get_linear_model: Train a linear model
In i3bionet/CoRegFlux: CoRegFlux

Description Usage Arguments Details Value See Also

Here we train a linear regression model of the form x= alpha + beta*I where x is the gene expression of the metabolic genes of the train data set train_expression, alpha is an intercept, I is the influence of the regulators of the training data set and beta are the coefficients.

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get_linear_model(train_expression,
  train_influence = regulatorInfluence(network, train_expression, minTarg
  = 10), network)

`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.
`train_influence`	Optional. Regulator influence scores computed using the function CoRegNet::regulatorInfluence for the training data set, default minTarg = 10
`network`	CoRegNet object use to build the linear model and to compute the influence.

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.

A linear model

predict_linear_model_influence

i3bionet/CoRegFlux documentation built on May 31, 2019, 1:50 a.m.