analyze_bulk {numbat}

Call CNVs in a pseudobulk profile using the Numbat joint HMM

Description

Call CNVs in a pseudobulk profile using the Numbat joint HMM

Usage

analyze_bulk(
  bulk,
  t = 1e-05,
  gamma = 20,
  theta_min = 0.08,
  logphi_min = 0.25,
  nu = 1,
  min_genes = 10,
  exp_only = FALSE,
  allele_only = FALSE,
  bal_cnv = TRUE,
  retest = TRUE,
  find_diploid = TRUE,
  diploid_chroms = NULL,
  classify_allele = FALSE,
  run_hmm = TRUE,
  prior = NULL,
  exclude_neu = TRUE,
  phasing = TRUE,
  verbose = TRUE
)

Arguments

bulk

dataframe Pesudobulk profile

t

numeric Transition probability

gamma

numeric Dispersion parameter for the Beta-Binomial allele model

theta_min

numeric Minimum imbalance threshold

logphi_min

numeric Minimum log expression deviation threshold

nu

numeric Phase switch rate

min_genes

integer Minimum number of genes to call an event

exp_only

logical Whether to run expression-only HMM

allele_only

logical Whether to run allele-only HMM

bal_cnv

logical Whether to call balanced amplifications/deletions

retest

logical Whether to retest CNVs after Viterbi decoding

find_diploid

logical Whether to run diploid region identification routine

diploid_chroms

character vector User-given chromosomes that are known to be in diploid state

classify_allele

logical Whether to only classify allele (internal use only)

run_hmm

logical Whether to run HMM (internal use only)

prior

numeric vector Prior probabilities of states (internal use only)

exclude_neu

logical Whether to exclude neutral segments from retesting (internal use only)

phasing

logical Whether to use phasing information (internal use only)

verbose

logical Verbosity

Value

a pseudobulk profile dataframe with called CNV information

Examples

bulk_analyzed = analyze_bulk(bulk_example, t = 1e-5, find_diploid = FALSE, retest = FALSE)