auto_predict_grid: Automatic filtering of signatures for exposure prediction...

In campbio/BAGEL: Novel Mutational Signature Analysis

Description

Automatic filtering of signatures for exposure prediction gridded across specific annotation

Usage

auto_predict_grid(
  bagel,
  table_name,
  signature_res,
  algorithm,
  sample_annotation = NULL,
  min_exists = 0.05,
  proportion_samples = 0.25,
  rare_exposure = 0.4,
  verbose = TRUE,
  combine_res = TRUE,
  seed = 1
)

Arguments

bagel	Input samples to predit signature weights
table_name	Name of table 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", decompTumor2Sig, and deconstructSigs
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
seed	Seed to use for reproducible results, set to null to disable

Value

Results a list of results, one per unique annotation value, if no annotation value is given, returns a single result for all samples, or combines into a single result if combines_res = TRUE

Examples

bay <- readRDS(system.file("testdata", "bagel_annot.rds", package = "BAGEL"))
auto_predict_grid(bagel = bay, table_name = "SBS96",
signature_res = cosmic_v2_sigs, algorithm = "lda",
sample_annotation = "Tumor_Subtypes")
auto_predict_grid(bay, "SBS96", BAGEL::cosmic_v2_sigs, "lda")

campbio/BAGEL documentation built on Oct. 6, 2020, 3:59 a.m.