Bioinformatics Toolbox 3.5

Microarray Data Analysis and Visualization

Bioninformatics Toolbox enables you to analyze and comprehend raw microarray data.

Microarray Normalization

You can use several methods for normalizing microarray data, including lowess, global mean, median absolute deviation (MAD), and quantile normalization. You can apply these methods to the entire microarray chip or to specific regions or blocks. Filtering and imputation functions let you clean raw data before running analysis and visualization routines.

Data Analysis and Visualization

Bioinformatics Toolbox lets you perform background adjustment and calculate gene (probe set) expression values from Affymetrix microarray probe-level data using robust multiarray average (RMA) and GCRMA. You can apply circular binary segmentation (CBS) to array CGH data and estimate the false discovery rate (FDR) of multiple hypotheses testing of gene expression data from a microarray experiment. You can perform rank-invariant set normalization on either probe intensities for multiple Affymetrix CEL files or gene expression values from two different experimental conditions.

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 visualize ideograms with G-banding patterns.

Using routines from Statistics Toolbox (required, and available separately), you can classify your results, perform hierarchical and K-means clustering, and represent your microarray data in statistical visualizations, such as two-dimensional clustergrams with optimal leaf ordering, heat maps, principle component plots, and classification trees.

Copy number alterations calculated and viewed alongside ideograms using Bioinformatics Toolbox. Click on image to see enlarged view.