plgem.pValue {plgem} | R Documentation |
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
).
plgem.pValue(observedStn, plgemResampledStn, verbose=FALSE)
observedStn |
|
plgemResampledStn |
|
verbose |
|
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 dim
ensions 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)