data_manipulation

This function ensures that at least one peak for each sample is greater than
a specified threshold (default: 500) and then formats the data frame for
the next steps in the analysis.

Analyzing genetic data obtained from pooled samples.
This package can read in Fragment Analysis output files, process the data,
and score peaks, as well as facilitate various analyses, including cluster
analysis, calculation of genetic distances and diversity indices,
as well as bootstrap resampling for statistical inference. Specifically
tailored to handle genetic data efficiently, researchers can explore
population structure, genetic differentiation, and genetic relatedness
among samples. We updated some functions from Covarrubias-Pazaran et al.
(2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats
and referenced the following to write our genetic analysis functions:
Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008)
<doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973)
<doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006)
<doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008)
<doi:10.1111/j.1541-0420.2008.01010.x>.

Kathleen Kuesters

pooledpeaks

Genetic Analysis of Pooled Samples

Jeffrey Long

Jessica Blanton

Walter Blank

Jeffrey Kovach

Ronald Blanton

data_manipulation function

<dl><dt>marker</dt>
<dd>A data frame containing marker data, where each row represents
a marker and each column represents a sample.</dd>
<dt>threshold</dt>
<dd>The threshold value for peak height. Peaks below this
threshold will be replaced with 0.</dd></dl>

Arguments

Data Manipulation for Marker Data — data_manipulation

<dl>

<dt>marker</dt>
<dd>A data frame containing marker data, where each row represents
a marker and each column represents a sample.</dd>


<dt>threshold</dt>
<dd>The threshold value for peak height. Peaks below this
threshold will be replaced with 0.</dd>

</dl>

data_manipulation: Data Manipulation for Marker Data

Description

Usage

Value

Arguments

Examples