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 |
matrix of observed PLGEM STN values; output of
plgem.obsStn . |
plgemResampledStn |
list ; output of
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))
plgem.pValue
returns a matrix with the same dim
ensions
and dimnames
as the input ‘observedStn’, where each entry
represents the p-value of the corresponding observed PLGEM STN value.
Mattia Pelizzola mattia.pelizzola@gmail.com
Norman Pavelka nxp@stowers-institute.org
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. 2007 Nov 19; http://www.mcponline.org/cgi/content/abstract/M700240-MCP200v1
plgem.fit
, plgem.obsStn
,
plgem.resampledStn
, run.plgem
data(LPSeset) LPSfit <- plgem.fit(data=LPSeset) LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit) head(LPSobsStn) set.seed(123) LPSresampledStn <- plgem.resampledStn(data=LPSeset, plgemFit=LPSfit) LPSpValues <- plgem.pValue(LPSobsStn, LPSresampledStn) head(LPSpValues)