ICBioMark (version 0.1.4)

fit_gen_model_uninteract: Fit Generative Model Without Gene/Variant Type-Specific Interactions

Description

A function to fit a generative model to a mutation dataset that does not incorporate gene/variant-specific effects. Otherwise acts similarly to the function fit_gen_model().

NOTE: fits produced by this model will not be compatible with predictive model fits downstream - it is purely for comparing with full models.

Usage

fit_gen_model_uninteract(
  gene_lengths,
  matrix = NULL,
  sample_list = NULL,
  gene_list = NULL,
  mut_types_list = NULL,
  col_names = NULL,
  table = NULL,
  nlambda = 100,
  n_folds = 10,
  maxit = 1e+09,
  seed_id = 1234,
  progress = FALSE
)

Arguments

gene_lengths

(dataframe) A table with two columns: Hugo_Symbol and max_cds, providing the lengths of the genes to be modelled.

matrix

(Matrix::sparseMatrix) A mutation matrix, such as produced by the function get_table_from_maf().

sample_list

(character) The set of samples to be modelled.

gene_list

(character) The set of genes to be modelled.

mut_types_list

(character) The set of mutation types to be modelled.

col_names

(character) The column names of the 'matrix' parameter.

table

(list) Optional parameter combining matrix, sample_list, gene_list, mut_types_list, col_names, as is produced by the function get_tables().

nlambda

(numeric) The length of the vector of penalty weights, passed to the function glmnet::glmnet().

n_folds

(numeric) The number of cross-validation folds to employ.

maxit

(numeric) Technical parameter passed to the function glmnet::glmnet().

seed_id

(numeric) Input value for the function set.seed().

progress

(logical) Show progress bars and text.

Value

A list comprising three objects:

• An object 'fit', a fitted glmnet model.

• A table 'dev', giving average deviances for each regularisation penalty factor and cross-validation fold.

• An integer 's_min', the index of the regularsisation penalty minimising cross-validation deviance.

• A list 'names', containing the sample, gene, and mutation type information of the training data.

Examples

# NOT RUN {
example_gen_model_unisamp <- fit_gen_model_unisamp(example_maf_data$gene_lengths,
                                                   table = example_tables$train)
print(names(example_gen_model))
# }