Needs Assessment for Scientific Visualization of Multivariate, High-Dimensional Microarray Data (original) (raw)
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There are several visualization tools available for scientists that allow for modeling, simulation and visualization of complex biological systems data. The functionality and features of these tools vary depending on the layer (cellular, molecular, etc.) of the system to be explored. My BBSI research effort will focus upon developing a different way of visualizing complex microarray datasets that have multiple variables of interest. We will use The T. cruzi parasite as the initial development data; however if implemented correctly, the resulting tool could be used to visualize datasets that contain more than 5dimensions or variables of interest and may include time as well. This proposal will outline the approach we plan to take towards visualizing multivariate data.
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There are several visualization tools available for scientists that allow for modeling, simulation and visualization of complex biological systems data. The functionality and features of these tools vary depending on the layer (cellular, molecular, etc.) of the system to be explored. My BBSI research effort will focus upon developing a different way of visualizing complex microarray datasets that have multiple variables of interest. We will use The T. cruzi parasite as the initial development data; however if implemented correctly, the resulting tool could be used to visualize datasets that contain more than 5dimensions or variables of interest and may include time as well. This proposal will outline the approach we plan to take towards visualizing multivariate data.
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Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use "abstract" graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work.
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Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an Auto Regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.
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Microarrays are a relatively new, high-throughput data acquisition technology for investigating biological phenomena at the microlevel. One of the more common procedures for microarray experimentation is that of the microarray time-course experiment. The product of microarray time-course experiments is time-series data, which subject to proper analysis has the potential to have significant impact on the diagnosis, treatment, and prevention of diseases. While existing information visualization techniques go some way to making microarray time-series data more manageable, requirements analysis has revealed significant limitations. The main finding was that users were unable to uncover and quantify common changes in value over a specified time-period. This paper describes a novel technique that provides this functionality by allowing the user to visually formulate and modify measurable queries with separate time-period and condition components. These visual queries are supported by the combination of a traditional value against time graph representation of the data with a complementary scatter-plot representation of a specified time-period. The multiple views of the visualization are coordinated so that the user can formulate and modify queries with rapid reversible display of query results in the traditional value against time graph format.