engene TM

Gene-Expression Data Processing and
Exploratory Data Analysis

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Welcome to engeneTM, a versatile, web-based and platform independent exploratory data analysis tool for gene expression data that aims at storing, visualizing and processing large sets of expression patterns. engene (standing for Gene Engine) integrates a variety of analysis tools for visualizing, pre-processing and clustering expression data. The system includes different filters and normalization methods as well as an efficient treatment of missing data. The clustering algorithms included in the system range from the classical partitional and hierarchical methods, to the complex fuzzy ones, including: k-means, HAC, Fuzzy c-means and Kernel c-means. Linear and non-linear projection methods such as PCA, Sammon, and different variants of Self-Organizing Maps (classical, Fuzzy and Probabilistic) are also provided, including a completely novel SOM strategy aiming at producing truly quantitative Self-Organizing maps. Novel strategies for data pre-processing, gene and sample clustering and feature selection are also incorporated. Additionally, a Java suite for interactive Self-organizing Maps and partitional clustering is also included in the system. This tool enables the analysis of large sets of gene expression data in an easy and transparent manner, allowing the analysis of the outcome of different pre-processing and clustering methods at the same time. Free access to this tool is available upon request