Description Usage Arguments Details Value Author(s) See Also Examples
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.
1 2 3 4 5 6 7 | demisummary(object, target)
## S4 method for signature 'DEMIDiff'
demisummary(object, target)
## S4 method for signature 'DEMIExperiment'
demisummary(object, target)
|
object |
A |
target |
A |
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.
Returns the mean normalized expression levels of the specified targets.
Sten Ilmjarv
DEMIExperiment
,DEMIDiff
,attachResult
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | ## 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)
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