DiNAR: revealing hidden patterns of plant signalling dynamics using Differential Network Analysis in R

Zagorščak et al. Plant Methods (2018) 14:78 https://doi.org/10.1186/s13007-018-0345-0 Plant Methods Open Access SOFTWARE DiNAR: revealing hidden patterns of plant signalling dynamics using Differential Network Analysis in R Maja Zagorščak1* , Andrej Blejec1,2, Živa Ramšak1, Marko Petek1, Tjaša Stare1 and Kristina Gruden1 Abstract Background: Progress in high-throughput molecular methods accompanied by more complex experimental designs demands novel data visualisation solutions. To specifically answer the question which parts of the specifical biological system are responding in particular perturbation, integrative approach in which experimental data are superimposed on a prior knowledge network is shown to be advantageous. Results: We have developed DiNAR, Differential Network Analysis in R, a user-friendly application with dynamic visualisation that integrates multiple condition high-throughput data and extensive biological prior knowledge. Implemented differential network approach and embedded network analysis allow users to analyse condition-specific responses in the context of topology of interest (e.g. immune signalling network) and extract knowledge concerning patterns of signalling dynamics (i.e. rewiring in network structure between two or more biological conditions). We validated the usability of software on the Arabidopsis thaliana and Solanum tuberosum datasets, but it is set to handle any biological instances. Conclusions: DiNAR facilitates detection of network-rewiring events, gene prioritisation for future experimental design and allows capturing dynamics of complex biological system. The fully cross-platform Shiny App is hosted and freely available at https://nib-si.shinyapps.io/DiNAR. The most recent version of the source code is available at https:// github.com/NIB-SI/DiNAR/ with a DOI 10.5281/zenodo.1230523 of the archived version in Zenodo. Keywords: Biological networks, Clustering, Gene expression, Time series, Dynamic network analysis, Dynamic data visualisation, Web application, Multi-conditional datasets, Background knowledge Background Technological progress in biological data generation enhanced development of network modelling to allow comprehension at systems level [1]. The ideal in silico network should be concise and able to capture key features of the actual system. Although this is difficult to achieve, particularly with non-model organisms, network-based strategies have proven very useful for interpreting biological data [2]. In line with emerging network views of biological systems, development of user-friendly visualisation tools becomes even more relevant. *Correspondence: 1 Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia Full list of author information is available at the end of the article Efficient network visualisation is lagging behind, especially in exploration of multi-conditional setups. Few solutions combining background knowledge and network analysis to enable visualisation of experimental data have so far been implemented in this area [3–9]. We developed an application to extend existing tools and further facilitate biological insight into dynamic rewiring events. DiNAR uses prior knowledge accompanied by differential network analysis to visualise complex experimental datasets. Main advanced features of DiNAR are (1) dynamic visualisation of complex multi-conditional experiments, (2) identification of strong differential interactions and (3) recall of latent effects that are present in multi-conditional experiments. Although DiNAR was primarily set for analysis of Arabidopsis thaliana and Solanum © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Zagorščak et al. Plant Methods (2018) 14:78 tuberosum datasets, it can handle other background knowledge networks in combination with experimental dataset of interest, e.g. transcriptomics, proteomics, metabolomics. Implementation DiNAR is written in R [10] and extended with JavaScript and Shiny package for interactive web applications. The implementation requires R version 3.1 or higher and several R packages, including animatoR [11], visNetwork [12] and ndtv [13]. Homotopy, as implemented in animatoR package, is used to interpolate node and edge weight value between discrete conditions/time-points. visNetwork, a browser based visualization library, allows implementation for easier manipulation and interaction with the data. Implementation of the ndtv package functions provides downloadable interactive movie rendering. The fully cross-platform validated application is hosted and freely available at https://nib-si.shinyapps.io/DiNAR . Source code is stored at https://github.com/NIB-SI/ DiNAR, where a more detailed application manual and package list are available. DiNAR can also be run locally in R or hosted on a local RStudio Shiny Server. Current application release provides the user with two embedded background knowledge networks: manually constructed plant immune signalling network (PIS) [14] translated to S. tuberosum at the orthologue groups level [15] and one constructed from prior knowledge on A. thaliana—the A. thaliana Comprehensive Knowledge Network (AtCKN) [15]. AtCKN, containing 20,012 nodes and 70,091 connections, was first analysed to determine disjoint communities (i.e. clusters) based on network centrality measures, for easier visualisation. Multi-level community detections algorithm followed by spinglass community detection algorithm were used, both implemented in igraph R package [16]. As the result, AtCKN was divided into 48 clusters. DiNAR also provides an option of uploading a user-defined network. Any kind of network in the proper format can be used to visualise changes in omics dataset (e.g. transcriptomics, miRNAomics, proteomics and metabolomics). Notice that the background network node identifiers should be consistent with the corresponding experimental data identifiers as well as the statistical analyses between different omics levels have to be standardised. Both static graphics and interactive animations can be exported, together with a record of user settings, which is compliant with FAIR guiding principles for reproducible research [17]. In addition to DiNAR core scripts, optional pre-processing and clustering tools (subApps) are also hosted at shinyapps.io platform: https://nib-si.shinyapps.io/preprocessing and https://nib-si.shin (...truncated)