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Brief Bioinform. 2018 Apr 23. doi: 10.1093/bib/bby027. [Epub ahead of print]

Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data.

Author information

1
Institute of Computational Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010 Graz, Austria. Tel.:+43 316 873 5335; E-mail: B.Pucher@tugraz.at.
2
Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Ave, Boston MA 02115, USA. Tel.:+1 617 525 4670; E-mail: ozeleznik@bwh.harvard.edu.
3
Institute of Computational Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010 Graz, Austria. Tel.:+43 316 873 5343; E-mail: Gerhard.Thallinger@tugraz.at.

Abstract

Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1-99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison.

PMID:
29688321
DOI:
10.1093/bib/bby027

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