ExportEV: Export an ExpressionView file
In ExpressionView: Visualize biclusters identified in gene expression data

Description Usage Arguments Details Author(s) See Also Examples

Exports the biclusters identified in gene expression data with all the relevant biological data to an XML file that can be read by the ExpressionView Flash applet.

## S4 method for signature 'ISAModules'
ExportEV(biclusters, eset,
         order=OrderEV(biclusters), filename=file.choose(),
         norm=c("sample", "feature", "raw", "x", "y"), cutoff=0.95,
         description=NULL, GO, KEGG, ...)
## S4 method for signature 'Biclust'
ExportEV(biclusters, eset, order, filename, norm,
         cutoff, description, ...) 
## S4 method for signature 'list'
ExportEV(biclusters, eset, order=OrderEV(biclusters),
         filename=file.choose(),
         norm=c("sample", "feature", "raw", "x", "y"),
         cutoff=0.95, description=NULL, ...)

`biclusters`	An `ISAModules` object, a `Biclust` object, or a named list, the last one possibly coming from the `isa2` package.
`eset`	A `ExpressionSet` object containing the gene expression data. Please see below how to use this function on other kind of data.
`order`	A named list (result of the `OrderEV` function) containing the optimal order. If not specified, an ordering with the default parameters is performed.
`filename`	The filename of the output file. If not specified, the file is selected via the user interface.
`norm`	The normalization of the gene expression data. The `isa.normalize` function can normalize (zero mean and unit variance) the data with respect to the genes or the samples. Possible values: ‘`feature`’, ‘`sample`’ and ‘`raw`’. ‘`x`’ is the same as ‘`feature`’ and ‘`y`’ is the same as ‘`sample`’. The default value is ‘`sample`’.
`cutoff`	The cutoff for the coloring is a value between 0 and 1. It represents the fraction of data points taken into account for the density plots. The default value is 0.95, i.e., the extrema of the coloring are chosen in such a way that 95% of the data points can be represented.
`description`	A named list containing an alternative description of the data. By default, the metadata is extracted from `eset`. Please see below how to assemble the data description if you are dealing with data other than gene expression.
`GO`	A list of three `GOListHyperGResult` objects, containing the enrichment calculation results for the three Gene Ontology ontologies, for all modules, as returned by the `ISAGO` function in the `eisa` package. If not specified, then it is calculated automatically.
`KEGG`	A `GOListHyperGResult` object, that contains the of the enrichment calculation results for all modules, against the KEGG pathway database, as returned by the `ISAKEGG` function in the `eisa` package. If not specified, then it is calculated automatically.
`...`	Additional arguments, nothing currently.

If the data is available in the form of a ExpressionSet, the ExportEV function automatically uses the metadata associated with the gene expression data. If the underlying data does not contain any annotations, you can provide them manually, by defining various items in the description list, see the second example below.

Andreas Lüscher andreas.luescher@a3.epfl.ch

OrderEV, LaunchEV, ISA, biclust

## Gene expression data
## We use the acute T-cell lymphocytic leukemia (ALL) data together with
## the Iterative Signature Algorithm (ISA).

## Load the package and the ALL data
library(ExpressionView)
library(eisa)
library(ALL)
library(hgu95av2.db)
data(ALL)

## Initialize random number generator to get reproducible results
set.seed(5)

## Find biclusters (=modules)
## To avoid some minutes of waiting, we just load the data
## set included in the 'eisa' package instead of
## really performing the calculation.
#modules <- ISA(ALL, thr.gene=2.7, thr.cond=1.4)
data(ALLModulesSmall)
modules <- ALLModulesSmall

## Realign the gene exptression matrix to optimize arrangements of
## biclusters 
optimalorder <- OrderEV(modules)

## Export the data to an ExpressionView file
## Don't forget to change the filename
## Not run: ExportEV(modules, ALL, optimalorder, filename="file.evf")


## In-silico data
## We use insilico data together with the ISA and manually annotate the
## data set. Simply explore the data file with the Flash applet to
## figure out where the various annotations are placed.

## Load the package
library(ExpressionView)

## Generate noisy in-silico data with dimensions m x n
m <- 50
n <- 500
data <- isa.in.silico(num.rows=m, num.cols=n, noise=0.1,
                      overlap.row=0)[[1]] 

## Find biclusters (=modules)
modules <- isa(data)

## Annotate the rows and columns of data set
rownames(data) <- paste("row", seq_len(nrow(data)))
colnames(data) <- paste("column", seq_len(ncol(data)))

## Add metadata associated with the rows of the data set
rowdata <- outer(1:nrow(data), 1:sample(1:20, 1), function(x, y) {
  paste("row description (", x, ", ", y, ")", sep="")
})
rownames(rowdata) <- rownames(data)
colnames(rowdata) <- paste("row tag", seq_len(ncol(rowdata)))

## Add metadata associated with the columns of the data set 
coldata <- outer(1:ncol(data), 1:sample(1:20, 1), function(x, y) {
  paste("column description (", x, ", ", y, ")", sep="")
})
rownames(coldata) <- colnames(data)
colnames(coldata) <- paste("column tag", seq_len(ncol(coldata)))

## Merge the different annotations in a single list and 
## add a few global things
description <- list(
experiment=list(
	title="Title", 
	xaxislabel="x-Axis Label",
	yaxislabel="y-Axis Label",
	name="Author", 
	lab="Address", 
	abstract="Abstract", 
	url="URL", 
	annotation="Annotation", 
	organism="Organism"),
coldata=coldata,
rowdata=rowdata
)

## Realign the gene exptression matrix to optimize arrangements of
## biclusters 
optimalorder <- OrderEV(modules)

## Export the data to an ExpressionView file
## Don't forget to change the filename
ExportEV(modules, data, optimalorder, filename="file.evf",
         description=description)