plgem.fit) to detect differential expression in an
ExpressionSet, containing either microarray or proteomics data.
plgem.obsStn(data, plgemFit, covariate=1, baselineCondition=1, verbose=FALSE)ExpressionSet; see Details for important
information on how the phenoData slot of this object will be
interpreted by the function.list; the output of function plgem.fit.integer, numeric or character; specifies
the covariate to be used to distinguish the various experimental conditions
from one another. See Details for how to specify the covariate.integer, numeric or character;
specifies the condition to be treated as the baseline. See Details for how
to specify the baselineCondition.logical; if TRUE, comments are printed out while
running.phenoData slot of the ExpressionSet given as input is
expected to contain the necessary information to distinguish the various
experimental conditions from one another. The columns of the pData are
referred to as ‘covariates’. There has to be at least one covariate
defined in the input ExpressionSet. The sample attributes according to
this covariate must be distinct for samples that are to be treated as distinct
experimental conditions and identical for samples that are to be treated as
replicates.
There is a couple different ways how to specify the covariate: If an
integer or a numeric is given, it will be taken as the covariate
number (in the same order in which the covariates appear in the
colnames of the pData). If a character is given, it will
be taken as the covariate name itself (in the same way the covariates are
specified in the colnames of the pData). By default, the first
covariate appearing in the colnames of the pData is used.
Similarly, there is a couple different ways how to specify which experimental
condition to treat as the baseline. The available ‘condition names’ are
taken from unique(as.character(pData(data)[, covariate])). If
baselineCondition is given as a character, it will be taken as
the condition name itself. If baselineCondition is given as an
integer or a numeric value, it will be taken as the condition
number (in the same order of appearance as in the ‘condition names’).
By default, the first condition name is used.
PLGEM-STN values are a measure of the degree of differential expression
between a condition and the baseline: $$
STN = \frac{mean_{condition}-mean_{baseline}}{modeledSpread_{condition}+modeledSpread_{baseline}},$$
where: $$\log{(modeledSpread)}=PLGEMslope*\log{(mean)}+PLGEMintercept$$ plgem.obsStn determines the observed PLGEM-STN values for each gene
or protein in data; see References for details.
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.resampledStn,
plgem.pValue, plgem.deg, run.plgem
data(LPSeset)
LPSfit <- plgem.fit(data=LPSeset)
LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit)
head(LPSobsStn[["PLGEM.STN"]])
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