run.plgem {plgem}R Documentation

Wrapper for Power Law Global Error Model PLGEM

Description

This function automatically performs model fitting and evaluation, observed and resampled PLGEM-STN determination and selection of differentially expressed genes using the PLGEM method.

Usage

run.plgem(esdata, signLev = 0.001, rank = 100, covariateNumb = 1, baselineCondition = 1, Iterations = "automatic",
fitting.eval = TRUE, plotFile = FALSE, writeFiles = FALSE, Verbose = FALSE)

Arguments

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.

Details

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.

Value

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.

Author(s)

Mattia Pelizzola mattia.pelizzola@gmail.com and Norman Pavelka NXP@stowers-institute.org

References

N. Pavelka et al., BMC Bioinformatics, 2004 Dec 17;5(1):203; http://www.genopolis.it

See Also

plgem.fit,plgem.obsStn,plgem.resampledStn,plgem.write.summary

Examples

data(LPSeset)
LPSdegList <- run.plgem(esdata = LPSeset)

[Package plgem version 1.10.1 Index]