Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets

Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to 1-dimensional space by using a filter function that is subsequently used to reconstruct the data topology relationships. However, domain context information and prior knowledge have not been considered in current TDA modeling frameworks. Here, we report the development and evaluation of a semi-supervised topological analysis (STA) framework that incorporates discrete or continuously labeled data points and selects the most relevant filter functions accordingly. We validate the proposed STA framework with simulation data and then apply it to samples from Genotype-Tissue Expression data and ovarian cancer transcriptome datasets. The graphs generated by STA for these 2 datasets, based on gene expression profiles, are consistent with prior knowledge, thereby supporting the effectiveness of the proposed framework.

InterVisAR: An Interactive Visualization for Association Rule Search

Association rule mining has been utilized extensively in many areas because it has the ability to discover relationships among variables in large databases. However, one main drawback of association rule mining is that it attempts to generate a large number of rules and does not guarantee that the rules are meaningful in the real world. Many visualization techniques have been proposed for association rules. These techniques were designed to provide a global overview of all rules so as to identify the most meaningful rules. However, using these visualization techniques to search for specific rules becomes challenging especially when the volume of rules is extremely large. In this study, we have developed an interactive association rule visualization technique, called InterVisAR, specifically designed for effective rule search. We conducted a user study with 24 participants, and the results demonstrated that InterVisAR provides an efficient and accurate visualization solution. We also verified that InterVisAR satisfies a non-factorial property that should be guaranteed in performing rule search. All participants also expressed high preference towards InterVisAR as it provides a more comfortable and pleasing visualization in association rule search.

Visual integration of quantitative proteomic data, pathways, and protein interactions

We introduce several novel visualization and interaction paradigms for visual analysis of published protein-protein interaction networks, canonical signaling pathway models, and quantitative proteomic data. We evaluate them anecdotally with domain scientists to demonstrate their ability to accelerate the proteomic analysis process. Our results suggest that structuring protein interaction networks around canonical signaling pathway models, exploring pathways globally and locally at the same time, and driving the analysis primarily by the experimental data, all accelerate the understanding of protein pathways. Concrete proteomic discoveries within T-cells, mast cells, and the insulin signaling pathway validate the findings. The aim of the paper is to introduce novel protein network visualization paradigms and anecdotally assess the opportunity of incorporating them into established proteomic applications. We also make available a prototype implementation of our methods, to be used and evaluated by the proteomic community.