Add binomial confidence intervals to mutation frequencies.

Uses the binomial distribution to create confidence intervals for mutation frequencies calculated from a single point estimate. Calculating binomial confidence intervals for mutation frequencies is not part of MutSeqR's recommended workflow, but is provided here for users who wish to use it.

Usage

get_binom_ci(
  mf_data,
  sum_col = "sum_min",
  depth_col = "group_depth",
  conf_level = 0.95,
  method = "wilson"
)

Arguments

mf_data

The data frame containing the mutation frequencies per sample. Obtained as an output from calculate_mf.

sum_col

Column name that specifies the mutation count (e.g., sum_min)

depth_col

Column name that specifies the sequencing depth (e.g., total_depth)

conf_level

Confidence interval to calculate, default 95% (0.95)

method

The method used by binom::binom.confint to calculate intervals. Default is "wilson" (recommended).

Value

A mf data frame with added columns indicating the confidence intervals.

Examples

# Example data  consists of 24 mouse bone marrow
# samples exposed to three doses of BaP alongside vehicle controls.
# Libraries were sequenced with Duplex Sequencing using
# the TwinStrand Mouse Mutagenesis Panel which consists of 20 2.4kb
# targets = 48kb of sequence. Example data can be retrieved from
# MutSeqRData, an ExperimentHub data package:
## library(ExperimentHub)
## eh <- ExperimentHub()
## query(eh, "MutSeqRData")
# Mutation frequency data was precalculated using
## mf_data_global <- calculate_mf(mutation_data = eh[["EH9861"]],
##   cols_to_group = "sample",
##   retain_metadata_cols = c("dose_group", "dose"))

 mf <- readRDS(system.file("extdata", "Example_files",
                           "mf_data_global.rds",
                           package = "MutSeqR"))
 confint <- get_binom_ci(
   mf_data = mf,
    sum_col = "sum_min",
    depth_col = "group_depth"
  )