Summarise Signature Bootstraps — sig_summarise

This function computes summary statistics for signature-level bootstraps, including quantiles, min/max contribution proportions, and an experimental p-value.

Usage

sig_summarise_bootstraps(
  bootstraps,
  threshold = 0.05,
  outliers_as_strings = FALSE
)

Arguments

bootstraps: A data frame containing bootstrap results in a sigverse-style format. See sigshared::example_bootstraps() for details.
threshold: The minimum proportion of mutations that must be explained by a signature for it to be considered present in a bootstrap. This threshold is used for calculating the experimental p-value via sig_compute_experimental_p_value().
outliers_as_strings: Logical. Return outliers as a character vector of '|' delimited strings rather than as a list-column (ignored if return_dataframe = FALSE). Can customise the delimiter using the delim argument

Value

A data frame containing the following columns:

signatures: Names of the signatures.
quantiles, iqr, min, max,outlier_thresholds: Statistical summary columns (quantiles, min, and max) of the contribution proportions across bootstraps.
outliers: either a list column or a character of '|' outlier strings (depending on outliers_as_strings argument
p_value: Computed experimental p-value for each signature, based on the threshold.

Examples

library(sigshared)
bootstraps <- example_bootstraps()
sig_summarise_bootstraps(bootstraps, threshold = 0.05)
#>   signatures  min  max     q1     q3   iqr median n outlier_low_threshold
#> 1 Signature1 0.30 0.44 0.3350 0.4050 0.070  0.370 2                 0.230
#> 2 Signature2 0.50 0.69 0.5475 0.6425 0.095  0.595 2                 0.405
#> 3 Signature3 0.01 0.06 0.0225 0.0475 0.025  0.035 2                -0.015
#>   outlier_high_threshold outliers p_value
#> 1                  0.510              0.0
#> 2                  0.785              0.0
#> 3                  0.085              0.5