predict_linear_model_influence {CoRegFlux} | R Documentation |
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 |
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))