run.plgem {plgem} | R Documentation |
This function automatically performs model fitting and evaluation, observed and resampled PLGEM-STN determination and selection of differentially expressed genes using the PLGEM method.
run.plgem(esdata, signLev = 0.001, rank = 100, covariateNumb = 1, baselineCondition = 1, Iterations = "automatic", fitting.eval = TRUE, plotFile = FALSE, writeFiles = FALSE, Verbose = FALSE)
esdata |
an object of class ‘ExpressionSet’. |
signLev |
number or array; significance level(s) for the DEG selection, value(s) must be in (0,1). |
rank |
number; the number of genes to be selected according to their PLGEM-STN rank; see details. |
covariateNumb |
number; the covariate used to determine on which samples to fit plgem. |
baselineCondition |
number; the condition to be treated as the baseline. |
Iterations |
number of iterations for the resampling step; if "automatic" it is automatically determined. |
fitting.eval |
logical; if TRUE, the fitting is evaluated generating a diagnostic plot. |
plotFile |
logical; if TRUE, the generated plot is written on a file. |
writeFiles |
logical; if TRUE, the generated list of DEG is written on disk file(s). |
Verbose |
logical; if TRUE, comments are printed out while running. |
The covariateNumb covariate (the 1st one by default) of the phenoData of the ExpressionSet ‘data’ is expected to contain the necessary information about te 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.
The model is fitted on the most replicated condition. When more conditions exist with the max number of replicates, the condition providing the best fit is chosen.
If less than 3 replicates are provided for the condition used for fitting, then the selection is based on ranking according to the observed PLGEM-STN statistics. In this case the first ‘rank’ genes are selected for each comparison.
Otherwise DEG are selected comparing the observed and resampled PLGEM-STN at the ‘sign.lev’ significance level(s), that can be treated as an estimate of the false positive rate. See References for details.
This function returns a list with a number of items that is equal to the number of different significance levels (‘delta’) used as input. Each item 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. In each list-item the values are the observed PLGEM-STN and the names are the DEG probeset ids.
Mattia Pelizzola mattia.pelizzola@gmail.com and Norman Pavelka NXP@stowers-institute.org
N. Pavelka et al., BMC Bioinformatics, 2004 Dec 17;5(1):203; http://www.genopolis.it
plgem.fit
,plgem.obsStn
,plgem.resampledStn
,plgem.write.summary
data(LPSeset) LPSdegList <- run.plgem(esdata = LPSeset)