predict_exposure: LDA prediction of samples based on existing signatures

In campbio/BAGEL: Novel Mutational Signature Analysis

Description

LDA prediction of samples based on existing signatures

Usage

predict_exposure(
  bagel,
  g,
  table_name,
  signature_res,
  algorithm,
  signatures_to_use = seq_len(ncol(signature_res@signatures)),
  seed = 1,
  verbose = FALSE
)

Arguments

bagel	Input samples to predit signature weights
g	A BSgenome object indicating which genome reference the variants and their coordinates were derived from.
table_name	Name of table used for posterior prediction. Must match the table type used to generate the prediction signatures
signature_res	Signatures to use for prediction
algorithm	Algorithm to use for prediction. Choose from "lda_posterior", decompTumor2Sig, and deconstructSigs
signatures_to_use	Which signatures in set to use (default all)
seed	Seed to use for reproducible results, set to null to disable
verbose	Whether to show intermediate results

Value

Results a result object containing signatures and sample weights

Examples

bay <- readRDS(system.file("testdata", "bagel.rds", package = "BAGEL"))
g <- select_genome("19")
build_standard_table(bay, g, "SBS96", overwrite = TRUE)
predict_exposure(bagel = bay, table_name = "SBS96",
signature_res = BAGEL::cosmic_v2_sigs, algorithm = "lda")

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