Classification of gene signatures for their information value and functional redundancy. (December 2018)

• Record Type: Journal Article
• Authors: Cantini, Laura; Calzone, Laurence; Martignetti, Loredana; Rydenfelt, Mattias; Blüthgen, Nils; Barillot, Emmanuel; Zinovyev, Andrei
• Is Part Of: Npj systems biology and applications. Volume 4(2018)
• Page Start: 1
• Page End: 11
• Publication Date: 2018-12
• Subjects: Systems biology -- Periodicals; 570.113
• DOI: 10.1038/s41540-017-0038-8
• Languages: English
• ISSNs: 2056-7189

Abstract

Abstract Gene signatures are more and more used to interpret results of omics data analyses but suffer from compositional (large overlap) and functional (correlated read-outs) redundancy. Moreover, many gene signatures rarely come out as significant in statistical tests. Based on pan-cancer data analysis, we construct a restricted set of 962 signatures defined as informative and demonstrate that they have a higher probability to appear enriched in comparative cancer studies. We show that the majority of informative signatures conserve their weights for the genes composing the signature (eigengenes) from one cancer type to another. We finally construct InfoSigMap, an interactive online map of these signatures and their cross-correlations. This map highlights the structure of compositional and functional redundancies between informative signatures, and it charts the territories of biological functions. InfoSigMap can be used to visualize the results of omics data analyses and suggests a rearrangement of existing gene sets. Data-driven signature classification An informative collection of gene signatures for transcriptomic data analysis is constructed. The number of transcriptomic signatures grows fast and their collections are highly redundant that hampers omics data analyses interpretation. A computational biology team from Institut Curie led by Andrei Zinovyev selected a collection of 962 gene signatures shown to be informative for cancer studies and reflecting mechanisms of cancer progression. The signatures were filtered from a large compendium without requiring any manual curation by experts through a large-scale unbiased analysis of pancancer data. They have much higher chance to obtain significant enrichment scores in a comparative trancriptomic study. The authors integrated the 962 signatures into InfoSigMap, a new data visualization resource for the interpretation of the results of omics data analyses, which facilitates getting an insight into the mechanisms driving cancer.