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poolABC

The goal of poolABC is to help users perform model choice and parameter inference using Pool-seq data integrated into an Approximate Bayesian Computation (ABC) framework. This package provides functions to simulate Pool-seq data under models of ecotype formation and to import Pool-seq data from real populations. Two ABC algorithms are implemented to perform parameter estimation and model selection using Pool-seq data. Cross-validation can also be performed to assess the accuracy of ABC estimates and calculate model misclassification.

Installation

You can install the development version of poolABC from GitHub with:

# install.packages("devtools")
devtools::install_github("joao-mcarvalho/poolABC")

Alternatively, you can install the current stable version of poolABC from CRAN:

install.packages("poolABC")

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Version

Install

install.packages('poolABC')

Monthly Downloads

177

Version

1.0.0

License

GPL (>= 3)

Issues

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Repository

https://github.com/joao-mcarvalho/poolABC

Maintainer

João Carvalho

Last Published

August 8th, 2023

Functions in poolABC (1.0.0)

Compute heterozygosity between all pairs of populations

Create SCRM command line for a single origin scenario

Create SCRM command line for a model with two populations

Calculate cross-validation prediction error of parameter estimation

Create SCRM command line for a parallel origin scenario

Import and clean a single file containing data in popoolation2 format

Remove sites with missing data

Check if the major allele is the same in all populations

Compute error in model selection with Approximate Bayesian Computation

Create a header for a _rc file of popoolation2

Parameter estimation with Approximate Bayesian Computation using rejection sampling and recording just the index of accepted simulations

Import multiple files containing data in PoPoolation2 format

Filter the data by the frequency of the minor allele

Calculate the mode of a distribution

Import a single file containing data in popoolation2 format

Compute the fraction of exclusive sites

Compute the fraction of sites fixed between populations

Compute euclidean distance

Force the simulations to contain the required number of loci

Draw parameters from the priors

Obtain the index of SNPs inside a block with defined size

Merge posterior distributions

Matrix of prior limits

Matrix of simulated parameter values

meanExpected_Het

Compute mean expected heterozygosity within a population

Normalize data - adjust values measured on different scales

Compute mode of a locfit object

Parameter estimation with Approximate Bayesian Computation for multiple targets

Perform model selection with Approximate Bayesian Computation

organize.poststat

Organize point estimates from multiple posterior distributions

Randomly select blocks of a given size from several contigs

Pairwise FST among populations

Organize scrm output

plot_Posteriors

Plot multiple posterior distributions

Matrix of simulated parameter values

Prediction error plots for ABC

Prediction error plots for ABC using a list

Back-transform the parameters values

Plot the density estimation of a given parameter

Plot model misclassification

Organize information by contig - for multiple data files

Plot the fit of a summary statistic to the target

Organize information by contigs - for a single data file

Construct matrix of prior limits

Calculate point estimates from the posterior distribution

Simulation of Pooled DNA sequencing

Plot the density estimation of a given parameter

Pairwise FST among populations and across multiple loci

Compute summary statistics from Pooled DNA sequencing

Data frame with an example of observed data

remove_quantileReads

Remove sites using quantiles of the depth of coverage

Compute scaled migration rate limits

remove_realReads

Remove sites, according to their coverage, from real data

Compute the fraction of sites shared between populations

Parameter estimation with Approximate Bayesian Computation using rejection sampling

Remove columns with zero variance

Parameter estimation with Approximate Bayesian Computation using local linear regression

Data frame with an example of observed data

scaled.migration

Compute scaled migration rates

Run scrm and obtain genotypes

Compute summary statistics from observed data

Leave-one-out cross validation of model selection

Parameter estimation with Approximate Bayesian Computation for a single target

Perform an Approximate Bayesian Computation simulation study

summary_modelSelect

Posterior model probabilities

Matrix of summary statistics computed from simulated data

Apply a transformation to the parameters

Compute weighted point estimates

Parameter estimation with Approximate Bayesian Computation with several targets

Calculate the abba portion of the D-statistic

Compute expected heterozygosity within a population

Perform D-statistics analysis

calculate D-statistic

Transform matrix of parameter values

Back-transform matrix of parameter values

Calculate the baba portion of the D-statistic

Remove sites with incorrect depths of coverage