4.4. Exploring networkΒΆ

In order to substantially facilitate network navigation and information extraction, MONGKIE provides sophisticated options to explore networks in highly interactive ways, including searching, filtering, grouping, manual or automatic node selection, highlight, dragging, zoom and panning the display, overview of the complete network, and lastly data table view displaying attributes of nodes and edges in a tabular format.

MONGKIE provides easy-to-use search functions for the loaded network. One can enter any keyword or regular expression to search all data attributes held in the nodes and edges. The matching nodes or edges immediately highlighted in the visualization; and selection in the visualization propagates to the selection of the relevant rows in the data table scrolling to them, and vice versa. Furthermore, the network can be filtered down to interactions meeting a given filtering constraint according to their attributes. For instance, the network may be filtered to show only proteins occurring in particular locations, thus reducing the network complexity and restricting the one’s attention only to interactions within a given sub-cellular location, and this could greatly improve the visual perception of complex biological networks.

Given the large and complex networks, one common approach to interpret and visualize such networks is trying to display the complete network on the screen and providing functionalities to zoom, pan, and overview of the network for exploration. Like many other network visualization tools, MONGKIE provides these basic techniques for network navigation too. However, as the size and complexity of interactions grow, it is increasingly impossible to understand underlying structures and extract biological insights from such huge networks just using those basic navigation techniques. Another improved strategy is to dissect the complete network into smaller sub-networks that are manageable, biologically significant regions that can be understandable (Gehlenborg et al., 2010). These sub-networks are typically defined as, for example, sets of proteins that are occurring at the same sub-cellular location, or that belong to similar functional GO terms, or that are members of a densely connected cluster identified through the established network clustering methods. The resultant sub-networks are typically of a size that is more amenable to visualization and analysis.

In order to specifically support of these processes of dissection, MONGKIE provides a variety of ways to define groups of functionally or topologically related nodes, including enrichment analysis for functional modules (See Over-representation analysis), clustering analysis for topological clusters (See Network clustering), grouping by manual selection, or automatic partitioning of nodes according to their attributes. The defined groups or sub-networks are visually represented with distinct styles and importantly laid out separately from other parts in a way that automatically attracts each other nodes in a group while repelling other groups (See Fig. 5.1), and this geometric separation is essential to focus only on particular groups without being disturbed by unnecessary or noisy interactions. Additionally, one can create a new visualization of each sub-network, then analysis it independently from original one from which it derived.

MONGKIE is developed specially with these grouping functionalities in mind as one of main development goals so that it can be used to help dissect large interactions, thus reducing the overall complexity, and focusing on smaller but biologically interesting parts to gain biological insights without being overwhelmed by complexity and noises in the network.