Principal component analysis in PopGenHelpR
+ + + Source:vignettes/articles/PopGenHelpR_PCA.Rmd
+ PopGenHelpR_PCA.RmdOverview +
+Principal component analysis (PCA) is a widely used technique to +identify patterns of genetic structure in genomic data or any data +really. PCA is commonly paired with structure-like analyses since PCA is +model-free, meaning that it is not based on any biological model (see +Patterson et al., 2006 for a discussion on model vs model-free +approaches).
+We will perform PCA and visualize the results. Note that
+we use ggplot2 to visualize the results, not
+PopGenHelpR.
Performing a PCA in PopGenHelpR
+
+Running a PCA in PopGenHelpR is straightforward and only
+requires the genetic data. One caveat is that the data must be complete,
+meaning that there is no missing data. This means that you will have to
+impute most genomic data sets, or perform stringent filtering; I usually
+use LEA to impute my data (Frichot et al., 2015).
+HL_pca <- PCA(HornedLizard_VCF)Our HL_pca object is a list with two elements. First, we
+have the loadings of each individual (sample) on the principal
+components. Second, we have the percent variance explained by each
+principal component (PC). We expect that the first few PCs will explain
+the majority of the variance, and most researchers generate PCA scatter
+plots using the first few PCs.
Visualizng the PCA results +
+Let’s see how much variance is explained by the first 10 PCs.
+
+Var_exp <- as.data.frame(t(HL_pca$`Variance Explained`))
+Var_exp$PC <- seq(1:nrow(Var_exp))
+
+## Plot the percent variance explained
+
+ggplot(Var_exp, aes(x = PC, y = `Percent variance explained`)) + geom_bar(stat = "identity") + theme_classic() 
We see that the first two principal components account for the +majority of the variance, so we will generate a pca scatter plot using +those axes. We will color the points according to which +population/genetic cluster they belong to. This is commonly done to see +if both model-free (e.g., PCA) and model-based (e.g., sNMF) analyses +agree.
+This will also require additional information (a population +assignment file) to color the points.
+
+# Get the population information
+Pop <- HornedLizard_Pop
+
+# Check to see if the PCA individuals and Pop indivudals are ordered in the same way, we expect it to be TRUE
+rownames(Dat_loadings) == Pop$Sample
+
+# Isolate loadings for the first 2 PCs
+Scores_toplot <- as.data.frame(Dat_loadings[,1:2])
+Scores_toplot$group <- Pop$Population
+
+# Set colors for each group
+Scores_toplot$group[Scores_toplot$group == 'South'] <- "#d73027"
+Scores_toplot$group[Scores_toplot$group == 'East'] <- "#74add1"
+Scores_toplot$group[Scores_toplot$group == 'West'] <- "#313695"
+
+# Create a custom theme
+theme<-theme(panel.background = element_blank(),panel.border=element_rect(fill=NA),
+ panel.grid.major = element_blank(),panel.grid.minor = element_blank(),
+ strip.background=element_blank(),axis.text.x=element_text(colour="black"),
+ axis.text.y=element_text(colour="black"),axis.ticks=element_line(colour="black"),
+ plot.margin=unit(c(1,1,1,1),"line"))
+
+# Plot and include the variance explained by the axes wer are plotting
+ggplot(Scores_toplot, aes(x = PC1, y = PC2)) +
+ geom_point(shape = 21, color = "black", fill = Scores_toplot$group, size = 3) +
+ scale_shape_identity() + theme + ylab(paste("PC2 (", round(Dat_pc_var[2,1],2),"% variance explained)", sep = "")) + xlab(paste("PC1 (", round(Dat_pc_var[1,1],2),"% variance explained)", sep = ""))
We see that there are 3 main clusters in our PCA and that the +individuals largely cluster by the population/genetic cluster that was +assigned by sNMF, with the exception of sample E_71_7760.
+Questions??? +
+Please email Keaka Farleigh (farleik@miamioh.edu) if you need help generating a +q-matrix or with anything else.
+References +
+-
+
- Frichot, E., & François, O. (2015). LEA: An R package for +landscape and ecological association studies. Methods in Ecology and +Evolution, 6(8), 925-929. https://doi.org/10.1111/2041-210X.12382 + +
- Patterson, N., Price, A. L., & Reich, D. (2006). Population +structure and eigenanalysis. PLoS genetics, 2(12), e190. https://doi.org/10.1371/journal.pgen.0020190 + +