Taming a Graph Hairball: Local Exploration in a Global Context

Abello, James; Mawhirter, Daniel; Sun, Keye

doi:10.1007/978-3-030-06222-4_10

Cited by 1 publication

(1 citation statement)

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“…Many factors contribute towards this poor readability [56], most notably the number of edge bends and crucially the number of edge crossings [57, 58], even in larger figures [59]. In the worst case, naive attempts to visualize such networks as (force-directed) straight-line node-link diagrams result in so-called “hairballs”; dense, unintelligible clusters of nodes and edges, which do not allow for meaningful exploration of either local or global topology [60, 61]. In specXplore, we tackle the layout problem by combining a latent variable space embedding [35, 62] of nodes with interactively triggered partial network drawings [45, 63].…”

Section: Discussionmentioning

confidence: 99%

Tailored mass spectral data exploration using the specXplore interactive dashboard

Mildau,

Ehlers,

Oesterle

et al. 2023

Preprint

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Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two dimensional t-SNE embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of of state of the art ms2deepscore pairwise spectral similarities as a quantitative backbone, while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part in mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.Technical TermsA network is a collection of connected features. In our case, a network consists of MS/MS spectral features connected provided their spectral similarity is high. Networks are represented using node-link-diagrams.Node-link diagram -a term commonly used to refer to the graphical representation of a network via nodes and links (i.e. edges). In this paper, we use node-link diagram and network-view interchangeably.A node is a feature in a network that can be connected to other features via edges. An alternative term for node is vertex.An edge is a connection between two nodes. Other terms for edges are links or vertices.Network layout refers to the spatial arrangement of nodes and edges on an usually two dimensional plotting surface. Network layout is also sometimes referred to as embedding. This term is avoided in this paper to avoid confusion with embedding in the machine learning sense.Given a networkG(V, E), whereVdenotes its nodes andEits (weighted) edges, we define its topology as the relationships between individual (groups of) nodes and edges or the network as a whole, irrespective of the network’s layout.Molecular Networking (MN) is an exploratory data analysis technique merging spectral similarity-based topological clustering and visualization as node-link diagrams.The plain English words group/grouping are wherever appropriate to avoid jargon terms such as clustering (as in k-medoid or k-means clustering), embedding (as in projection of groups of features into a close-by lower dimensional space), or molecular families. The latter are groups of spectral data features clustered and visualized as network-views via traditional MN or feature based molecular networking (FBMN). Molecular families, usually represent smaller, disconnected networks that are part of a larger dataset. When we refer to this disconnected nature, we use the phrasing disjoint sub-network for emphasis.

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