Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

Abstract

Abstract-The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

“…[51], [52], [53], [54], [55], [56] Groups & Classification [57] [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74] [75], [76], [77], [78], [79], [80] Dependence & Prediction [81], [82], [46] [83], [84], [85], [86], [87], [88], [89] [90], [91], [92] being analyzed. The results are then presented to the user through different visual encodings that are often accompanied by interaction.…”

“…Examples in biomedical domain are rare in this category. One example is by Rubel et al [76], who present a framework for clustering and visually exploring (3D) expression data. In the domain of molecular dynamics simulation, there are some examples of tight integrations of interactive visualizations, clustering algorithms, and statistics to support the validity of the resulting structures [77], [78].…”

Integrating cluster formation and cluster evaluation in interactive visual analysis

“…The authors introduce the concept of flow groups, which display cluster evolution over time, to validate the quality of clustering results. In a recent study, Rubel et al [Rubel et al 2010 [Sharko et al 2008]. This approach proves to be useful in validating the clusters when many different clusterings for the same dataset exist.…”

Linking Automated Data Analysis and Visualization with Applications in Developmental Biology and High-Energy Physics

“…Second, if the user has a priori knowledge of the number of clusters k but the number of input genes is very large, then the advanced user may chose to perform dimension reduction prior to clustering (see Figure 4.11, Decompose and Cluster). In this case, singular value decomposition (SVD) [140,141] is used to decompose the expression data matrix and only the user selected SVD eigen-genes are considered in the clustering process [160] (see Figure 4.13). Third, if the user has no a priori knowledge of the number of clusters k then PCX can assist the user in finding a good value for k (see Figure 4.11,…”