demisummary: Returns the mean normalized expression levels for the specified targets

Description

The function demisummary returns the mean normalized expression levels for the specified targets. It returns the mean expression values for the whole dataset as well as for individual groups. Depending on the analysis parameter of the underlying DEMIExperiment object the target can be ensembl gene ID or gene symbol (e.g. 'MAOB'), ensembl transcript ID, ensembl peptide ID or genomic region ID.

Usage

demisummary(object, target)
"demisummary"(object, target)
"demisummary"(object, target)

Arguments

object

A DEMIExperiment, DEMIDiff object.

target

A vector. Depending on the analysis the target can be ensembl gene ID or gene symbol (e.g. 'MAOB'), ensembl transcript ID, ensembl peptide ID or genomic region ID.

Value

Returns the mean normalized expression levels of the specified targets.

Details

To see available targets used in the analysis you can try head(getAnnotation(x)) where x is an object of class DEMIExperiment. Alternatively you could use head(getAnnotation(getExperiment(y))) where y is of class DEMIDiff.

If no results have been attached to the DEMIExperiment object then it only returns the mean normalized expression values for the whole dataset not for individual groups. To attach results to DEMIExperiment object use the function attachResult(x,y) where x is an object of class DEMIExperiment and y is an object of class DEMIDiff that stores the results.

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 demisummary
# 
# # 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 mean normalized expression values for the specified targets
# demisummary( demiexp, c( "MAOB" ) )
# demisummary( demidiff, "MAOB" )
# 
# # Attach results from 'DEMIDiff' object to 'DEMIExperiment' object
# demiexp_attached <- attachResult( demiexp, demidiff )
# 
# # Retrieve mean normalized expression values again and note these are also retrieved for specific
# # groups
# demisummary( demiexp_attached, "MAOB" )
# 
# ## End(Not run)