DEMIWrap-methods: A wrapper for DEMI analysis

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

Description

Function demi is a wrapper for the whole DEMI analysis. First it creates a DEMIExperiment object, then uses it to create a DEMIClust object that contains the list of clustered probes and then performs differential expression analysis by running the function DEMIDiff that creates DEMIDiff object. The latter contains the results of the differential expression analysis. It also prints out the results to the working directory. If parameter pathway is set to TRUE, it also performs gene ontology analysis on the results in DEMIDiff object to determine statistically significant gene ontology categories (it also prints out those in the working directory with the file containing the string 'pathway'). It then returns a list containing the DEMIExperiment object where the results have been attached to and a data.frame that contains the functional annotation analysis results. NB! The results will be printed out in the working directory.

Usage

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demi(analysis = "transcript", celpath = character(),
  experiment = character(), organism = character(), maxtargets = 0,
  maxprobes = character(), pmsize = 25, sectionsize = character(),
  group = character(), norm.method = norm.rrank, filetag = character(),
  cluster = list(), clust.method = function() { }, cutoff.pvalue = 0.05,
  pathway = logical())

Arguments

analysis

A character. Defines the analysis type. It can be either 'transcript', 'gene', 'exon' or 'genome'. The default value is 'transcript'. For 'genome' analysis sectionsize parameter needs to be defined as well.

celpath

A character. It can point to the directory containing CEL files or is a vector that points directly to the CEL files.

experiment

A character. A custom name of the experiment defined by the user (e.g. 'myexperiment').

organism

A character. The name of the species the micrroarrays are measuring (e.g. 'homo_sapiens' or 'mus_musculus') given in lowercase and words separated by underscore.

maxtargets

A numeric. The maximum number of allowed targets (e.g. genes or transcripts) one probe can match against. If to set it to 1 it means that the probe can match only one gene. If the analysis is set to 'transcript' the program still calculates the number of matches on genes. Hence a probe matching two transcripts on the same gene would be included but a probe matching two transcripts on different genes would not be included. The value needs to be a positive integer or 0. By default maxtargets is set to 0.

maxprobes

A character. Sets the number of unique probes a target is allowed to have a match against. All the targets that yield more alignments to different probes then set by maxprobes will be scaled down to the number defined by the maxprobes parameter. It can be either a positive integer or set as 'median' or 'max' - 'median' meaning the median number of probes matching to all targets and 'max' meaning the maximum number of probes matching to a target. By default maxprobes is not set which is the same as setting maxprobes to 'max'.

pmsize

A numeric. The minimum number of consecutive nucleotides that need to match perfectly against the target sequence. It can be either 23, 24 or 25. This means that alignments with smaller perfect match size will not be included in the experiment set up. The default value is 25.

sectionsize

A numeric. This is only used if the analysis parameter is set to 'genome'. It defines the length of the genomic target region used in the 'genome' analysis.

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.

norm.method

A function. Defines a function used to normalize the raw expression values. The default normalization function is norm.rank.

filetag

A character. This is a custom string that can be used to identify the experiment. It incorporates it to the names of the output files.

cluster

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

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 a 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.

cutoff.pvalue

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

pathway

A logical. If set to TRUE the functional annotation analysis is done on top of differential expression analysis.

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 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 list containing the DEMIExperiment object where differential expression results have been added to and a data.frame consisting of the functional annotation analysis results.

Author(s)

Sten Ilmjarv

See Also

DEMIExperiment, DEMIClust, DEMIPathway, DEMIDiff, demi.wilcox.test.fast, wilcox.test

Examples

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## 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

# Do DEMI analysis with functional annotation analysis
demires <- demi(analysis = 'gene', celpath = destfolder, group = c( "BRAIN", "UHR" ),
		experiment = 'myexperiment', organism = 'homo_sapiens',
		clust.method = demi.wilcox.test.fast, pathway = TRUE)

# Do DEMI analysis without functional annotation analysis
demires <- demi(analysis = 'gene', celpath = destfolder, group = c( "BRAIN", "UHR" ),
		experiment = 'myexperiment', organism = 'homo_sapiens',
		clust.method = demi.wilcox.test.fast, pathway = FALSE)

# Retrieve results from the created object
head( getResultTable( demires$experiment ) )


## End(Not run)

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

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