get_p: Construct Optimisation Parameters.

In ICBioMark: Data-Driven Design of Targeted Gene Panels for Estimating Immunotherapy Biomarkers

Description

An internal function. From the learned generative model and training data, produces a vector of weights p to be used in the subsequent group lasso optimisation, alongside a biomarker-dependent normalisation quantity p_norm.

Usage

get_p(gen_model, training_matrix, marker_mut_types, gene_lengths)

Arguments

gen_model	(list) A generative mutation model, fitted by fit_gen_model().
training_matrix	(sparse matrix) A sparse matrix of mutations in the training dataset, produced by get_mutation_tables().
marker_mut_types	(character) A character vector listing which mutation types (of the set specified in the generative model attribute 'names') constitute the biomarker in question.
gene_lengths	(dataframe) A table with two columns: Hugo_Symbol and max_cds, providing the lengths of the genes to be modelled.

Value

A list with three entries:

• A vector p, with an entry corresponding to each combination of gene and mutation type specified in the generative model fitted. Each component is a non-negative value corresponding to a weighting p to be supplied to a group lasso optimisation.

• A numeric p_norm, giving the factor between p_gs and phi_0gs (see paper for details).

• A vector biomarker_columns, detailing which of the elements of p correspond to gene/mutation type combinations contributing to the biomarker in question.

Examples

p <- get_p(example_gen_model, example_tables$train$matrix,
           marker_mut_types = c("I"), gene_lengths = example_maf_data$gene_lengths)
print(p$p[1:5])
print(p$p_norm)
print(p$bc[1:5])

ICBioMark documentation built on Nov. 15, 2021, 5:09 p.m.