Package 'moire'

Title: Multiplicity of Infection and Allele Frequency Recovery from Noisy Polyallelic Genetics Data
Description: A Markov Chain Monte Carlo (MCMC) based approach to Bayesian estimation of individual level multiplicity of infection, within host relatedness, and population allele frequencies from polyallelic genetic data.
Authors: Maxwell Murphy [aut, cre] , Bryan Greenhouse [aut, ths]
Maintainer: Maxwell Murphy <[email protected]>
License: GPL (>= 3)
Version: 3.3.2
Built: 2024-06-28 01:28:04 UTC
Source: https://github.com/eppicenter/moire

Help Index

Calculate the expected heterozygosity from allele frequencies

Description

Calculate the expected heterozygosity from allele frequencies

Usage

calculate_he(allele_freqs)

Arguments

allele_freqs

Simplex of allele frequencies

Calculate the geometric median of the posterior distribution of allele frequencies

Description

Calculate the geometric median of the posterior distribution of allele frequencies

Usage

calculate_med_allele_freqs(mcmc_results, merge_chains = TRUE)

Arguments

mcmc_results

Result of calling run_mcmc()
merge_chains

boolean indicating that all chain results should be merged

Details

Returns the geometric median of the posterior distribution, defined as the point minimizing the L2 distance from each sampled point.

Calculate naive allele frequencies

Description

Calculate naive allele frequencies

Usage

calculate_naive_allele_frequencies(data)

Arguments

data

List of lists of numeric vectors, where each list element is a collection of observations across samples at a single genetic locus

Details

Estimate naive allele frequencies from the empirical distribution of alleles

Calculate naive COI

Description

Calculate naive COI

Usage

calculate_naive_coi(data)

Arguments

data

List of lists of numeric vectors, where each list element is a collection of observations across samples at a single genetic locus.

Details

Estimates the complexity of infection using a naive approach that chooses the highest number of observed alleles.

Calculate naive COI offset

Description

Calculate naive COI offset

Usage

calculate_naive_coi_offset(data, offset)

Arguments

data

List of lists of numeric vectors, where each list element is a collection of observations across samples at a single genetic locus.
offset

Numeric offset – n'th highest number of observed alleles

Details

Estimates the complexity of infection using a naive approach that chooses the n'th highest number of observed alleles.

Load delimited data

Description

Load delimited data

Usage

load_delimited_data(data, sep = ";", warn_uninformative = TRUE)

Arguments

data

data.frame containing the described data
sep

string used to separate alleles
warn_uninformative

boolean whether or not to print message when removing uninformative loci

Details

Load data.frame with a sample_id column and the remaining columns are loci. Each cell contains a separator delimited string representing the observed alleles at that locus for that sample. Returned data contains vectors sample_ids and loci that are ordered as the results will be ordered from running the MCMC algorithm.

Load long form data

Description

Load long form data

Usage

load_long_form_data(df, warn_uninformative = TRUE)

Arguments

df

data frame with 3 columns: sample_id, locus, allele. Each row is a single observation of an allele at a particular locus for a given sample.
warn_uninformative

boolean whether or not to print message when removing uninformative loci

Details

Long form data is a data frame with 3 columns: sample_id, locus, allele. Returned data contains vectors sample_ids and loci that are ordered as the results will be ordered from running the MCMC algorithm.

MCMC results from using the packaged simulated data and calling run_mcmc()

Description

MCMC results from using the packaged simulated data and calling run_mcmc()

Usage

mcmc_results

Format

An object of class list of length 3.

Plot chain swap acceptance rates

Description

Plot chain swap acceptance rates

Usage

plot_chain_swaps(mcmc_results)

Arguments

mcmc_results

list of results from run_mcmc

Details

Plot the swap acceptance rates for each chain. The x-axis is the temperature, and the y-axis is the swap acceptance rate. The dashed lines indicate the temperatures used for parallel tempering.

Value

list of ggplot objects

Dirichlet distribution

Description

Dirichlet distribution

Usage

rdirichlet(n, alpha)

Arguments

n

total number of draws
alpha

vector controlling the concentration of simplex

Details

Implementation of random sampling from a Dirichlet distribution

Sample from the target distribution using MCMC

Description

Sample from the target distribution using MCMC

Usage

run_mcmc(
  data,
  is_missing = FALSE,
  allow_relatedness = TRUE,
  thin = 1,
  burnin = 10000,
  samples_per_chain = 1000,
  verbose = TRUE,
  use_message = FALSE,
  eps_pos_alpha = 1,
  eps_pos_beta = 1,
  eps_neg_alpha = 1,
  eps_neg_beta = 1,
  r_alpha = 1,
  r_beta = 1,
  mean_coi_shape = 0.1,
  mean_coi_scale = 10,
  max_eps_pos = 2,
  max_eps_neg = 2,
  max_coi = 40,
  num_chains = 1,
  num_cores = 1,
  pt_chains = 1,
  pt_grad = 1,
  pt_num_threads = 1,
  adapt_temp = TRUE,
  pre_adapt_steps = 25,
  temp_adapt_steps = 25,
  max_initialization_tries = 10000
)

Arguments

data

Data to be used in MCMC, as generated by the ⁠load_*_data⁠ functions
is_missing

Boolean matrix indicating whether the observation should be treated as missing data and ignored. Number of rows equals the number of loci, number of columns equals the number samples. Alternatively, the user may pass in FALSE if no data should be considered missing.
allow_relatedness

Bool indicating whether or not to allow relatedness within host
thin

Positive Integer. How often to sample from mcmc, 1 means do not thin
burnin

Positive Integer. Number of MCMC samples to discard as burnin
samples_per_chain

Positive Integer. Number of samples to take after burnin
verbose

Logical indicating if progress is printed
use_message

Logical indicating if progress is printed using message or print
eps_pos_alpha

Positive Numeric. Alpha parameter in Beta distribution for eps_pos prior
eps_pos_beta

Positive Numeric. Beta parameter in Beta distribution for eps_pos prior
eps_neg_alpha

Positive Numeric. Alpha parameter in Beta distribution for eps_neg prior
eps_neg_beta

Positive Numeric. Beta parameter in Beta distribution for eps_neg prior
r_alpha

Positive Numeric. Alpha parameter in Beta distribution for relatedness prior
r_beta

Positive Numeric. Beta parameter in Beta distribution for relatedness prior
mean_coi_shape

shape parameter for gamma hyperprior on mean COI
mean_coi_scale

scale parameter for gamma hyperprior on mean COI
max_eps_pos

Numeric. Maximum allowed value for eps_pos
max_eps_neg

Numeric. Maximum allowed value for eps_neg
max_coi

Positive Numeric. Maximum allowed complexity of infection
num_chains

Total number of chains to run, possibly simultaneously
num_cores

Total OMP parallel threads to use to run chains. num_cores * pt_num_threads should not exceed the number of cores available on your system.
pt_chains

Total number of chains to run with parallel tempering or a vector containing the temperatures that should be used for parallel tempering.
pt_grad

Power to raise parallel tempering chains to. A value of 1 results in evenly distributed temperatures between [0,1], below 1 will bias towards 1 and above 1 will bias towards 0. Only used if pt_chains is a single value (i.e. not a vector).
pt_num_threads

Total number of OMP parallel threads to be used to process parallel tempered chains num_cores * pt_num_threads should not exceed the number of cores available on your system.
adapt_temp

Logical indicating whether or not to adapt the parallel tempering temperatures. If TRUE, the temperatures will be adapted during the burnin period, starting after pre_adapt_steps steps. The adaptation will occur every temp_adapt_steps steps until burnin is complete. The range of temperatures will remain the same as specified by pt_chains.
pre_adapt_steps

Number of steps to take before starting to adapt the parallel tempering temperatures. Only used if adapt_temp is TRUE.
temp_adapt_steps

Number of steps to take between temperature adaptation steps. Only used if adapt_temp is TRUE.
max_initialization_tries

Number of times to try to initialize the chain before giving up

Simulate allele frequencies

Description

Simulate allele frequencies

Usage

simulate_allele_frequencies(alpha, num_loci)

Arguments

alpha

vector parameter controlling the Dirichlet distribution
num_loci

total number of loci to draw

Details

Simulate allele frequency vectors as a draw from a Dirichlet distribution

Simulate data generated according to the assumed model

Description

Simulate data generated according to the assumed model

Usage

simulate_data(
  mean_coi = NULL,
  num_samples,
  epsilon_pos,
  epsilon_neg,
  sample_cois = NULL,
  locus_freq_alphas = NULL,
  allele_freqs = NULL,
  internal_relatedness_alpha = 0,
  internal_relatedness_beta = 1,
  internal_relatedness = NULL,
  missingness = 0
)

Arguments

mean_coi

Mean multiplicity of infection drawn from a Poisson
num_samples

Total number of biological samples to simulate
epsilon_pos

False positive rate, expected number of false positives
epsilon_neg

False negative rate, expected number of false negatives
sample_cois

List of sample COIs to be used instead of simulating
locus_freq_alphas

List of alpha vectors to be used to simulate from a Dirichlet distribution to generate allele frequencies.
allele_freqs

List of allele frequencies to be used instead of simulating allele frequencies
internal_relatedness_alpha

alpha parameter of beta distribution controlling the random relatedness draws for each sample
internal_relatedness_beta

beta parameter of beta distribution controlling the random relatedness draws for each sample
internal_relatedness

List of internal relatedness values to be used instead of simulating
missingness

probability of data being missing

Value

Simulated data that is structured to go into the MCMC sampler

Simulates the observation process

Description

Simulates the observation process

Usage

simulate_observed_allele(alleles, epsilon_pos, epsilon_neg, missingness)

Arguments

alleles

A numeric vector representing the number of strains contributing each allele
epsilon_pos

expected number of false negatives
epsilon_neg

expected number of false positives
missingness

probability that the data is missing

Details

Takes a numeric value representing the number of strains contributing an allele and returns a binary vector indicating the presence or absence of the allele.

Simulate observed genotypes

Description

Simulate observed genotypes

Usage

simulate_observed_genotype(
  true_genotypes,
  epsilon_pos,
  epsilon_neg,
  missingness
)

Arguments

true_genotypes

a list of numeric vectors that are input to sim_observed_allele
epsilon_pos

expected number of false positives
epsilon_neg

expected number of false negatives
missingness

probability of data being missing

Details

Simulate the observation process across a list of observation vectors

Simulate sample COI

Description

Simulate sample COI

Usage

simulate_sample_coi(num_samples, mean_coi)

Arguments

num_samples

the total number of biological samples to simulate
mean_coi

mean multiplicity of infection

Details

Simulate sample COIs from a zero-truncated Poisson distribution

Simulate sample genotype

Description

Simulate sample genotype

Usage

simulate_sample_genotype(sample_cois, locus_allele_dist, internal_relatedness)

Arguments

sample_cois

Numeric vector indicating the multiplicity of infection for each biological sample
locus_allele_dist

Allele frequencies – simplex parameter of a multinomial distribution
internal_relatedness

numeric 0-1 indicating the probability for a strain's allele to come from an existing lineage within host

Details

Simulates sampling the genetics at a single locus given an allele frequency distribution and a vector of sample COIs

Simulated genotyping data

Description

A simulated dataset created using simulate_data()

Usage

simulated_data

Format

An object of class list of length 9.

Summarize Function of Allele Frequencies

Description

Summarize Function of Allele Frequencies

Usage

summarize_allele_freq_fn(
  mcmc_results,
  fn,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc()
fn

Function that takes as input a simplex to apply to each allele frequency vector
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
merge_chains

boolean indicating that all chain results should be merged

Details

General function to summarize the posterior distribution of functions of the sampled allele frequencies

Summarize allele frequencies

Description

Summarize allele frequencies

Usage

summarize_allele_freqs(
  mcmc_results,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc()
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
merge_chains

boolean indicating that all chain results should be merged

Details

Summarize individual allele frequencies from the posterior distribution of sampled allele frequencies

Summarize COI

Description

Summarize COI

Usage

summarize_coi(
  mcmc_results,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  naive_offset = 2,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
naive_offset

Offset used in calculate_naive_coi_offset
merge_chains

boolean indicating that all chain results should be merged

Details

Summarize complexity of infection results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of COI for each biological sample, as well as naive estimates of COI.

Summarize effective COI

Description

Summarize effective COI

Usage

summarize_effective_coi(
  mcmc_results,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc()
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
merge_chains

boolean indicating that all chain results should be merged

Details

Summarize effective COI from MCMC. Returns a dataframe that contains summaries of the posterior distribution of effective COI for each biological sample.

Summarize epsilon_neg

Description

Summarize epsilon_neg

Usage

summarize_epsilon_neg(
  mcmc_results,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc()
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
merge_chains

boolean indicating that all chain results should be merged

Details

Summarize epsilon negative results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of epsilon negative for each biological sample.

Summarize epsilon_pos

Description

Summarize epsilon_pos

Usage

summarize_epsilon_pos(
  mcmc_results,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc()
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
merge_chains

boolean indicating that all chain results should be merged

Details

Summarize epsilon positive results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of epsilon positive for each biological sample.

Summarize locus heterozygosity

Description

Summarize locus heterozygosity

Usage

summarize_he(
  mcmc_results,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc()
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
merge_chains

Merge the results of multiple chains into a single summary

Details

Summarize locus heterozygosity from the posterior distribution of sampled allele frequencies.

Summarize relatedness

Description

Summarize relatedness

Usage

summarize_relatedness(
  mcmc_results,
  lower_quantile = 0.025,
  upper_quantile = 0.975,
  merge_chains = TRUE
)

Arguments

mcmc_results

Result of calling run_mcmc()
lower_quantile

The lower quantile of the posterior distribution to return
upper_quantile

The upper quantile of the posterior distribution to return
merge_chains

boolean indicating that all chain results should be merged

Details

Summarize relatedness results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of relatedness for each biological sample.