plgem.pValue: Computation of PLGEM p-values
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 computes p-values for observed PLGEM signal-to-noise ratio (STN) values (typically obtained via a call to plgem.obsStn) from resampled STN values (typically obtained via a call to plgem.resampledStn).

1	plgem.pValue(observedStn, plgemResampledStn, verbose=FALSE)

`observedStn`	`list` containing a `matrix` of observed PLGEM-STN values; output of function `plgem.obsStn`.
`plgemResampledStn`	`list` containing a `matrix` of resampled PLGEM-STN values; output of function `plgem.resampledStn`.
`verbose`	`logical`; if `TRUE`, comments are printed out while running.

The p-value of each given observed STN value is computed based on the quantile that the given value occupies in the corresponding distribution of resampled PLGEM-STN values, based on the following relationship:

p-value = min(2*quantile, 2*(1-quantile))

A matrix with the same dimensions and dimnames as the input observedStn$PLGEM.STN, where each entry represents the p-value of the corresponding observed PLGEM-STN value.

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.fit, plgem.obsStn, plgem.resampledStn, plgem.deg, run.plgem

  data(LPSeset)
  LPSfit <- plgem.fit(data=LPSeset)
  LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit)
  head(LPSobsStn[["PLGEM.STN"]])
  set.seed(123)
  LPSresampledStn <- plgem.resampledStn(data=LPSeset, plgemFit=LPSfit)
  LPSpValues <- plgem.pValue(LPSobsStn, LPSresampledStn)
  head(LPSpValues)