plgem.deg {plgem} | R Documentation |
This function selects differentially expressed genes/proteins (DEG) at a given
significance level ‘delta’, based on observed PLGEM
signal-to-noise ratio (STN) values (typically obtained via a call to
plgem.obsStn
) and pre-computed p-values (typically obtained via
a call to plgem.pValue
).
plgem.deg(observedStn, plgemPval, delta=0.001, verbose=FALSE)
observedStn |
matrix of observed STN values; output of function
plgem.obsStn . |
plgemPval |
matrix of p-values; output of function
plgem.pValue . |
delta |
numeric vector; the significance level(s) to be used for the selection of DEG; value(s) must be between 0 and 1 (excluded). |
verbose |
logical ; if TRUE , comments are printed out while
running. |
This function allows for the selection of DEG by setting a significance cut-off on pre-calculated p-values. The significance level ‘delta’ roughly represents the false positive rate of the DEG selection, e.g. if a ‘delta’ of 0.001 is chosen in a microarray dataset with 10000 genes, on average 10 of the selected DEG are expected to be false positives.
This function returns a list with a number of items equal to the number of
different significance levels (‘delta’) used as input. Each item of
this list is again a list, whose number of items correspond to the number of
performed comparisons (i.e. the number of conditions in the starting
ExpressionSet
minus the baseline). Each of these second level
list-items is a vector of observed STN values of the genes or proteins that
passed the corresponding significance threshold in the corresponding
comparison.
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
, plgem.pValue
,
run.plgem
data(LPSeset) LPSfit <- plgem.fit(data=LPSeset, fittingEval=TRUE) LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit) set.seed(123) LPSresampledStn <- plgem.resampledStn(data=LPSeset, plgemFit=LPSfit) LPSpValues <- plgem.pValue(LPSobsStn, LPSresampledStn) LPSdegList <- plgem.deg(observedStn=LPSobsStn, plgemPval=LPSpValues, delta=0.001)