|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|
- Managing Gene Expression Data in Objects
Overview of objects for Microarray Gene Expression Data
- Representing Expression Data Values in DataMatrix Objects
Construct DataMatrix objects, get and set properties, and access data.
- Representing Expression Data Values in ExptData Objects
Construct ExptData objects, use properties and methods, and access data.
- Representing Sample and Feature Metadata in MetaData Objects
Construct MetaData objects, use properties and methods, and access data.
- Representing Experiment Information in a MIAME Object
Construct MIAME objects, use properties and methods, and access data.
- Representing All Data in an ExpressionSet Object
Construct ExpressionSet objects, use properties and methods, and access data.
- Microarray Data Analysis Tools
The MATLAB® environment is widely used for microarray data analysis, including reading, filtering, normalizing, and visualizing microarray data.
- Statistical Learning and Visualization
You can classify and identify features in data sets, set up cross-validation experiments, and compare different classification methods.