applicability in practice. Since these approaches cannot integrate heterogeneous information available across independent experiments, they neither account for bias inherent in individual studies, nor do they benefit from increased sample size. Here we present a novel framework able to integrate miRNA and mRNA data (vertical data integration) available in independent studies (horizontal meta-analysis) allowing for a comprehensive analysis of the given phenotypes. To demonstrate the utility of our method, we conducted a meta-analysis of pancreatic and colorectal cancer, using 1,471 samples from 15 mRNA and 14 miRNA expression datasets. Our two-dimensional data integration approach greatly increases the power of statistical analysis and correctly identifies pathways known to be implicated in the phenotypes. The proposed framework is sufficiently general to integrate other types of data obtained from high-throughput assays.

Tannic acid inhibits lipid metabolism and induce ROS in prostate cancer cells

“…The purified mRNA samples were subjected to gene microarray studies by utilizing Clariom S Human gene array (Affymterix, Santa Clara, CA, USA) per manufacturing instructions. The results of the microarray studies were validated and analyzed for variation in pathways of through iPathway guide analysis (Advaita Corporation, Plymouth, MI, USA) 66 . This method scores the pathway by the level of protein expression in cells during drug treatments through Impact Analysis method 67,68 .…”

A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures

“…The approaches in the second category combine sample-matched studies from multiple data types and provide biomarkers that can capture data heterogeneity present across the omic layers. Integrating such information from multiple data types is essential for obtaining a comprehensive overview of the given biological system and thought to provide better prognostic markers (Berger et al, 2013 ; Kristensen et al, 2014 ; Nguyen et al, 2016b ). For instance, it has been shown that integrating miRNA and mRNA expression profiles results in greater statistical power and better understanding of the underlying disease phenomena, both in the context of biomarker discovery (Volinia and Croce, 2013 ; Wotschofsky et al, 2016 ) and pathway analysis (Calura et al, 2014 ; Vlachos et al, 2015 ; Alaimo et al, 2016 ; Diaz et al, 2016 ).…”

Methods to increase reproducibility in differential gene expression via meta-analysis

“…One of the ways to improve reproducibility is integrating multiple microarray datasets via gene expression meta-analysis, which has proven useful in practice because it produces results that validate in independent datasets (14–22). Gene expression meta-analysis is often performed using data from growing public repositories such as the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) and the European Bioinformatics Institute's (EBI) ArrayExpress, which together house over 70 000 datasets composed of over 1.7 million assays.…”