DEMIDiff: Creates a `DEMIDiff` object

Description

The DEMIDiff object calculates differential expression and holds the analysis results, results of clustering and the original metadata of the experiment. To retrieve the results from the DEMIDiff object use the the function getResultsTable that returns the results as a data.frame.

Usage

DEMIDiff(cluster = "DEMIClust")

Arguments

cluster

A DEMIClust object. The DEMIClust object that holds the clusters used in the analysis.

Value

A DEMIDiff object.

Details

The DEMIDiff object calculates the differential expression for every cluster in the DEMIClust object set by the cluster parameter. The results are then stored in the DEMIDiff object under the slot result as a DEMIResult object. This object can be retrieved with the function getResult but most of the times it is recommended to use the function getResultTable which returns the results in a data.frame sorted by the FDR values.

Examples

Run this code

## 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 DEMIDiff
# 
# # Set up an experiment.
# demiexp <- DEMIExperiment(analysis = 'gene', celpath = destfolder,
# 		experiment = 'myexperiment', organism = 'homo_sapiens')
# 
# # Create clusters with an optimized wilcoxon's rank sum test incorporated within demi that
# # precalculates the probabilities.
# demiclust <- DEMIClust( demiexp, group = c( "BRAIN", "UHR" ), clust.method = demi.wilcox.test.fast )
# 
# # Calcuate differential expression
# demidiff <- DEMIDiff( demiclust )
# 
# # Retrieve the results in a 'data.frame'
# head( getResultTable( demidiff ) )
# 
# # Attach the results to the original 'DEMIExperiment' object
# demiexp <- attachResult( demiexp, demidiff )
# 
# # Retrieve the results from the 'DEMIExperiment' object
# head( getResultTable( demiexp ) )
# 
# ## End(Not run)