ICBioMark (version 0.1.4)

pred_refit_panel: Refitted Predictive Model for a Given Panel

Description

A function taking the output of a call to pred_first_fit(), as well as gene length information, and a specified panel (list of genes), and producing a refitted predictive model on that given panel.

Usage

pred_refit_panel(
  pred_first = NULL,
  gene_lengths = NULL,
  model = "T",
  genes,
  biomarker = "TMB",
  marker_mut_types = c("NS", "I"),
  training_data = NULL,
  training_values = NULL,
  mutation_vector = NULL,
  t_s = NULL
)

Arguments

pred_first

(list) A first-fit predictive model as produced by pred_first_fit().

gene_lengths

(dataframe) A dataframe of gene lengths (see example_maf_data$gene_lengths for format).

model

(character) A choice of "T", "OLM" or "Count" specifying how predictions should be made.

genes

(character) A vector of gene names detailing the panel being used.

biomarker

(character) If "TMB" or "TIB", automatically defines marker_mut_types, otherwise this will need to be specified separately.

marker_mut_types

(character) A vector specifying which mutation types groups determine the biomarker in question.

training_data

(list) Training data, as produced by get_mutation_tables() (select train, val or test).

training_values

(dataframe) Training true values, as produced by get_biomarker_tables() (select train, val or test).

mutation_vector

(numeric) Optional vector specifying the values of the training matrix (training_data$matrix) in vector rather than matrix form.

t_s

(numeric) Optional vector specifying the frequencies of different mutation types.

Value

A list with three elements:

• fit, a list including a sparse matrix 'beta' giving prediction weights.

• panel_genes, a sparse (logical) matrix giving the genes included in prediction.

• panel_lengths, a singleton vector giving the length of the panel used.

Examples

# NOT RUN {
example_refit_panel <- pred_refit_panel(pred_first = example_first_pred_tmb,
  gene_lengths = example_maf_data$gene_lengths, genes = paste0("GENE_", 1:10))
# }