demi (version 1.1.2)

demi.wilcox.test.fast: Cluster probes into higher and lower clusters based on their differential signalling

Description

Performs a modified wilcox.test on normalized expression value matrix defined in DEMIClust object. It precalculates the probabilities of the rank sums and makes the algorithm run a lot quicker.

Usage

demi.wilcox.test.fast(x = "DEMIClust")

Arguments

x

A DEMIClust object. The DEMIClust object containing normalized expression values used for statistical significance test on differential signalling of probes. The object contains the column indexes of groups (e.g. 'test' and 'control') used in the analysis.

Value

A list. Returns a list containing different sets of probes that behave similarly under current statistical test (e.g. up- or down-regulated probes).

See Also

wilcox.test which this function mimics and wprob which this function implements.

Examples

## Not run: 
# 
# # To use the example we need to download a subset of CEL files from
# # http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9819 published
# # by Pradervand et al. 2008.
# 
# # Set the destination folder where the downloaded files fill be located.
# # It can be any folder of your choosing.
# destfolder <- "demitest/testdata/"
# 
# # Download packed CEL files and change the names according to the feature
# # they represent (for example to include UHR or BRAIN in them to denote the
# # features).
# # It is good practice to name the files according to their features which
# # allows easier identification of the files later.
# 
# ftpaddress <- "ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM247nnn"
# download.file( paste( ftpaddress, "GSM247694/suppl/GSM247694.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "UHR01_GSM247694.CEL.gz", sep = "" ) )
# download.file( paste( ftpaddress, "GSM247695/suppl/GSM247695.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "UHR02_GSM247695.CEL.gz", sep = "" ) )
# download.file( paste( ftpaddress, "GSM247698/suppl/GSM247698.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "UHR03_GSM247698.CEL.gz", sep = "" ) )
# download.file( paste( ftpaddress, "GSM247699/suppl/GSM247699.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "UHR04_GSM247699.CEL.gz", sep = "" ) )
# download.file( paste( ftpaddress, "GSM247696/suppl/GSM247696.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "BRAIN01_GSM247696.CEL.gz", sep = "" ) )
# download.file( paste( ftpaddress, "GSM247697/suppl/GSM247697.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "BRAIN02_GSM247697.CEL.gz", sep = "" ) )
# download.file( paste( ftpaddress, "GSM247700/suppl/GSM247700.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "BRAIN03_GSM247700.CEL.gz", sep = "" ) )
# download.file( paste( ftpaddress, "GSM247701/suppl/GSM247701.CEL.gz", sep = "/" ),
# 		destfile = paste( destfolder, "BRAIN04_GSM247701.CEL.gz", sep = "" ) )
# 
# # We need the gunzip function (located in the R.utils package) to unpack the gz files.
# # Also we will remove the original unpacked files for we won't need them.
# library( R.utils )
# for( i in list.files( destfolder ) ) {
# 	gunzip( paste( destfolder, i, sep = "" ), remove = TRUE )
# }
# 
# # Now we can continue the example of the function demi.wilcox.test.fast
# 
# # Basic experiment set up.
# demiexp <- DEMIExperiment(analysis = 'gene', celpath = destfolder,
# 			experiment = 'myexperiment', organism = 'homo_sapiens')
# 
# # Create clusters with default behaviour
# demiclust <- DEMIClust( demiexp, group = c( "BRAIN", "UHR" ) )
# 
# # Retrieve probes whose differential signalling was statistically significant
# sigprobes <- demi.wilcox.test.fast( demiclust )
# 
# # However it makes more sense to incorporate the method straight into \code{DEMIClust} object
# demiclust <- DEMIClust( demiexp, group = c( "BRAIN", "UHR" ), clust.method = demi.wilcox.test.fast )
# 
# # Retrieve the probes whose differential signalling was statistically significant
# sigprobes <- getCluster( demiclust )
# 
# # Retrieve the cluster names since we have both up-regulated and down-regulated probe clusters
# names( sigprobes )
# 
# # Retrieve the up-regulated probes whose cluster names contain the sign '[H]'
# head( sigprobes[[grep("\\[H\\]", names( sigprobes ))]] )
# 
# # Retrieve the down-regulated probes whose cluster names contain the sign '[L]'
# head( sigprobes[[grep("\\[L\\]", names( sigprobes ))]] )
# 
# ## End(Not run)