summaryFunctions: Summary functions for some MIGSA classes

In MIGSA: Massive and Integrative Gene Set Analysis

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)

MIGSA documentation built on Nov. 8, 2020, 8:26 p.m.