plgem.deg {plgem} | R Documentation

## 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.

### 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.

### 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.

### Author(s)

Mattia Pelizzola mattia.pelizzola@gmail.com

Norman Pavelka normanpavelka@gmail.com

### 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)

[Package plgem version 1.64.0 Index]