inferMarkerEfficiency: inferMarkerEfficiency

In oyvble/MPSproto: A Quantitative Model for MPS data with complex stutters

inferMarkerEfficiency

Description

Infer marker efficiency

Usage

inferMarkerEfficiency(
  dat,
  AT,
  locNames = NULL,
  runMCMC = FALSE,
  mleOptions = list(maxIter = 1000, delta = 0.1, steptol = 1e-06, seed = 1000),
  mcmcOptions = list(niter = 10000, delta = 0.025, seed = 1),
  verbose = FALSE,
  mleObj = NULL
)

Arguments

dat	Datatable with columns (SampleName,Locus,Coverage)
AT	Vector with analytical threshold per marker
locNames	The order of markers can be specified (otherwise extracted as unique from dataset)
runMCMC	Whether to run MCMC for inferring the posterior distribution
mleOptions	Option input for optimization
mcmcOptions	Option input for MCMC simulation
verbose	Whether to print out progress
mleObj	An output from inferMarkerEfficiency with mle fit (used for directly performing MCMC)

Details

Using the sum of the coverage per marker as data (per sample), the function estimates marker efficiency and sample specific EXP(mu) and CV(omega). Using Maximum likelihood estimation for a gamma distribution. Requires specification of AT to account for dropout markers.

The function also performs MCMC for diagnostic: the mleObj argument allows for possibility for doing MCMC directly.

oyvble/MPSproto documentation built on March 19, 2024, 5:32 a.m.