Expression Analysis

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

Functions

mattestPerform two-sample t-test to evaluate differential expression of genes from two experimental conditions or phenotypes
mafdrEstimate positive false discovery rate for multiple hypothesis testing
mavolcanoplotCreate significance versus gene expression ratio (fold change) scatter plot of microarray data
mairplotCreate intensity versus ratio scatter plot of microarray data
maboxplotCreate box plot for microarray data
maloglogCreate loglog plot of microarray data
mapcaplotCreate Principal Component Analysis (PCA) plot of microarray data
nbintestUnpaired hypothesis test for count data with small sample sizes
redbluecmapCreate red and blue colormap
redgreencmapCreate red and green colormap
probesetplotPlot Affymetrix probe set intensity values
metafeaturesAttractor metagene algorithm for feature engineering using mutual information-based learning
rankfeaturesRank key features by class separability criteria
randfeaturesGenerate randomized subset of features
knnimputeImpute missing data using nearest-neighbor method
crossvalindGenerate cross-validation indices
classperfEvaluate classifier performance

Classes

DataMatrixCreate DataMatrix object
DataMatrix objectData structure encapsulating data and metadata from microarray experiment so that it can be indexed by gene or probe identifiers and by sample identifiers
bioma.ExpressionSetContain data from microarray gene expression experiment
bioma.data.ExptDataContain data values from microarray experiment
bioma.data.MetaDataContain metadata from microarray experiment
bioma.data.MIAMEContain experiment information from microarray gene expression experiment
NegativeBinomialTestUnpaired hypothesis test result
HeatMapObject containing matrix and heatmap display properties
clustergramObject 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