PopGenHelpR Vignette


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

Overview of 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.

Assumptions of 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.

# vcftools
vcftools --vcf myfile.vcf --max-alleles 2 --recode --recode-INFO-all --out my_biallelic_file.vcf

# bcftools
bcftools view -m2 -M2 -v snps myfile.vcf > my_biallelic_file.vcf

Load the data

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


Genomic Analysis

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.

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


Differentiation is another basic analysis in population genomic studies. PopGenHelpR allows you to estimate FST, Nei’s D (individual and population), and Jost’s D. Like 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.

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

Private alleles

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.

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

Let’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.

Ancestry Plots

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).
Qmat <- Q_dat[[1]]
Loc <- Q_dat[[2]]

# 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.
Test_all <- Ancestry_barchart(anc.mat = Qmat, pops = Loc, K = 5,
plot.type = 'all', col = c('#d73027', '#f46d43', '#e0f3f8', '#74add1', '#313695'))

Test_all$`Individual Ancestry Plot`

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.

Test_all$`Population Ancestry Plot`

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. You’ll notice that the individual map looks weird; the pie charts have a bunch of partitions. That’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).
Qmat <- Q_dat[[1]]
Loc <- Q_dat[[2]]

# 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.
Test_all_piemap <- Piechart_map(anc.mat = Qmat, pops = Loc, K = 5,plot.type = 'all', col = c('#d73027', '#f46d43', '#e0f3f8', '#74add1', '#313695'), 
                                Lat_buffer = 1, Long_buffer = 1)

Test_all_piemap$`Individual Map`

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

Test_all_piemap$`Population Map`

Differentiation visualizations

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.

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

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.

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

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

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

Heterozygosity and Other Visualizations

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.

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

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

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

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.

Sample_map <- Plot_coordinates(HornedLizard_Pop)

Thank you for your interest in our package; please reach out to Keaka Farleigh () 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.