Keywords
Data display, Expression profiling, Graphical user interfaces, Metabolite profiling, Small multiples
This article is included in the Cytoscape gateway.
Data display, Expression profiling, Graphical user interfaces, Metabolite profiling, Small multiples
Developments in high-throughput -omics measurement techniques have allowed researchers to routinely obtain presence/absence, levels or interactions of molecules at a genome-wide scale, often addressing important questions that could not be answered before. Consequently, the demand for more and better tools for data analysis is growing, especially visualization tools to project data of multiple samples (e.g. different time points or tissues) on network or pathway maps. Visualization plays an important role in data analysis as a way to obtain insights, select details and present findings1.
Several software tools enable visualization of expression data on pathways2–4 and the functionality to plot charts on nodes has become a standard feature of Cytoscape 35. While these visualizations often work well for analyzing measurement patterns of individual genes in a network, they do not easily show changes at a more global level in the network. Often, these global effects are the most interesting to the biologist.
Here, we present a different approach that uses “small multiples”. This approach is a visualization technique that shows multiple copies of a graph with the same combination of variables, but with different changes in each variable6. Small multiples facilitate comparison, presentation, storytelling and search for patterns, trends and outliers1.
There is currently no visualization tool enabling projection of data from different experiments/samples on small multiples. Therefore, we developed the Cytoscape app CyLineUp, that takes advantage of the tools for importing and displaying maps integrated into Cytoscape to create small multiples. We applied CyLineUp to visualize changes in the glycolysis/tricarboxylic acid (TCA) cycle induced by the phytohormone abscisic acid (ABA) in germinated Arabidopsis thaliana seeds.
CyLineUp was written in Java 7 as an OSGi (Open Services Gateway Initiative) bundle. It adds a “CyLineUp” tab to the Cytoscape “Control Panel” offering functionality to import the data, configure the views and visual styles, and export an SVG rendering of the visualization. The data import functionality is implemented as a Cytoscape Task, using Tunable annotations for user input dialogs. The small multiples visualization is implemented as a set of views on the same underlying network. Each view has its own visual style for coloring the nodes according to the input data. The FreeHEP Graphics 2D Library is used for exporting an SVG image with the small multiples.
Visualizing -omics data with CyLineUp consists of four main steps. (I) Creating the map or network that will be the basis for data visualization. A large collection of pathways from the WikiPathways platform (http://wikipathways.org) can be easily imported using the WikiPathways Cytoscape app7. Optionally, pathways created and imported directly from Pathvisio8 and networks created in Cytoscape can be used. The users should take care that the names of the identifiers are the same in the data file and in the pathway/network. (II) Importing data and linking it to the map using the graphical user interface provided by CyLineUp (Figure 1). CyLineUp accepts text files with comma, semicolon, tab or arbitrary whitespaces separated values. CyLineUp provides auto-import functionality, but manual selection of data is also possible. The auto-import functionality is based on the order of the columns in the data file. Therefore, it is advisable to have the identifiers in the first column and the values (e.g. fold change) and the p-values for each treatment/sample in subsequent columns. (III) Customizing the visualization to the user’s needs by changing the Visual styles in the CyLineUp user interface (Figure 2). Nodes that have no data associated can be shown (default), greyed out or hidden. In case the nodes are hidden, the edges associated to them will also be hidden. The default colors are green (HEX #00FF00) for up-regulated genes and red (HEX #FF0000) for down-regulated genes. The user can chose different colors by pressing the Pick color buttons. After each change, the user can update the view of the small multiples by pressing the button Update network views. (IV) Exporting the visualization to an SVG image file. A real-time image preview is available, but can be disabled in case it takes too much computer resources.
We used available gene expression data from a time series of microarrays9 to validate the use of CyLineUp. The data were generated to explore the temporal changes induced by ABA in germinated A. thaliana seeds9. Seeds at the stage of radicle protrusion (0 h) and after four periods (2, 12, 24 and 72 h) of incubation in ABA were used. Details about data acquisition and processing can be found in Costa et al.9. Here, we used CyLineUp to explore the main changes in the glycolysis/TCA cycle during ABA incubation (Figure 3).
The map of the cycle was created using Pathvisio 3.2.08 and imported into Cytoscape 3.2.1 (Supplementary file S1.gpml). Then, the expression data and Bonferroni-corrected p-values (comma-separated values (CSV) file: Supplementary file S2.csv) were imported using the CyLineUp user interface. The visual style was set to “grey out” nodes that have no data associated, use a “p-value cut-off” of 0.1 and “use fill color to visualize p-value”.
The glycolysis/TCA cycle provides most of the energy for processes in the seed. A general activation of the cycle could be observed as one of the immediate cellular responses induced by ABA10.
CyLineUp allows easy visual analysis of data from multiple samples on a biological network to study changes at the (sub-)network level. It is a useful and timely addition to the Cytoscape network analysis platform.
Software available from: http://apps.cytoscape.org/apps/cylineup
Latest source code: https://github.com/Slijkhuis/CyLineUp
Archived source code as at time of publication: http://dx.doi.org/10.5281/zenodo.4813511
License: Lesser GNU Public License 2.1
MCDC wrote the manuscript and provided experimental data to validate the use of the app. TS developed initial version of the app. WL, HWMH and DDR provided input on the manuscript. HN supervised app development, improved the app and provided input on the manuscript.
This work has been supported by the ‘Conselho Nacional de Desenvolvimento Científico e Tecnológico’ (CNPq, Brazil) (MCDC).
Supplementary file S1.gpml Map of the glycolysis/TCA cycle.
Supplementary file S2.csv Expression values and Bonferroni-corrected p-values.
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Competing Interests: No competing interests were disclosed.
Competing Interests: Non-financial competing interests: I reviewed an earlier version of the paper as guest editor for this collection of Cytoscape app articles. I am a core developer of Cytoscape. I am also a developer of WikiPathways, which is mentioned and cited in the article. I believe these interests did not bias my review beyond informing my perspective on the usefulness of the tool, but I'll leave that judgement to you, the reader of these open peer reviews.
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