2226313
Fourier harmonic approach for visualizing temporal patterns of gene expression data
DNA microarray technology provides a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex dataset by presenting the data in a graphical format in which the key characteristics become more apparent. The purpose of this study is to present an interactive visualization technique conveying the temporal patterns of gene expression data in a form intuitive for nonspecialized end-users. The first Fourier harmonic projection (FFHP) was introduced to translate the multidimensional time series data into a two dimensional scatter plot. The spatial relationship of the points reflect the structure of the original dataset and relationships among clusters become two dimensional. The proposed method was tested using two published, array-derived gene expression datasets. Our results demonstrate the effectiveness of the approach.
32889588
Enhanced visualization of time series through higher fourier harmonics
Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. A new visualization technique, the first Fourier harmonic projection (FFHP) was introduced to translate the multi-dimensional data into a two dimensional scatter plot where the spatial relationship of the points reflects the structure of the original data set. FFHP has been shown capable of visualizing various gene expression data sets. However, dimension arrangement is crucial for the effectiveness of FFHP and certain data, such as time series, prevents dimensions (time points) from being freely rearranged. In this paper, we present an alternative approach through higher Fourier harmonic projections to enhance the visualization. Our algorithm takes advantage of the theoretical meaning of the Fourier harmonics and "reshuffles" the dimensions of the data set without physically rearranging them. The experimental results demonstrated significant improvement of the visualizations.
636109
VizStruct: exploratory visualization for gene expression profiling
MOTIVATION
DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples.


RESULTS
The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy.


AVAILABILITY
Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.
14721070
Visualized Classification of Multiple Sample Types
The goal of the knowledge discovery and data mining is to extract the useful knowledge from the given data. Visualization enables us to find structures, features, patterns, and relationships in a dataset by presenting the data in various graphical forms with possible interactions. Recently, DNA microarray technology provides a board snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Such information can thus be used to analyze different samples by the gene expression profiles. Last few years saw many cluster analysis and classsification methods extensively be applied to capture the similarity pattern of gene expressions. A novel interactive visualization approach, VizCluster, was presented and applied to classify samples of two types. It combines the merits of both high dimensional projection scatter plot and parallel coordinate plot, taking advantage of graphical visualization methods to reveal the underlining data patterns. In this paper, we expand VizCluster to classify multiple types of samples. First, we identify genes which are differentially expressed across the sample groups. Then we apply VizCluster to build classifiers based on those genes. Finally, classifiers were evaluated by either hold out or cross validation. Five gene expression data sets were used to illustrate the approach. Experimental performance demonstrated the feasibility and usefulness of this approach.
17513101
Supporting Content-Based Retrieval in Large Image Database Systems
In this paper, we investigate approaches to supporting effective and efficient retrieval of image data based on content. We firstintroduce an effective block-oriented image decomposition structure which can be used to represent image content inimage database systems. We then discuss theapplication of this image data model to content-based image retrieval.Using wavelet transforms to extract image features,significant content features can be extracted from image datathrough decorrelating the data in their pixel format into frequency domain. Feature vectors ofimages can then be constructed. Content-based image retrievalis performed by comparing the feature vectors of the query imageand the decomposed segments in database images.Our experimental analysis illustrates that the proposed block-oriented image representationoffers a novel decomposition structure to be used tofacilitate effective and efficient image retrieval.
40069963
Block-oriented image decomposition and retrieval in image database systems
We investigate approaches to support effective and efficient retrieval of image data based on content. We first introduce an effective block-oriented image decomposition structure which can be used to represent image content in image database systems. We then discuss the application of this image data model to content-based image retrieval. Using wavelet transforms to extract image features, significant content features can be extracted from image data through decorrelating the data in their pixel format into the frequency domain. Feature vectors of images can then be constructed. Content-based image retrieval is performed by comparing the feature vectors of the query image and the decomposed segments in database images. Our experimental analysis illustrates that the proposed block-oriented image representation offers a novel decomposition structure to be used to facilitate effective and efficient image retrieval.