run.plgem {plgem} | R Documentation

## Wrapper for Power Law Global Error Model (PLGEM) analysis method

### Description

This function automatically performs PLGEM fitting and evaluation, determination of observed and resampled PLGEM-STN values, and selection of differentially expressed genes/proteins (DEG) using the PLGEM method.

### Usage

      run.plgem(esdata, signLev=0.001, rank=100, covariate=1,
        baselineCondition=1, Iterations="automatic", trimAllZeroRows=FALSE,
        zeroMeanOrSD=c("replace", "trim"), fitting.eval=TRUE,
        plotFile=FALSE, writeFiles=FALSE, Verbose=FALSE)

### Arguments

`esdata` | an object of class `ExpressionSet`; see Details for important information on how the `phenoData` slot of this object will be interpreted by the function.
`signLev` | numeric vector; significance level(s) for the DEG selection. Value(s) must be in (0,1).
`rank` | `integer` (or coercible to `integer`); the number of genes or proteins to be selected according to their PLGEM-STN rank. Only used if number of available replicates is too small to perform resampling (see Details).
`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`.
`Iterations` | number of iterations for the resampling step; if `"automatic"` it is automatically determined.
`trimAllZeroRows` | `logical`; if `TRUE`, rows in the data set containing only zero values are trimmed before fitting PLGEM. See help page of function `plgem.fit` for details.
`zeroMeanOrSD` | either `NULL` or `character`; what should be done if a row with non-positive mean or zero standard deviation is encountered before fitting PLGEM? Current options are one of `"replace"` or `"trim"`. Partial matching is used to switch between the options and setting the value to `NULL` will cause the default behaviour to be enforced, i.e. to `"replace"`. See help page of function `plgem.fit` for details.
`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 `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.

The model is fitted on the most replicated condition. When more conditions exist with the maximum number of replicates, the condition providing the best fit is chosen (based on the adjusted r^2). If there is again a tie, the first one is arbitrarily taken.

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 values. In this case the first `rank` genes or proteins are selected for each comparison.

Otherwise DEG are selected comparing the observed and resampled PLGEM-STN values at the `signLev` significance level(s), based on p-values obtained via a call to function `plgem.pValue`. See References for details.

### Value

A `list` of four elements:

`fit` | the input `plgemFit`.
`PLGEM.STN` | a `matrix` of observed PLGEM-STN values (see `plgem.obsStn` for details).
`p-value` | a `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. If ranking method is used due to insufficient number of replicates (see Details), this list will be of length 1 and named `firstXXX`, where `XXX` is the number provided by argument `rank`. 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`, `plgem.deg`, `plgem.write.summary`

### Examples

      data(LPSeset)
      set.seed(123)
      LPSdegList <- run.plgem(esdata=LPSeset, fitting.eval=FALSE)

[Package plgem version 1.60.0 Index]