summary {MIGSA} | R Documentation

## Summary functions for some MIGSA classes

### Description

R base summary overwritten functions to manipulate MIGSA objects.

### Usage

    ## S3 method for class 'SEAparams'
    summary(object, ...)

    ## S3 method for class 'GSEAparams'
    summary(object, ...)

    ## S3 method for class 'IGSAinput'
    summary(object, ...)

    ## S3 method for class 'MIGSAres'
    summary(object, ...)

### Arguments

`object` | SEAparams, GSEAparams, IGSAinput or MIGSAres object.
`...` | not in use.

### Value

A summary of the object.

### Examples

    ## Lets get the summary of the default SEAparams object
    seaParams <- SEAparams();
    summary(seaParams);

    ## Lets get the summary of the default GSEAparams object
    gseaParams <- GSEAparams();
    summary(gseaParams);

    ## Lets create a basic valid IGSAinput object to get its summary.
    ## First create a expression matrix.
    maData <- matrix(rnorm(10000),ncol=4);
    rownames(maData) <- 1:nrow(maData); # It must have rownames (gene names).
    maExprData <- new("MAList",list(M=maData));

    ## Now lets create the FitOptions object.
    myFOpts <- FitOptions(c("Cond1", "Cond1", "Cond2", "Cond2"));

    ## And now we can create our IGSAinput ready for MIGSA.
    igsaInput <- IGSAinput(name="myIgsaInput", expr_data=maExprData,
    fit_options=myFOpts);
    summary(igsaInput);

    ## Now lets get the summary of out migsaRes data object.
    data(migsaRes);

    ### As enrichment cutoff is not set then we will get for each experiment the
    ### number of enriched gene sets at different cutoff values.
    summary(migsaRes);

    ### Lets set the enrichment cutoff at 0.01
    migsaResWCoff <- setEnrCutoff(migsaRes, 0.01);

    ### Now as summary we will get the number of enriched gene sets per
    ### experiment and their intersections.
    summary(migsaResWCoff);

[Package MIGSA version 1.6.0 Index]