![]() Using routines from Statistics and Machine Learning Toolbox, you can classify your results, perform hierarchical and K-means clustering, and represent your microarray data in statistical visualizations, such as 2D clustergrams with optimal leaf ordering, heat maps, principle component plots, and classification trees. ![]() You can also visualize ideograms with G-banding patterns. Specialized routines for visualizing microarray data include volcano plots, box plots, loglog plots, I-R plots, and spatial heat maps of the microarray. You can also perform rank-invariant set normalization on either probe intensities for multiple Affymetrix CEL files or gene expression values from two different experimental conditions. ![]() JPred is can also be run on a single sequence or. You can apply circular binary segmentation to array CGH data and estimate the false discovery rate of multiple hypotheses testing of gene expression data from a microarray experiment. Web server for protein secondary structure prediction, buried site prediction and coiled coils prediction. Bioinformatics Toolbox lets you perform background adjustments and calculate gene (probe set) expression values from Affymetrix ® microarray probe-level data using Robust Multi-Array Average (RMA) and GC Robust Multi-Array Average (GCRMA) procedures.
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