plgem.resampledStn {plgem} | R Documentation |
This function computes 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
, containing either microarray or proteomics data.
plgem.resampledStn(data, plgemFit, covariate=1, baselineCondition=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 function plgem.fit . |
covariate |
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 . |
baselineCondition |
integer , numeric or character ;
specifies the condition to be treated as the baseline. See Details for how
to specify the baselineCondition . |
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 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 = [mean(condition)-mean(baseline)] / [modeledSpread(condition)+modeledSpread(baseline)],
where:
ln(modeledSpread) = PLGEMslope * ln(mean) + PLGEMintercept
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 available replicates of the condition used for fitting the model.
A list
of two elements:
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.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.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\nand resampled PLGEM-STN values") lines(density(LPSobsStn[["PLGEM.STN"]], bw=0.01), col="red") legend("topright", legend=c("resampled", "observed"), col=c("black", "red"), lwd=2:1)