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 1.14.1 Index]