Visual Analysis of Microarray Data from Bioinformatics Applications

We present a new application designed for the visual exploration of microarray data.It is based on an extension and adaption of parallel coordinates to support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene-expression data as generated by microarray experiments. We combine refined visual exploration with statistical methods to a visual analytics approach, which proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene-expression datasets from different bioinformatics applications

OmicsVis: an interactive tool for visually analyzing metabolomics data

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery

Visualizing Meta-Features in Proteomic Maps

<p>Abstract</p> <p>Background</p> <p>The steps of a high-throughput proteomics experiment include the separation, differential expression and mass spectrometry-based identification of proteins. However, the last and more challenging step is inferring the biological role of the identified proteins through their association with interaction networks, biological pathways, analysis of the effect of post-translational modifications, and other protein-related information.</p> <p>Results</p> <p>In this paper, we present an integrative visualization methodology that allows combining experimentally produced proteomic features with protein meta-features, typically coming from meta-analysis tools and databases, in synthetic Proteomic Feature Maps. Using three proteomics analysis scenarios, we show that the proposed visualization approach is effective in filtering, navigating and interacting with the proteomics data in order to address visually challenging biological questions. The novelty of our approach lies in the ease of integration of any user-defined proteomic features in easy-to-comprehend visual representations that resemble the familiar 2D-gel images, and can be adapted to the user's needs. The main capabilities of the developed VIP software, which implements the presented visualization methodology, are also highlighted and discussed.</p> <p>Conclusions</p> <p>By using this visualization and the associated VIP software, researchers can explore a complex heterogeneous proteomics dataset from different perspectives in order to address visually important biological queries and formulate new hypotheses for further investigation. VIP is freely available at <url>http://pelopas.uop.gr/~egian/VIP/index.html</url>.</p

Advanced modelling and visualisation of liquid-liquid separations of complex sample components, with variable phase distribution and mode of operation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is about liquid-liquid chromatography modelling. While the main focus was on liquid-liquid chromatography, where the stationary and mobile phases are both liquid, theory of different types of chromatography, including the currently most used techniques, were considered as well. The main goal of this research was to develop a versatile liquid-liquid separation model, able to model all potential operating scenarios and modes of operation. A second goal was to create effective and usable interfaces to such a model, implying primarily information visualisation, and secondarily educative visualisation. The first model developed was a model based on Counter-Current Distribution. Next a new more elemental model was developed, the probabilistic model, which better models continuous liquid-liquid chromatography techniques. Finally, a more traditional model was developed using transport theory. These models were used and compared to experimental data taken from literature. The models were demonstrated to model all main liquid-liquid chromatography techniques, incorporated the different modes of operation, and were able to accurately model many sample components and complex sample injections. A model interface was developed, permitting functional and effective model configuration, exploration and analysis using visualisation and interactivity. Different versions of the interface were then evaluated using questionnaires, group interviews and Insight Evaluation. The visualisation and interactivity enhancements have proven to contribute understanding and insight of the underlying chromatography process. This also proved the value of the Insight Evaluation method, providing valuable qualitative evaluation results desired for this model interface evaluation. A prototype of a new graphical user interface developed, and showed great potential for combining model parameter input and exploring the liquid-liquid chromatography processes. Additionally, a new visualisation method was developed that can accurately visualise different modes of operation. This was used to create animations, which were also evaluated. The results of this evaluation show the new visualisation helps understanding of the liquid-liquid chromatography process amongst CCC novices. The model software will be a valuable tool for industry for predicting, evaluating and validating experimental separations and production processes. While effective models already existed, the use of interactive visualisation permits users to explore the relationship between parameters and performances in a simpler yet more powerful way. It will also be a valuable tool for academia for teaching & training, both staff and students, on how to use the technology. Prior to this work no such tool existed or existing tools were limited in their accessibility and educational value.This study was supported by Brunel University and the Royal Academy of Engineering