GPLearn: Fit a GP model

In ahonkela/tigre: Transcription factor Inference through Gaussian process Reconstruction of Expression

Description

Forms an optimized model of the desired genes. The function can form a model with GPsim or GPdisim and it's also possible to use initial parameters or fix parameters for future use. The genes can also be filtered based on ratios calculated from the expression values. The given data can also be searched for the data of specific genes.

Usage

  GPLearn(preprocData, TF = NULL, targets = NULL,
    useGpdisim = !is.null(TF), randomize = FALSE, addPriors = FALSE,
    fixedParams = FALSE, initParams = NULL, initialZero = TRUE,
    fixComps = NULL, dontOptimise = FALSE,
    allowNegativeSensitivities = FALSE, quiet = FALSE,
    gpsimOptions = NULL, allArgs = NULL)

Arguments

preprocData	The preprocessed data to be used.
TF	The probe corresponding to the transcription factor (TF) mRNA if TF protein translation model is used, or NULL (default) if the translation model is not used.
targets	The target genes of the model.
useGpdisim	A logical value determining whether a model of translation is included. By default TRUE if TF is set, FALSE if TF is unset.
randomize	A logical value determining whether the parameters of the model are randomized before optimization.
addPriors	A logical value determining whether priors are added to the model.
fixedParams	A logical value determining whether the initial parameters are fixed.
initParams	The initial parameters for the model. In combination with fixedParams a value NA denotes parameters to learn.
initialZero	Assume a zero initial TF protein concentration, default = TRUE.
fixComps	The blocks of the kernel the parameters of which are to be fixed. To be used together with fixedParams and initParams.
dontOptimise	Just create the model, do not run optimisation.
allowNegativeSensitivities	Allow sensitivities to go negative. This is an experimental feature, and the negative values have no physical interpretation.
quiet	Suppress optimiser output.
gpsimOptions	Internal: additional options to pass to gp[di]simCreate.
allArgs	A list of arguments that can be used to override ones with the same name.

Value

Returns the optimized model.

Author(s)

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

See Also

GPRankTargets, GPRankTFs.

Examples

  # Load a mmgmos preprocessed fragment of the Drosophila developmental
  # time series
  data(drosophila_gpsim_fragment)

  # Get the target probe names
  library(annotate)
  aliasMapping <- getAnnMap("ALIAS2PROBE",
                    annotation(drosophila_gpsim_fragment))
  twi <- get('twi', env=aliasMapping)
  fbgnMapping <- getAnnMap("FLYBASE2PROBE",
                   annotation(drosophila_gpsim_fragment))
  targetProbe <- get('FBgn0035257', env=fbgnMapping)

  # Create the model but do not optimise (rarely needed...)
  model <- GPLearn(drosophila_gpsim_fragment,
                   TF=twi, targets=targetProbe,
                   useGpdisim=TRUE, quiet=TRUE,
                   dontOptimise=TRUE)
## Not run: 
  # Create and learn the model
  model <- GPLearn(drosophila_gpsim_fragment,
                   TF=twi, targets=targetProbe,
                   useGpdisim=TRUE, quiet=TRUE)

## End(Not run)

ahonkela/tigre documentation built on Aug. 6, 2021, 12:08 p.m.