A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping. (27th November 2019)

• Record Type: Journal Article
• Authors: Sathyanarayanan, Anita; Gupta, Rohit; Thompson, Erik W; Nyholt, Dale R; Bauer, Denis C; Nagaraj, Shivashankar H
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 6(2020)
• Page Start: 1920
• Page End: 1936
• Publication Date: 2019-11-27
• Subjects: multi-omics data -- cancer -- multi-staged integration -- meta-dimensional integration -- tools evaluation; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz121
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.