Expression Analysis

Identify, visualize, and classify differentially expressed genes and expression profiles

Evaluate gene expression data and identify differentially expressed genes using hypothesis tests. Use various machine learning functions to train classifier models, perform principal component analysis, rank features, and impute missing data. Visualize microarray data using intensity versus ratio (IR) plots, box plots, or log-log plots.

Functions

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Perform Hypothesis Tests

`mattest`	Two-sample t-test to evaluate differential expression of genes from two experimental conditions or phenotypes
`mafdr`	Estimate positive false discovery rate for multiple hypothesis testing
`nbintest`	Unpaired hypothesis test for count data with small sample sizes

Rank Features and Train Classifiers

`metafeatures`	Attractor metagene algorithm for feature engineering using mutual information-based learning
`rankfeatures`	Rank key features by class separability criteria
`randfeatures`	Generate randomized subset of features
`knnimpute`	Impute missing data using nearest-neighbor method
`crossvalind`	Generate indices for training and test sets
`classperf`	Evaluate classifier performance

Visualize Data

`mavolcanoplot`	Create significance versus gene expression ratio (fold change) scatter plot of microarray data
`mairplot`	Create intensity versus ratio scatter plot of microarray data
`maboxplot`	Create box plot for microarray data
`maloglog`	Create loglog plot of microarray data
`mapcaplot`	Create Principal Component Analysis (PCA) plot of microarray data
`redbluecmap`	Create red and blue colormap
`redgreencmap`	Create red and green colormap

Objects

`DataMatrix`	Data structure encapsulating data and metadata from microarray experiment
`bioma.ExpressionSet`	Contain data from microarray gene expression experiment
`bioma.data.ExptData`	Contain data values from microarray experiment
`bioma.data.MetaData`	Metadata from microarray experiment
`bioma.data.MIAME`	Contain experiment information from microarray gene expression experiment
`NegativeBinomialTest`	Unpaired hypothesis test result
`HeatMap`	Object containing matrix and heatmap display properties
`clustergram`	Object containing hierarchical clustering analysis data

Topics

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.

Featured Examples

Analyzing Illumina Bead Summary Gene Expression Data

Analyze Illumina® BeadChip gene expression summary data using MATLAB® and Bioinformatics Toolbox™ functions.

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Analyzing Array-Based CGH Data Using Bayesian Hidden Markov Modeling

Use a Bayesian hidden Markov model (HMM) technique to identify copy number alteration in array-based comparative genomic hybridization (CGH) data.

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Identifying Biomolecular Subgroups Using Attractor Metagenes

Use the metafeatures function to explore biomolecular phenotypes in breast cancer.

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Working with the Clustergram Function

Work with the clustergram function.

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