plgem.deg {plgem} | R Documentation |
This function selects differentially expressed genes/proteins (DEG) at a given
significance level, 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 |
list containing a matrix of observed
PLGEM-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 10,000 genes
(none of which is truly differentially expressed), on average 10
genes/proteins are expected to be selected by chance alone.
A list
of four elements:
fit |
the input plgemFit . |
PLGEM.STN |
the input matrix of observed PLGEM-STN values (see
plgem.obsStn for details). |
p-value |
the input matrix of p-values (see
plgem.pValue for details). |
significant |
a list with a number of elements equal to the number
of different significance levels (delta ) used as input. Each element
of this list is again a list, whose number of elements 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 elements is a character vector of significant gene/protein
names that passed the statistical test at the corresponding significance
level. |
Mattia Pelizzola mattia.pelizzola@gmail.com
Norman Pavelka nxp@stowers.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. 2008 Apr; 7(4):631-44; http://www.mcponline.org/cgi/content/abstract/7/4/631.
plgem.fit
, plgem.obsStn
,
plgem.resampledStn
, plgem.pValue
,
run.plgem
data(LPSeset) LPSfit <- plgem.fit(data=LPSeset, fittingEval=FALSE) 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)