MATLAB APPLICATION DEPLOYMENT - WEB EXAMPLE GUIDE Manuale Utente Scaricare il pdf (Pagina 22)

When the task completes, it returns an R list of filenames that are the output from the task. In this example,

transpose.out now has a list of filenames (of length 1, in this case). The application displays the results in a file

viewer window and also loads them into a matrix so that further manipulation can be performed:

# display the transposed results

file.show(transpose.out$transposed.odf);

# now read the output into a matrix

# so we can do further manipulation in R

data <- read.delim(transpose.out$transposed.odf, as.is=T, header=F, sep="\t",

skip=9, comment="";)

data <- as.matrix(data);

cols <- length(data[1,]);

rows <- length(data[,1]);

You can combine GenePattern analyses with all of the rich statistical functionality of R. For example, you can use R's

plot and legend methods to create graphic output, output JPEGs of your visualized data using savePlot, save

modified matrices to files using save, or summarize and report on the data using your own code. The basic idea to

remember is that GenePattern tasks create result files and those files are available to the R client for processing.

For a list of the GenePattern modules, with links to their documentation, see the

Modules page. For generated R

code that describes the function of and the input parameters for a module, point your browser to

http://localhost:8080/gp/taskWrapperGenerator.jsp?name=TransposeDataset, replacing TransposeDataset with the

name of any GenePattern task.

The GenePattern R library also has convenience methods to read and write GenePattern files (such as res, gct, and

cls files), to enable running of multiple tasks in parallel, to run tasks with input from files that were output from

previous tasks without moving them from the server, and other utilities. Even if you choose not to look in the library,

you can extend the techniques shown above to implement your own analyses.

Using LSIDs from R

As of version 1.3 of the GenePattern server, Life Science Identifiers (LSIDs) can be used instead of task names to

identify tasks for GenePattern to run. For R, this is primarily useful when you want to specify a particular version of a

task for GenePattern to run. The easiest way to specify a particular version of a task is to specify the LSID as an

argument to the R method for a GenePattern task, such as TransposeDataset. For example, the following statement

invokes version 1 rather than the latest version of the TransposeDataset task:

transpose.out <- TransposeDataset(input.filename="all_aml_train.res",

output.file.name="<input.filename_basename>.transposed",

LSID="urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00026

:1")

For more information about LSIDs, see Understanding Version Numbers in the GenePattern Java Client Guide.

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MATLAB APPLICATION DEPLOYMENT - WEB EXAMPLE GUIDE Manuale Utente Pagina 22