Automatic filtering of signatures for exposure prediction gridded across specific annotation

Usage

auto_predict_grid(
  musica,
  modality,
  signature_res,
  algorithm,
  model_id = NULL,
  result_name = "result",
  sample_annotation = NULL,
  min_exists = 0.05,
  proportion_samples = 0.25,
  rare_exposure = 0.4,
  verbose = TRUE,
  combine_res = TRUE,
  make_copy = FALSE,
  table_name = NULL
)

Arguments

musica: Input samples to predict signature weights
modality: Modality used for posterior prediction (e.g. SBS96)
signature_res: Signatures to automatically subset from for prediction
algorithm: Algorithm to use for prediction. Choose from "lda_posterior", and decompTumor2Sig
model_id: Name of model
result_name: Name for result_list entry to save the results to. Default "result".
sample_annotation: Annotation to grid across, if none given, prediction subsetting on all samples together
min_exists: Threshold to consider a signature active in a sample
proportion_samples: Threshold of samples to consider a signature active in the cohort
rare_exposure: A sample will be considered active in the cohort if at least one sample has more than this threshold proportion
verbose: Print current annotation value being predicted on
combine_res: Automatically combines a list of annotation results into a single result object with zero exposure values for signatures not found in a given annotation's set of samples
make_copy: If FALSE, the inputted musica object is updated and nothing is returned. If TRUE, a new musica object is created and returned. Default FALSE.
table_name: Use modality instead

Value

Returns nothing or a new musica object, depending on the make_copy parameter.

Examples

data(musica_annot)
data(cosmic_v2_sigs)
auto_predict_grid(
  musica = musica_annot, modality = "SBS96",
  signature_res = cosmic_v2_sigs, algorithm = "lda",
  sample_annotation = "Tumor_Subtypes"
)
#> Lung
#> Breast
auto_predict_grid(musica_annot, "SBS96", cosmic_v2_sigs, "lda")