Abstract
BackgroundSeveral mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.ResultsHere, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.ConclusionGEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.
Highlights
Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data
Microarray platforms have become available at relatively low costs, becoming more popular among research groups which are interested in gene expression analysis
GEDI allows the analysis of gene expression data in four major steps, starting from eliminating the bias generated by the microarray technique, followed by identification of differentially expressed genes, classification of samples based on molecular profiles to identify potential biomarkers or targets for drugs, and, inferring gene functionality by constructing gene expression regulatory networks
Summary
Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Much effort has been spent in developing improved methods to analyze the data derived from these microarrays These methods involve advanced mathematical and statistical models, which are quite cumbersome to biomedical researchers who attempt to implement these methods. Due to this difficulty, some of these advanced methods are often abandoned and data analysis is carried out using only the classical methods, which are implemented in popular statistical softwares. An userfriendly software could make it possible to use recently (page number not for citation purposes)
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