Genesis 1.7.5 | 55 MB
We have developed a platform independent Java package of tools to simultaneously visualize and analyze a whole set of gene expression experiments. After reading the data from flat files several graphical representations of hybridizations can be generated, showing a matrix of experiments and genes, where multiple experiments and genes can be easily compared with each other.
Fluorescence ratios can be normalized in several ways to gain a best possible representation of the data for further statistical analysis. We have implemented hierarchical and non hierarchical algorithms to identify similar expressed genes and expression patterns, including: 1) hierarchical clustering, 2) k-means, 3) self organizing maps, 4) principal component analysis, and 5) support vector machines. More than 10 different kinds of similarity distance measurements have been implemented, ranging from simple Pearson correlation to more sophisticated approaches like mutual information. Moreover, it is possible to map gene expression data onto chromosomal sequences. The flexibility, variety of analysis tools and data visualizations, as well as the free availability to the research community makes this software suite a valuable tool in future functional genomic studies.
Fluorescence ratios can be normalized in several ways to gain a best possible representation of the data for further statistical analysis. We have implemented hierarchical and non hierarchical algorithms to identify similar expressed genes and expression patterns, including: 1) hierarchical clustering, 2) k-means, 3) self organizing maps, 4) principal component analysis, and 5) support vector machines. More than 10 different kinds of similarity distance measurements have been implemented, ranging from simple Pearson correlation to more sophisticated approaches like mutual information. Moreover, it is possible to map gene expression data onto chromosomal sequences. The flexibility, variety of analysis tools and data visualizations, as well as the free availability to the research community makes this software suite a valuable tool in future functional genomic studies.
