plgem.write.summary: Write the Result of a PLGEM Analysis to the Working Directory
In plgem: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM)

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

This function writes the output of function plgem.deg or run.plgem to a series of files in the current working directory.

1	plgem.write.summary(x, prefix=NULL, verbose=FALSE)

`x`	`list`; the output of either `plgem.deg` or `run.plgem` (see corresponding help papes for details).
`prefix`	optional `character` to use as a prefix of the file names to be written.
`verbose`	`logical`; if `TRUE`, comments are printed out while running.

This function writes three types of files to the current working directory:

1) A comma-separated text file containing the PLGEM fitting parameters;

2) A comma-separated text file containing the observed STN values and their associated p-values for all performed comparisons; (STN values and p-values from different comparisons appear in different columns, with column headers reflecting the underlying comparison)

3) One or more plain text files containing the identifiers of the significant genes or proteins, with filenames reflecting the specific comparisons that were performed (i.e. which experimental condition was compared to the baseline) and the specific significance threshold that were used in the DEG selection step.

Before files are written, the function checks for existence of files with identical names in the working directory and prompts the user to decide whether to abort the writing process or to overwrite the existing files.

The function returns no value. It is called for its side effect to write files to the working directory.

Mattia Pelizzola mattia.pelizzola@gmail.com

Norman Pavelka normanpavelka@gmail.com

Pavelka N, Pelizzola M, Vizzardelli C, Capozzoli M, Splendiani A, Granucci F, Ricciardi-Castagnoli P. A power law global error model for the identification of differentially expressed genes in microarray data. BMC Bioinformatics. 2004 Dec 17; 5:203; http://www.biomedcentral.com/1471-2105/5/203.

Pavelka N, Fournier ML, Swanson SK, Pelizzola M, Ricciardi-Castagnoli P, Florens L, Washburn MP. Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol Cell Proteomics. 2008 Apr; 7(4):631-44; http://www.mcponline.org/cgi/content/abstract/7/4/631.

plgem.deg, run.plgem

## Not run: 
  data(LPSeset)
  LPSdegList <- run.plgem(LPSeset, fitting.eval=FALSE)
  plgem.write.summary(LPSdegList, prefix="test", verbose=TRUE)
## End(Not run)