plgem (version 1.44.0)

plgem.deg: Selection of Differentially Expressed Genes/Proteins With PLGEM

Description

This function selects differentially expressed genes/proteins (DEG) at a given significance level, 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

list containing a matrix of observed PLGEM-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.

Value

A list of four elements:

fit

the input plgemFit.

PLGEM.STN

the input matrix of observed PLGEM-STN values (see plgem.obsStn for details).

p-value

the input matrix of p-values (see plgem.pValue for details).

significant

a list with a number of elements equal to the number of different significance levels (delta) used as input. Each element of this list is again a list, whose number of elements 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 elements is a character vector of significant gene/protein names that passed the statistical test at the corresponding significance level.

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 10,000 genes (none of which is truly differentially expressed), on average 10 genes/proteins are expected to be selected by chance alone.

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. 2008 Apr; 7(4):631-44; http://www.mcponline.org/cgi/content/abstract/7/4/631.

See Also

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

Examples

  data(LPSeset)
  LPSfit <- plgem.fit(data=LPSeset, fittingEval=FALSE)
  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)