Elucidation of time-dependent systems biology cell response patterns with time course network enrichment

Advances in OMICS technologies emerged both massive expression data sets and huge networks modelling the molecular interplay of genes, RNAs, proteins and metabolites. Network enrichment methods combine these two data types to extract subnetwork responses from case/control setups. However, no methods exist to integrate time series data with networks, thus preventing the identification of time-dependent systems biology responses. We close this gap with Time Course Network Enrichment (TiCoNE). It combines a new kind of human-augmented clustering with a novel approach to network enrichment. It finds temporal expression prototypes that are mapped to a network and investigated for enriched prototype pairs interacting more often than expected by chance. Such patterns of temporal subnetwork co-enrichment can be compared between different conditions. With TiCoNE, we identified the first distinguishing temporal systems biology profiles in time series gene expression data of human lung cells after infection with Influenza and Rhino virus. TiCoNE is available online (https://ticone.compbio.sdu.dk) and as Cytoscape app in the Cytoscape App Store (http://apps.cytoscape.org/)

A Visual Framework Invites Human into the Clustering Process

Clustering is a technique commonly used in scientific research. The task of clustering inevitably involves human participation - the clustering is not finished when the computer/algorithm finishes but the user has evaluated, understood and accepted the patterns. This defines a human involved clustering-analysis/evaluation iteration. Instead of neglecting this human involvement, we provide a visual framework (VISTA) with all power of algorithmic approaches (since their result can be visualized), and in addition we allow the user to steer/monitor/refine the clustering process with domain knowledge. The visual-rendering result also provides a precise pattern for fast post-processing

A Visual Framework Invites Human into the Clustering Process

Clustering is a technique commonly used in scientific research. The task of clustering inevitably involves human participation - the clustering is not finished when the computer/algorithm finishes but the user has evaluated, understood and accepted the patterns. This defines a human involved clustering-analysis/evaluation iteration. Instead of neglecting this human involvement, we provide a visual framework (VISTA) with all power of algorithmic approaches (since their result can be visualized), and in addition we allow the user to steer/monitor/refine the clustering process with domain knowledge. The visual-rendering result also provides a precise pattern for fast post-processing

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions