GPRankTargets {tigre} | R Documentation

## Ranking possible target genes or regulators

### Description

`GPRankTargets` ranks possible target genes by forming optimized models with a fixed transcription factor, a set of known target genes and targets to be tested. The transcription factor and the known targets are always included in the models while the tested targets are tested by including them in the models one at a time. The function determines itself whether to use GPSIM or GPDISIM based on the input arguments.

### Usage

    GPRankTargets(preprocData, TF = NULL, knownTargets = NULL,
                  testTargets = NULL, filterLimit = 1.8,
                  returnModels = FALSE, options = NULL,
                  scoreSaveFile = NULL,
                  datasetName = "", experimentSet = "")
    GPRankTFs(preprocData, TFs, targets,
              filterLimit = 1.8, returnModels = FALSE, options = NULL,
              scoreSaveFile = NULL, datasetName = "", experimentSet = "")

### Arguments

`preprocData` | The preprocessed data to be used.
`TF` | The transcription factor (TF) probe present in all models when TF protein translation model is used. Set to NULL (default) when translation model is not used.
`knownTargets` | The target genes present in all models.
`testTargets` | Target genes that are tested by including them in the models one at a time. Can be names of genes, or a set of indices in preprocData.
`filterLimit` | Genes with an average expression z-score above this figure are accepted after filtering. If this value is 0, all genes will be accepted.
`returnModels` | A logical value determining whether the function returns the calculated models.
`options` | A list of additional arguments to pass to GPLearn.
`scoreSaveFile` | Name of file to save the scores to after processing each gene.
`TFs` | The transcription factors that are tested by including them in the models one at a time.
`targets` | The target genes present in all models.
`datasetName` | For exporting the scores using `export.scores`: Name of the dataset in the database.
`experimentSet` | For exporting the scores using `export.scores`: Name of the experiment set in the database.

### Details

The models are formed by calling `GPLearn`. If there is no value given to the transcription factor, a model without protein translation is used. Without protein translation model, some known targets are needed. If known targets are given, a model is first created with only the transcription factor and the known targets. The parameters extracted from this model are used as initial parameters of the models with test targets.

`GPRankTFs` is very similar to `GPRankTargets`, except it loops over candidate regulators, not candidate targets.

### Value

The function returns a scoreList containing the genes, parameters and log-likelihoods of the models If returnModels is true, the function returns a list of the calculated models.

### Author(s)

Antti Honkela, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence

### See Also

`GPLearn, scoreList, generateModels, export.scores`.

### Examples

    ## Not run:
      # Load a mmgmos preprocessed fragment of the Drosophila developmental
      # time series
      data(drosophila_gpsim_fragment)

      # Get the target probe names
      targets <- c('FBgn0003486', 'FBgn0033188', 'FBgn0035257')
      library(annotate)
      aliasMapping <- getAnnMap("ALIAS2PROBE",
                        annotation(drosophila_gpsim_fragment))
      twi <- get('twi', env=aliasMapping)
      fbgnMapping <- getAnnMap("FLYBASE2PROBE",
                       annotation(drosophila_gpsim_fragment))
      targetProbes <- mget(targets, env=fbgnMapping)

      scores <- GPRankTargets(drosophila_gpsim_fragment, TF=twi,
                              testTargets=targetProbes,
                              options=list(quiet=TRUE),
                              filterLimit=1.8)

    ## End(Not run)

[Package tigre version 1.34.0 Index]