ITOP: Inferring the topology of omics data

Nanne Aben, Johan A. Westerhuis, Yipeng Song, Henk A.L. Kiers, Magali Michaut, Age K. Smilde, Lodewyk F.A. Wessels*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Motivation In biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets. Results We present iTOP, a methodology to infer a topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics. Availability and implementation An implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary Material. Supplementary information Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)i988-i996
Issue number17
Publication statusPublished - 1 Sept 2018
Externally publishedYes


The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC synergy grant agreement n° 319661 COMBATCANCER.

FundersFunder number
Seventh Framework Programme319661
European Research Council
Seventh Framework Programme


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