Title: Test for a Neutral Evolutionary Model in Cancer Sequencing Data
Version: 0.0.3
Description: Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.
Depends: R (≥ 3.4)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: dplyr, ggplot2, scales, pracma, ggpmisc, cowplot
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
URL: https://github.com/marcjwilliams1/neutralitytestr
BugReports: https://github.com/marcjwilliams1/neutralitytestr/issues
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-02-16 17:31:07 UTC; william1
Author: Marc Williams [aut, cre]
Maintainer: Marc Williams <marcjwilliams1@gmail.com>
Repository: CRAN
Date/Publication: 2021-02-16 18:00:06 UTC

Synthetic sequencing data generated from a evolutionary based cancer simulation.

Description

This data is generated from a neutral evolutionary model where all subclonal mutations are neutral.

Usage

VAFneutral

Format

A vector with variant allele frequencies (VAFs) ranging from 0 to 1

Source

Generated using cancer sequencing simulation https://github.com/marcjwilliams1/CancerSeqSim.jl

Synthetic sequencing data generated from a evolutionary based cancer simulation.

Description

This data is generated from an evolutionary model where there is on subclonal population and all other mutations are neutral passengers.

Usage

VAFselection

Format

A vector with variant allele frequencies (VAFs) ranging from 0 to 1

Source

Generated using cancer sequencing simulation https://github.com/marcjwilliams1/CancerSeqSim.jl

Plot cumulative distribution lsq_plot Plots the cumulative distribution of the data as well as the best fit linear model line.

Description

Plot cumulative distribution lsq_plot Plots the cumulative distribution of the data as well as the best fit linear model line.

Usage

lsq_plot(object)

Arguments

object

neutrality test object

Value

ggplot object.

Examples

lsq_plot(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25))

Testing for neutrality on cancer sequencing data

Description

neutralitytest returns a neutralitytest object which contains the result of various test statistics to test for neutrality as described in Williams et al. Nature Genetics 2018. WARNING: This package has been superseded by MOBSTER, see Caravagna et al. Nature Genetics 2020.

Usage

neutralitytest(
  VAF,
  fmin = 0.1,
  fmax = 0.25,
  read_depth = NULL,
  rho = 0,
  cellularity = 1,
  ploidy = 2
)

Arguments

VAF

Vector of variant allele frequencies (VAFs) from a deep sequencing experiment, numbers should be between 0 and 1

fmin

Minimum VAF of integration range, default is 0.1

fmax

Maximum VAF of integration range, default is 0.25

read_depth

Read depth of sample, if this is specified it will be used to calculate an approptiate integration range. default is NULL in which case the default or inputted fmin and fmax will be used.

rho

Overdispersion of sample if known, default is 0.0. Will be used to calculate integration range if read_depth != NULL

cellularity

Cellularity of sample, default is 1.0. Will be used to calculate integration range if read_depth != NULL

ploidy

Ploidy of the genome, default is 2. Ideally mutations should be filtered for this ploidy before running the test. Will be used to calculate integration range if read_depth != NULL

Value

neutralitytest object which contains test statistics which tests if the sequencing data is consistent a neutral evolutionary model. Test statistics are area between theoretical and empirical curves, kolmogorov distance, mean distance and R^2 statistics from linear model fit. Also returns an estimate of the mutation rate per tumour tumour doubling, the raw VAFs and cumulative distribution

Examples

neutralitytest(runif(100))
neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25)
neutralitytest(VAFneutral, read_depth = 100.0, cellularity = 0.8)

neutralitytestr package

Description

Package to test a neutral evolutionary model on deep sequencing data.

Details

See the README on GitHub

Plot normalized cumulative distribution normalized_plot Plots the (normalized) cumulative distribution of the data as well as the theoretical expectation from a neutral evolutionary model.

Description

Plot normalized cumulative distribution normalized_plot Plots the (normalized) cumulative distribution of the data as well as the theoretical expectation from a neutral evolutionary model.

Usage

normalized_plot(object)

Arguments

object

neutrality test object

Value

ggplot object.

Examples

normalized_plot(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25))

Plot all plots in the package and make composite figure. plot_all Plots histogram, linear model best fit plot and normalized plot and plot and makes composite figure.

Description

Plot all plots in the package and make composite figure. plot_all Plots histogram, linear model best fit plot and normalized plot and plot and makes composite figure.

Usage

plot_all(object)

Arguments

object

neutrality test object

Value

ggplot object.

Examples

plot_all(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25))

Plot VAF histogram vaf_histogram Plots a histogram of the variant allele frequencies.

Description

Plot VAF histogram vaf_histogram Plots a histogram of the variant allele frequencies.

Usage

vaf_histogram(object)

Arguments

object

neutrality test object

Value

ggplot object.

Examples

vaf_histogram(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25))