summary {MIGSA} | R Documentation |
R base summary overwritten functions to manipulate MIGSA objects.
## 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, ...)
object |
SEAparams, GSEAparams, IGSAinput or MIGSAres object. |
... |
not in use. |
A summary of the object.
## 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)