plgem.obsStn {plgem} | R Documentation |
These functions compute observed and resampled signal to noise ratio (STN)
values using PLGEM fitting parameters (obtained via a call to
function plgem.fit
) to detect differential expression in an
ExpressionSet
‘data’, containing either microarray or proteomics
data.
plgem.obsStn(data, plgemFit, covariateNumb=1, baseline.condition=1, verbose=FALSE) plgem.resampledStn(data, plgemFit, covariateNumb=1, baseline.condition=1, iterations="automatic", verbose=FALSE)
data |
an object of class ExpressionSet ; see Details for important
information on how the phenoData slot of this object will be
interpreted by the function. |
plgemFit |
list ; the output of ‘plgem.fit’. |
covariateNumb |
integer (or coercible to integer );
the covariate used to determine on which samples to fit the PLGEM. |
baseline.condition |
integer (or coercible to integer ); the
condition to be treated as the baseline. |
verbose |
logical ; if TRUE , comments are printed out while
running. |
iterations |
number of iterations for the resampling step; if "automatic" it is automatically determined. |
The ‘covariateNumb’ covariate (the 1st one by default) in the
pData
of the ExpressionSet
‘data’ is expected to
contain the necessary information about the experimental design. The values of
this covariate must be sample labels, that have to be identical for samples to
be treated as replicates. In particular, the ExpressionSet
‘data’ must have at least two conditions in the ‘covariateNumb’
covariate; by default the first one is considered the baseline.
PLGEM-STN values are a measure of the degree of differential expression between a condition and the baseline:
PLGEM-STN = [mean(condition)-mean(baseline)] / [modeledSpread(condition)+modeledSpread(baseline)],
where: ln(modeledSpread) = PLGEMslope * ln(mean) + PLGEMintercept
plgem.obsStn
determines the observed PLGEM-STN values for each gene
or protein in ‘data’. plgem.resampledStn
determines the
resampled PLGEM STN values for each gene or protein in ‘data’
using a resampling approach; see References for details. The number of
iterations should be chosen depending on the number of replicates of the
condition used for fitting the model.
plgem.obsStn
returns a matrix
of observed PLGEM STN
values. The rownames
of this matrix are identical to the
rownames
of ‘data’. The colnames
represent
the different experimental conditions that were compared to the baseline.
plgem.resampledStn
returns a list with two items:
RESAMPLED.STN |
matrix of resampled PLGEM STN values, with
rownames identical to those in ‘data’, and
colnames representing the different number of replicates
found in the different comparisons; see References for details. |
REPL.NUMBER |
the number of replicates found for each experimental condition; see References for details. |
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.pValue
, plgem.deg
,
run.plgem
data(LPSeset) LPSfit <- plgem.fit(data=LPSeset) LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit) set.seed(123) LPSresampledStn <- plgem.resampledStn(data=LPSeset, plgemFit=LPSfit) plot(density(LPSresampledStn[["RESAMPLED.STN"]], bw=0.01), col="black", lwd=2, xlab="PLGEM STN values", main="Distribution of observed and resampled PLGEM STN values") lines(density(LPSobsStn, bw=0.01), col="red") legend("topright", legend=c("resampled", "observed"), col=c("black", "red"), lwd=2:1)