Vetria L. Byrd, PhD
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Abstract The explosive growth in biological data (currently
GenBank contains over 44 billion base pairs and over 40 million sequences)
mandates an increasing need for sophisticated mathematical and
computational methods [1]and software environments capable of handling
the complexities and sizes of these various omic datasets [2]. This
is particularly true for microarray data. Microarray technology allows
for the simultaneous genomic analysis of entire organismal genomes [3] [4]. The
resulting datasets are high-dimensional, complex and frequently difficult to
interpret [5]. We decided to examine the need for advanced microarray
data analysis software tools. A survey research instrument entitled
Needs Assessment for Scientific Visualization of Microarray Data was created.
The survey research instrument was distributed to a non-random, snowball
sample set of researchers and biomedical life scientists currently
using microarray methods in their day-to-day research.Results
of the survey revealed microarray users are not satisfied with visualization
tools that are currently available.
Keywords: microarray tools, high-dimensional, visualization,
microarray data, microarray survey
Byrd, V., & Witten, T. (2006). Needs Assessment for Scientific Visualization of Multivariate, High-Dimensional Microarray Data. In BIOCOMP (pp. 103-109).
Abstract 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 systemto be explored. My BBSI research effort will focus upon developing a different way ofvisualizing complex microarray datasets that have multiple variables of interest. We willuse The T. cruzi parasite as the initial development data; however if implementedcorrectly, the resulting tool could be used to visualize datasets that contain more than 5-dimensions or variables of interest and may include time as well. This proposal willoutline the approach we plan to take towards visualizing multivariate data.
Byrd, V. L., Witten, T. M., & Skjellum, A. MULTIVARIATE HIGH DIMENSIONAL VISUALIZATION AND ANALYSIS OF MICROARRAY DATA INCORPORATING SIMULTANEOUS SPATIAL AND TEMPORAL COMPONENTS.
Abstract
Objective. The purpose of this study was to evaluate the semiautomatic alignment and correction of affine geometric discrepancies for digital subtraction radiography.
Study design. Algorithms were tested in vitro to determine their ability to semiautomatically select reference points on a second image based on points selected on a first (reference) image. A preserved human mandible was imaged with and without bone-equivalent material chips at varying degrees of angulation. Each chip had a mass of less than 10 mg and was no more than 0.3 mm thick. High levels of specificity and sensitivity for chip detection were achieved with 6 degrees of angular discrepancy or less. The algorithms were then applied to radiographs from six human subjects through use of the bone-chip validation model.
Results. Sensitivity was 89% and 100% for the three-point and four-point affine warp algorithms, respectively. Specificity for both algorithms was 100%.
Conclusions. The data indicate that semiautomated alignment algorithms may enhance the efficacy of digital subtraction radiography while maintaining diagnostic efficacy in clinical trials.
Byrd, V., Mayfield-Donahoo, T., Reddy, M. S., & Jeffcoat, M. K. (1998). Semiautomated image registration for digital subtraction radiography. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology, 85(4), 473-478.
doi:10.1016/S1079-2104(98)90077-4