DEMIPathway-methods: Functional annotation of DEMI results

In demi: Differential Expression from Multiple Indicators

Description

The function DEMIPathway performs functional annotation analysis on DEMI differential expression results stored in the DEMIDiff object. It takes into account the number of up- and down-regulated targets as well as the total number of targets for each functional category to calculate the statistical significance of the functional annotation. PS! This function can only be used if in the underlying DEMIExperiment object the analysis paramater was set as 'gene' or 'transcript' for it will before functional annotation only on genes.

Usage

DEMIPathway(object = "DEMIDiff")

Arguments

object

A DEMIDiff object. The DEMIDiff object contains the results to differential expression analysis that will be used for functional annotation analysis.

Value

Returns the results of the functional annotation analysis in a data.frame.

Author(s)

Sten Ilmjarv

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

# 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 )

# Perform functiona annotation analysis on the DEMI analysis results
demipath <- DEMIPathway( demidiff )


## End(Not run)

demi documentation built on May 2, 2019, 11:11 a.m.