plgem.obsStn {plgem} | R Documentation

## Computation of Observed PLGEM-STN Statistics

### Description

This function computes observed signal-to-noise ratio (STN) values using PLGEM fitting parameters (obtained via a call to function `plgem.fit`) to detect differential expression in an `ExpressionSet`, containing either microarray or proteomics data.

### Usage

      plgem.obsStn(data, plgemFit, covariate=1, baselineCondition=1,
        verbose=FALSE)

### Arguments

`data` | an object of class `ExpressionSet`; see Details for important information on how the `phenoData` slot of this object will be interpreted by the function.
`plgemFit` | `list`; the output of function `plgem.fit`.
`covariate` | `integer`, `numeric` or `character`; specifies the covariate to be used to distinguish the various experimental conditions from one another. See Details for how to specify the `covariate`.
`baselineCondition` | `integer`, `numeric` or `character`; specifies the condition to be treated as the baseline. See Details for how to specify the `baselineCondition`.
`verbose` | `logical`; if `TRUE`, comments are printed out while running.

### Details

The `phenoData` slot of the `ExpressionSet` given as input is expected to contain the necessary information to distinguish the various experimental conditions from one another. The columns of the `pData` are referred to as ‘covariates’. There has to be at least one covariate defined in the input `ExpressionSet`. The sample attributes according to this covariate must be distinct for samples that are to be treated as distinct experimental conditions and identical for samples that are to be treated as replicates.

There is a couple different ways how to specify the `covariate`: If an `integer` or a `numeric` is given, it will be taken as the covariate number (in the same order in which the covariates appear in the `colnames` of the `pData`). If a `character` is given, it will be taken as the covariate name itself (in the same way the covariates are specified in the `colnames` of the `pData`). By default, the first covariate appearing in the `colnames` of the `pData` is used.

Similarly, there is a couple different ways how to specify which experimental condition to treat as the baseline. The available ‘condition names’ are taken from `unique(as.character(pData(data)[, covariate]))`. If `baselineCondition` is given as a `character`, it will be taken as the condition name itself. If `baselineCondition` is given as an `integer` or a `numeric` value, it will be taken as the condition number (in the same order of appearance as in the ‘condition names’). By default, the first condition name is used.

PLGEM-STN values are a measure of the degree of differential expression between a condition and the baseline:

STN = [mean(condition)-mean(baseline)] / [modeledSpread(condition)+modeledSpread(baseline)],

where:

ln(modeledSpread) = PLGEMslope * ln(mean) + PLGEMintercept

`plgem.obsStn` determines the observed PLGEM-STN values for each gene or protein in `data`; see References for details.

### Value

A `list` of two elements:

`fit` | the input `plgemFit`.
`PLGEM.STN` | a `matrix` of observed PLGEM-STN values. The `rownames` of this matrix are identical to the `rownames` of `data`. The `colnames` represent the different experimental conditions that were compared to the baseline.

### 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.resampledStn`, `plgem.pValue`, `plgem.deg`, `run.plgem`

### Examples

      data(LPSeset)
      LPSfit <- plgem.fit(data=LPSeset)
      LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit)
      head(LPSobsStn[["PLGEM.STN"]])

[Package plgem version 1.60.0 Index]