predict_linear_model_influence: Predict the gene expression level based on condition-specific...
In i3bionet/CoRegFlux: CoRegFlux

Description Usage Arguments Value Examples

Build a linear model and use it to predict the gene expression level from the influence of an experiment

predict_linear_model_influence(network, model,
  train_influence = regulatorInfluence(network, train_expression,
  min_Target), experiment_influence, train_expression, min_Target = 10,
  tol = 1e-10, aliases = NULL, verbose = 0)

`network`	a coregnet object
`model`	A genome-scale metabolic model from a class modelOrg.
`train_influence`	Optional, if is train_expression is provided. An influence matrix as computed by the function `regulatorInfluence()` from CoRegNet
`experiment_influence`	Regulator influence scores for the condition of interest as a named vector with the TF as names.
`train_expression`	Gene expression of the training data set, not necessary if train_influence is supplied. Should be numerical matrix corresponding to the gene expression. Rownames should contain gene names/ids while samples should be in columns.
`min_Target`	Optional. Use in case train_influence is not provided. Default value = 10. See regulatorInfluence for more information.
`tol`	Fluxes values below this threshold will be ignored. Default
`aliases`	Optional, A two columns data.frame containing the name used in the gene regulatory network and their equivalent in the genome-scale metabolic model to allow the mapping of the GRN onto the GEM. The colnames should be geneName_model and geneName_GRN
`verbose`	Default to 0. Give informations about the process status

The predicted genes expressions/states

data("SC_GRN_1")
data("SC_experiment_influence")
data("SC_EXP_DATA")
data("iMM904")
data("aliases_SC")
PredictedGeneState <- predict_linear_model_influence(network = SC_GRN_1,
                    experiment_influence = SC_experiment_influence,
                    train_expression = SC_EXP_DATA,
                    min_Target = 4,
                    model = iMM904,
                    aliases= aliases_SC)

GeneState<-data.frame("Name"=names(PredictedGeneState),
                    "State"=unname(PredictedGeneState))