plgem.deg {plgem}R Documentation

Selection of differentially expressed genes/proteins using PLGEM

Description

This function selects differentially expressed genes/proteins (DEG) at a given significance level ‘delta’, based on observed PLGEM signal-to-noise ratio (STN) values (typically obtained via a call to plgem.obsStn) and pre-computed p-values (typically obtained via a call to plgem.pValue).

Usage

  plgem.deg(observedStn, plgemPval, delta=0.001, verbose=FALSE)

Arguments

observedStn matrix of observed STN values; output of function plgem.obsStn.
plgemPval matrix of p-values; output of function plgem.pValue.
delta numeric vector; the significance level(s) to be used for the selection of DEG; value(s) must be between 0 and 1 (excluded).
verbose logical; if TRUE, comments are printed out while running.

Details

This function allows for the selection of DEG by setting a significance cut-off on pre-calculated p-values. The significance level ‘delta’ roughly represents the false positive rate of the DEG selection, e.g. if a ‘delta’ of 0.001 is chosen in a microarray dataset with 10000 genes, on average 10 of the selected DEG are expected to be false positives.

Value

This function returns a list with a number of items equal to the number of different significance levels (‘delta’) used as input. Each item of this list 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). Each of these second level list-items is a vector of observed STN values of the genes or proteins that passed the corresponding significance threshold in the corresponding comparison.

Author(s)

Mattia Pelizzola mattia.pelizzola@gmail.com

Norman Pavelka nxp@stowers-institute.org

References

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

See Also

plgem.fit, plgem.obsStn, plgem.resampledStn, plgem.pValue, run.plgem

Examples

  data(LPSeset)
  LPSfit <- plgem.fit(data=LPSeset, fittingEval=TRUE)
  LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit)
  set.seed(123)
  LPSresampledStn <- plgem.resampledStn(data=LPSeset, plgemFit=LPSfit)
  LPSpValues <- plgem.pValue(LPSobsStn, LPSresampledStn)
  LPSdegList <- plgem.deg(observedStn=LPSobsStn, plgemPval=LPSpValues, delta=0.001)

[Package plgem version 1.14.0 Index]