Welcome to the PopGenHelpR vignette, please contact the authors if you have any questions about the package. You can also visit our Github for additional examples (https://kfarleigh.github.io/PopGenHelpR/).

```
# Load the package
library(PopGenHelpR)
```

`PopGenHelpR`

is a one-stop package for data analysis and
visualization. `PopGenHelpR`

can calculate commonly used
population genomic statistics such as heterozygosity and genetic
differentiation, with the functions `Heterozygosity`

,
`Differentiation`

, and `Private.alleles`

. While
also producing publication-quality figures using the functions
`Ancestry_barchart`

, `Network_map`

,
`Pairwise_heatmap`

, and `Piechart_map`

. Check out
the vignette below to see all of these functions in action!

Fig 1. A visualization of the `PopGenHelpR`

workflow.

`PopGenHelpR`

`PopGenHelpR`

is designed to be easy to use, but this also
means that you need to ensure that your data is in order before analysis
and pay attention to any warnings output by the functions.

**Data is assumed to be bi-allelic**. Please see the examples on filtering your vcf files to contain biallelic SNPs using vcftools and bcftools, respectively.

```
# vcftools
--vcf myfile.vcf --max-alleles 2 --recode --recode-INFO-all --out my_biallelic_file.vcf
vcftools
# bcftools
-m2 -M2 -v snps myfile.vcf > my_biallelic_file.vcf bcftools view
```

First, we will load the data. These data objects are examples of data
types that can be used in the functions in `PopGenHelpR`

.

```
data("Fst_dat")
data("Het_dat")
data("Q_dat")
data("HornedLizard_Pop")
data("HornedLizard_VCF")
```

Statistical analysis is a critical component of population genomics
study, but many R packages only calculate a subset of commonly used
population genomic statistics. `PopGenHelpR`

seeks to remedy
this by allowing researchers to calculate widely used diversity and
differentiation measures in a single package.

Heterozygosity is a fundamental statistic in population genomics that
allows researchers to evaluate the genetic diversity of individuals and
populations. `PopGenHelpR`

can estimate seven measures of
heterozygosity (individual and population). Here, we will calculate
observed heterozygosity, but please see the documentation for
`Heterozygosity`

to see all of the options. Better yet, check
out our article
on heterozygosity and when to use each measure!

All we need is a vcf or geno file, a population assignment file, and
the statistic you wish to estimate (`PopGenHelpR`

does them
all by default). Note that `PopGenHelpR`

assumes that the
first column indicates sample names and the second column indicates the
population to which each individual is assigned. You can use the
arguments `individual_col`

and `population_col`

to
specify which column indicates the sample and population names,
respectively. You can also write the results to a csv if you set
`write = TRUE`

.

`<- Heterozygosity(data = HornedLizard_VCF, pops = HornedLizard_Pop, statistic = "Ho") Obs_Het `

Differentiation is another basic analysis in population genomic
studies. `PopGenHelpR`

allows you to estimate
*F _{ST}*,

`Heterozygosity`

, all we need is
a vcf or geno file, a population assignment file, and the statistic you
want to calculate (`PopGenHelpR`

does them all by default).
Again, individual and population columns are assumed to be the first and
second columns but can be indicated by users with
`individual_col`

and `population_col`

,
respectively.`<- Differentiation(data = HornedLizard_VCF, pops = HornedLizard_Pop, statistic = "Fst") Fst `

Finally, we will calculate the number of private alleles in each
population. This analysis is often used to evaluate signals of range
expansion and helps researchers identify populations that harbor unique
alleles. Note that `Private.alleles`

can only use a vcf (no
geno files) and does not require you to specify a statistic (all you
absolutely need is a vcf and population file). Otherwise, it operates
just like `Heterozygosity`

or
`Differentiation`

.

`<- Private.alleles(data = HornedLizard_VCF, pops = HornedLizard_Pop) PA `

Letâ€™s move onto visualizations (the fun part), so you can get your work submitted!

A strength of `PopGenHelpR`

is its ability to generate
publication-quality figures. You can generate commonly used figures such
as ancestry plots (bar charts and piechart maps), sample maps, and other
figures such as the `Network_map`

that visualizes
relationships between points on a map.

`PopGenHelpR`

can generate commonly used ancestry
visualizations such as structure-like plots and ancestry piechart maps.
First, we will create structure-like plots for individuals and
populations. We need a q-matrix, population assignments for each
individual, and the number of genetic clusters (K). The q-matrix
represents the contribution of each cluster (K) to an individual or
population and can be obtained from programs like STRUCTURE, ADMIXTURE,
or sNMF. Please see our article
on how to extract the q-matrix from these programs or email Keaka
Farleigh.

```
# First, we separate the list elements into two separate objects. The q-matrix (Qmat) and the locality information for each individual (Loc).
<- Q_dat[[1]]
Qmat <- Q_dat[[2]]
Loc
# Now we will generate both population and individual plots by setting plot.type to 'all'. If you wanted, you could only generate individual or population plots by setting plot.type to "individual" and "population", respectively.
<- Ancestry_barchart(anc.mat = Qmat, pops = Loc, K = 5,
Test_all plot.type = 'all', col = c('#d73027', '#f46d43', '#e0f3f8', '#74add1', '#313695'))
$`Individual Ancestry Plot` Test_all
```

We can also generate an ancestry matrix by population. The ancestry of each population is calculated by averaging the ancestry of the individuals in a particular population.

`$`Population Ancestry Plot` Test_all`

Now, we will generate piechart maps of ancestry using the
`Piechart_map`

function. `Piechart_map`

requires
the same input as `Ancestry_barchart`

with the additional
requirement of coordinates for each individual/population. Youâ€™ll notice
that the individual map looks weird; the pie charts have a bunch of
partitions. Thatâ€™s because we have multiple individuals at the same
location, so the population map is probably a better choice. Instead of
layering individuals on top of each other, the population map averages
the ancestry of individuals in a population before mapping. See our
GitHub for additional examples (https://kfarleigh.github.io/PopGenHelpR/).

```
# First, we seperate the list elements into two seperate objects. The q-matrix (Qmat) and the locality information for each individual (Loc).
<- Q_dat[[1]]
Qmat <- Q_dat[[2]]
Loc
# Now we will generate both population and individual plots by setting plot.type to 'all'. If you wanted, you could only generate individual or population plots by setting plot.type to "individual" and "population", respectively.
<- Piechart_map(anc.mat = Qmat, pops = Loc, K = 5,plot.type = 'all', col = c('#d73027', '#f46d43', '#e0f3f8', '#74add1', '#313695'),
Test_all_piemap Lat_buffer = 1, Long_buffer = 1)
$`Individual Map` Test_all_piemap
```

Notice the weird partitions? We can take care of those using the population piechart map.

`$`Population Map` Test_all_piemap`

`PopGenHelpR`

can use symmetric matrices such as those
output by the `Differentiation`

function to plot heatmaps and
network maps. These plots can be great for understanding the
relationships between populations or individuals.

First, we will use the `Pairwise_heatmap`

function, which
allows us to see relationships between populations or individuals and
only requires a symmetric matrix and legend label (statistic argument).
You can also supply a color vector like we do below, but it is not
required.

`<- Pairwise_heatmap(Fst_dat[[1]], statistic = "Fst", col = c("#0000FF", "#FF0000")) PW_hmap `

We can also visualize these relationships on a map using the
`Network_map`

function. This function allows us to visualize
pairwise relationships as the color of links between the points. You
must supply a symmetric matrix (dat argument) and population assignment
file (pops argument). The remaining arguments are optional, but they
allow for greater customization. The neighbors argument, for example,
tells the function how many relationships to visualize, and you can also
use it to specify relationships you want to see. Please see the
documentation for details.

```
<- Network_map(Fst_dat[[1]], pops = Fst_dat[[2]], neighbors = 2, statistic = "Fst")
NW_map $Map NW_map
```

`Network_map`

can also be used to plot specific
relationships. Letâ€™s isolate the populations with the highest and lowest
Fst by supplying a character vector to the neighbors argument.

```
<- Network_map(Fst_dat[[1]], pops = Fst_dat[[2]], neighbors = c("East_West", "East_South"), statistic = "Fst")
NW_map2 $Map NW_map2
```

`PopGenHelpR`

can create maps using output from
`Heterozygosity`

or csv files from external programs to
understand how diversity (or other statistics) is distributed across
geographic space.

We will plot some observed heterozygosity data with the function
`Point_map`

. All you need is a data frame (or csv) and the
name of whatever statistic you are plotting (statistic argument).
`Point_map`

also assumes that the coordinate column names are
Latitude and Longitude.

```
<- Point_map(Het_dat, statistic = "Heterozygosity")
Het_map $`Heterozygosity Map` Het_map
```

We can also outline the points by setting the `out.col`

argument.

```
<- Point_map(Het_dat, statistic = "Heterozygosity", out.col = "#000000")
Het_map2 $`Heterozygosity Map` Het_map2
```

Finally, we can just plot coordinates using
`Plot_coordinates`

. All we need is a data frame or csv file
with the coordinates for each row indicated by columns names Latitude
and Longitude. You can change the size of the points with the
`size`

argument.

```
<- Plot_coordinates(HornedLizard_Pop)
Sample_map Sample_map
```

Thank you for your interest in our package; please reach out to Keaka
Farleigh (farleik@miamioh.edu) with any questions, things that
should be included in future versions of the package, or if you would
like to be kept up to date with `PopGenHelpR.`