| Bioinformatics Toolbox | |
MATLAB is widely used for microarray data analysis. However, the standard normalization and visualization tools that scientists use can be difficult to implement. The Bioinformatics Toolbox includes these standard functions.
Microarray data — Read Affymetrix GeneChip files (affyread), ImaGene results files (imageneread), and SPOT files (sptread). Read GenePix GPR files (gprread) and GAL files (galread). Get Gene Expression Omnibus (GEO) data from the web (getgeodata) and read GEO data from files (geosoftread).
Microarray normalization and filtering — The toolbox provides a number of methods for normalizing microarray data, such as lowess normalization (malowess) mean normalization (manorm). You can use filtering functions to clean raw data before analysis (geneentropyfilter, genelowvalfilter, generangefilter, genevarfilter), and calculate the range and variance of values (exprprofrange, exprprofvar).
Microarray visualization — The toolbox contains routines for visualizing microarray data. These routines include spatial plots of microarray data (maimage, redgreencmap), box plots (maboxplot), loglog plost (maloglog), and intensity-ratio plots (mairplot). You can also view clustered expression profiles (clustergram, redgreencmap). You can create 2–D scatter plots of principal components from the microarray data (mapcaplot).
Microarray utility functions — Use the following functions to work with Affymetrix and GeneChip data sets. Get library information for a probe (probelibraryinfo), gene information from a probe set (probesetlookup), and probe set values from CEL and CDF information (probesetvalues). Show probe set information from NetAffx (probesetlink) and plot probe set values (probesetplot).
The toolbox accesses statistical routines to perform cluster analysis and to visualize the results, and you can view your data through statistical visualizations such as dendrograms, classification, and regression trees.
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