Estimate mutation rates

Two component mixture models are fit to all data to estimate global high and low mutation rates for all samples. Estimation of these mutation rates are regularized through the use of informative priors whose parameters can be altered using the arguments defined

Usage

# S3 method for class 'EZbakRArrowData'
EstimateMutRates(
  obj,
  populations = "all",
  pnew_prior_mean = -2.94,
  pnew_prior_sd = 0.3,
  pold_prior_mean = -6.5,
  pold_prior_sd = 0.5,
  pold_est = NULL,
  pold_from_nolabel = FALSE,
  grouping_factors = NULL
)

Arguments

obj

An EZbakRData object

populations

Character vector of the set of mutational populations that you want to infer the fractions of. For example, say your cB file contains columns tracking T-to-C and G-to-A

pnew_prior_mean

logit-Normal mean for logit(pnew) prior.

pnew_prior_sd

logit-Normal sd for logit(pnew) prior.

pold_prior_mean

logit-Normal mean for logit(pold) prior.

pold_prior_sd

logit-Normal sd for logit(pold) prior.

pold_est

Background mutation rate estimates if you have them. Can either be a single number applied to all samples or a named vector of values, where the names should be sample names.

pold_from_nolabel

Fix background mutation rate estimate to mutation rates seen in -label samples. By default, a single background rate is used for all samples, inferred from the average mutation rate across all -label samples. The grouping_factors argument can be specified to use certain -label samples to infer background mutation rates for certain sets of +label samples.

grouping_factors

If pold_from_nolabel is TRUE, then grouping_factors will specify the sample-detail columns in the metadf that should be used to group -label samples by. Average mutation rates in each group of -label samples will be used as the background mutation rate estimate in +label samples with the same values for the relevant metadf columns.

Details

This method expects the input object to contain an on-disk Arrow Dataset in place of an in-memory cB file. Furtheromore, this dataset is expected to be partitioned by the individual samples. This allows EZbakR to load only a single sample worth of data in to memory at a time, which can significantly reduce RAM usage. The input object should be created with EZbakRArrowData().