Wessels, L. F. A. | Search bioRxiv

bioRxiv2018

iTOP: Inferring the Topology of Omics Data

MotivationIn 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.\n\nResultsWe present iTOP, a methodology to infera 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.\n\nAvailabilityAn 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.\n\nContacta.k.smilde@uva.nl and l.wessels@nki.nl\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Delineating feedback activity in the MAPK and AKT pathways using feedback-enabled Inference of Signaling Activity

An important aspect of cellular signaling networks is the existence of feedback mechanisms. However, due to the complexity of signaling networks, as well as the presence of multiple interrelated feedback events, it can be difficult to identify which signaling routes are active in any particular context. We have previously shown that Inference of Signaling Activity (ISA) can be a useful method to study steady-state oncogenic signaling across different cell lines and inhibitor treatments. However, ISA did not explicitly include feedback signaling events. Incorporating feedback will increase the complexity and computational cost of the model, and more data is likely to be needed to infer feedback activities. Here, we developed feedback-ISA (f-ISA), an extension of the ISA modeling approach which incorporates feedback signaling events. It also includes integrated batch correction in order to fit the models to multiple, independent datasets simultaneously. We find that the identifiability of feedback activities can be counter-intuitive, which shows the importance of analyzing the full, joint uncertainty in model parameters. By iteratively adapting the model and including multiple datasets, including both steady state and intervention data, we constructed a model that can explain a large part of the phosphorylation levels of several signaling molecules in the MAPK and AKT pathways, across many breast cancer cell lines and across various conditions. The resulting model delineates which routes in the signaling network are likely to be active in each cell line and condition, given all of the data. Additionally, such models can indicate whether datasets agree with each other, and identify which parts of the data cannot be explained, thereby highlighting gaps in the current knowledge. We conclude that this modeling approach can be useful to quantitatively understand how complex cellular signaling networks behave across different cell lines and conditions.

systems biology↗