DEMIClust-methods: Creates a 'DEMIClust' object

Description Usage Arguments Details Value Author(s) See Also Examples

Description

A DEMIClust object clusters probes by their expression profile. The clustering is done with a function defined by the clust.method parameter. One could also define custom clusters by defining the cluster parameter with a list of probes. It then stores the clusters of probes as a DEMIClust object.

Usage

1
2
DEMIClust(experiment = "DEMIExperiment", group = character(),
  clust.method = function() { }, cluster = list(), cutoff.pvalue = 0.05)

Arguments

experiment

A DEMIExperiment object. Holds the DEMIExperiment object whose metadata (such as normalized expression values) is used to cluster the probes.

group

A character. Defines the groups that are used for clustering (e.g 'group = c("TEST", "CONTROL")'). It uses grep function to locate the group names from the CEL file names and then builds index vectors determining which files belong to which groups.

clust.method

A function. Defines the function used for clustering. The user can build a custom clustering function. The input of the custom function needs to be the same DEMIClust object and the output is a list of probes, where each list corresponds to a specific cluster. The default function is demi.wilcox.test that implements the wilcox.test function. However we recommend to use the function demi.wilcox.test.fast that uses a custom wilcox.test and runs a lot faster.

cluster

A list. Holds the probes of different clusters in a list.

cutoff.pvalue

A numeric. Sets the cut-off p-value used for determining statistical significance of the probes when clustering the probes into clusters. Default is 0.05.

Details

Instead of automatically clustered probes DEMIClust object can use user defined lists of probes for later calculation of differential expression. This is done by setting the cluster parameter. It overrides the default behaviour of the DEMIClust object and no actual clustering occurs. Instead the list of probes defined in the cluster parameter are considered as already clustered probes. The list needs to contain proper names for probe vectors so that they would be recognizable later. Also instead of using the default clustering method the user can write his/her own function for clustering probes based on the expression values.

Further specification of the parameters:

Value

A DEMIClust object.

Author(s)

Sten Ilmjarv

See Also

DEMIExperiment, demi.wilcox.test, demi.wilcox.test.fast, demi.comp.test, wprob

Examples

 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
## 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 DEMIClust

# Set up an experiment.
demiexp <- DEMIExperiment(analysis = 'gene', celpath = destfolder,
			experiment = 'myexperiment', organism = 'homo_sapiens')

# Create clusters with default behaviour
demiclust <- DEMIClust( demiexp, group = c( "BRAIN", "UHR" ) )

# Create clusters with an optimized wilcoxon's rank sum test incorporated within demi that
# precalculates the probabilities.
# The user can specify his/her own function for clustering.
demiclust <- DEMIClust( demiexp, group = c( "BRAIN", "UHR" ), clust.method = demi.wilcox.test.fast )

# Create a 'DEMIClust' object with custom lists of probeID's
demiclust <- DEMIClust( demiexp, cluster = list( customcluster = c(1190, 1998, 2007) ) )

# To retrieve the clusters use
getCluster( demiclust )

# To retrieve cluster names use
names( getCluster( demiclust ) )


## End(Not run)

demi documentation built on May 30, 2017, 2:40 a.m.