Robin A simple to use microarray data preprocessor supporting all common microarray platforms
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analysis C preprocessor extension preprocessor Hypertext Preprocessor code preprocessor preprocessor application text preprocessor C code preprocessor preprocessor directives HTML preprocessor Preprocessor Elements Pascal Preprocessor gene expression microarray analyze microarray data microarray data analyzer microarray gene analysis microarray probe microarray Microarray Info Extractor Microarray Analysis microarray layout converter microarray experiment anlayzer data preprocessor microarray data microarray cluster two-dye microarray data sets microarray data analysis classify microarray data PhyloChip microarray DNA microarray SQL preprocessor

Robin Description

Currently, several commercial programs such as GeneSpring featuring a rich and simple user interface to statistically analyze microarrays are available. However, these are usually very expensive and might even require an annual subscription On the other hand, there are free, open source tools such as BioConductor which offer great statistical options and support for free, but this power often comes at the price of usability, since these tools often feature either only a command line interface or solely very basic user interaction. Finally, there are tools such as RMA Express which offer a rich user interface but only a very limited set of options. Robin tries to bridge this gap by providing a flexible user friendly graphical interface to unleash the power of R/BioConductor for the individual biologist. Robin comes as one installable file, that also installs the R and BioConductor frameworks. Currently, using Robin it is possible to normalize Affymetrix data and generic tabular two color or single channel array data using a range of different normalization methods. Furthermore, Robin offers a variety of quality control methods that can be used to gain an overview of the experimental data technical quality and structure. Using the built-in graphical experiment designer, researchers can define which samples to compare (even combinations of samples are possible) and finally produce a comprehensive set of tables and plots giving detailed results of the experiment such as the log fold change (M), average expression (A) values at a (user-adjustable) probability for differential expression. Tables summarizing information on the top 100 differentially regulated genes and several plots visualizing the quality of each chip are also generated. The results can directly be imported into MapMan and PageMan for visualization and further analysis. Robin brings you a complete solution for microarray analysis and data processing.

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