FRESA.CAD (version 2.2.0)

rankInverseNormalDataFrame: Perform a z-transformation of the data using the rank-based inverse normal transformation

Description

This function takes a data frame and a reference control population to return a z-transformed data set conditioned to the reference population. Each sample data for each feature column in the data frame is conditionally z-transformed using a rank-based inverse normal transformation, based on the rank of the sample in the reference frame.

Usage

rankInverseNormalDataFrame(variableList,
	                           data,
	                           referenceframe)

Arguments

variableList

A data frame with two columns. The first one must have the names of the candidate variables and the other one the description of such variables

data

A data frame where all variables are stored in different columns

referenceframe

A data frame similar to data, but with only the control population

Value

A data frame where each observation has been conditionally z-transformed, given control data

Examples

	## Not run: 
# 	# Start the graphics device driver to save all plots in a pdf format
# 	pdf(file = "Example.pdf")
# 	# Get the stage C prostate cancer data from the rpart package
# 	library(rpart)
# 	data(stagec)
# 	# Split the stages into several columns
# 	dataCancer <- cbind(stagec[,c(1:3,5:6)],
# 	                    gleason4 = 1*(stagec[,7] == 4),
# 	                    gleason5 = 1*(stagec[,7] == 5),
# 	                    gleason6 = 1*(stagec[,7] == 6),
# 	                    gleason7 = 1*(stagec[,7] == 7),
# 	                    gleason8 = 1*(stagec[,7] == 8),
# 	                    gleason910 = 1*(stagec[,7] >= 9),
# 	                    eet = 1*(stagec[,4] == 2),
# 	                    diploid = 1*(stagec[,8] == "diploid"),
# 	                    tetraploid = 1*(stagec[,8] == "tetraploid"),
# 	                    notAneuploid = 1-1*(stagec[,8] == "aneuploid"))
# 	# Remove the incomplete cases
# 	dataCancer <- dataCancer[complete.cases(dataCancer),]
# 	# Load a pre-stablished data frame with the names and descriptions of all variables
# 	data(cancerVarNames)
# 	# Set the group of no progression
# 	noProgress <- subset(dataCancer,pgstat==0)
# 	# z-transform g2 values using the no-progression group as reference
# 	dataCancerZTransform <- rankInverseNormalDataFrame(variableList = cancerVarNames[2,],
# 	                                                   data = dataCancer,
# 	                                                   referenceframe = noProgress)
# 	# Shut down the graphics device driver
# 	dev.off()## End(Not run)