Comparative assessment of differential network analysis methods. (25th July 2016)

• Record Type: Journal Article
• Authors: Lichtblau, Yvonne; Zimmermann, Karin; Haldemann, Berit; Lenze, Dido; Hummel, Michael; Leser, Ulf
• Is Part Of: Briefings in bioinformatics. Volume 18:Number 5(2017:Sep.)
• Page Start: 837
• Page End: 850
• Publication Date: 2016-07-25
• Subjects: differential network analysis -- gene-regulatory networks -- miRNAs -- transcriptome data -- biomarker; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw061
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.