|Perform two-sample t-test to evaluate differential expression of genes from two experimental conditions or phenotypes|
|Estimate positive false discovery rate for multiple hypothesis testing|
|Create significance versus gene expression ratio (fold change) scatter plot of microarray data|
|Create intensity versus ratio scatter plot of microarray data|
|Create box plot for microarray data|
|Create loglog plot of microarray data|
|Create Principal Component Analysis (PCA) plot of microarray data|
|Unpaired hypothesis test for count data with small sample sizes|
|Create red and blue colormap|
|Create red and green colormap|
|Plot Affymetrix probe set intensity values|
|Attractor metagene algorithm for feature engineering using mutual information-based learning|
|Rank key features by class separability criteria|
|Generate randomized subset of features|
|Impute missing data using nearest-neighbor method|
|Generate indices for training and test sets|
|Evaluate classifier performance|
|Create DataMatrix object|
|Data structure encapsulating data and metadata from microarray experiment so that it can be indexed by gene or probe identifiers and by sample identifiers|
|Contain data from microarray gene expression experiment|
|Contain data values from microarray experiment|
|Contain metadata from microarray experiment|
|Contain experiment information from microarray gene expression experiment|
Overview of objects for Microarray Gene Expression Data
Construct DataMatrix objects, get and set properties, and access data.
Construct ExptData objects, use properties and methods, and access data.
Construct MetaData objects, use properties and methods, and access data.
Construct MIAME objects, use properties and methods, and access data.
Construct ExpressionSet objects, use properties and methods, and access data.
The MATLAB® environment is widely used for microarray data analysis, including reading, filtering, normalizing, and visualizing microarray data.
You can classify and identify features in data sets, set up cross-validation experiments, and compare different classification methods.