Bioinformatics Toolbox

Microarray Data Analysis and Visualization

Bioinformatics 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 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. 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. 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.

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.

Volcano plot of microarray data showing significance versus gene expression ratio.

Using routines from Statistics 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.

Copy number alterations (left) calculated and viewed alongside ideograms (right) using Bioinformatics Toolbox.