predict_linear_model_influence: Predict the gene expression level based on condition-specific...

In CoRegFlux: CoRegFlux

Description

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

Usage

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)

Arguments

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

Value

The predicted genes expressions/states

Examples

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))

CoRegFlux documentation built on Nov. 8, 2020, 5:21 p.m.