get_linear_model {CoRegFlux} | R Documentation |
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.
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