Introduction

The example is split into 2 Parts:

Part 1 must be completed first to create a file, SNPs_cleaned.csv, that has been completely prepared for analysis.

Now in Part 2, you will analyze the data with PCA. The steps here will be:

  1. Center the data (scale())
  2. Run a PCA analysis (prcomp())
  3. Evaluate the scree plot from the PCA (screeplot())
  4. Evaluate the amount of variation explained by the first 2 PCs.
  5. Extract the PCA scores for plotting (vegan::scores())
  6. Plot the data

Tasks

In the code below all code is provided. Your tasks will be to do 4 things:

  1. Give a meaningful title to all sections marked “TODO: TITLE”
  2. Write 1 to 2 sentences describing what is being done and why in all sections marked “TODO: EXPLAIN”
  3. Add titles and axes to plots in all sections marked “TODO: UPDATE PLOT”
  4. Write 1 or 2 sentences interpreting the output from R in all sections marked “TODO: INTERPRET”

Preliminaries

Load the vcfR package with library()

library(vcfR) # KEY
## 
##    *****       ***   vcfR   ***       *****
##    This is vcfR 1.13.0 
##      browseVignettes('vcfR') # Documentation
##      citation('vcfR') # Citation
##    *****       *****      *****       *****
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.6-4
library(ggplot2)
library(ggpubr)

Set the working directory

Load the data

SNPs_cleaned <- read.csv(file = "SNPs_cleaned.csv")

warning("If this didn't work, its may be because you didn't set your working directory.")
## Warning: If this didn't work, its may be because you didn't set your working
## directory.

Data analysis

TODO: Scale the data

TODO: To do PCA, we scale our data using scale(). It allows us to compare data that is not measured the same way.

SNPs_scaled <- scale(SNPs_cleaned)

TODO: Run PCA

TODO: Use prcomp() to run PCA. The results will help us visualize variation in our dataset.

pca_scaled <- prcomp(SNPs_scaled)

TODO: Create a screeplot

TODO: Use screeplot() to make a plot that tells us which features are important.

TODO: UPDATE PLOT WITH TITLE(“my_pca”)

screeplot(pca_scaled, 
          ylab  = "Relative importance",
          main = "give me a basic title")

TODO: The screeplot tells us that PC1 is the most important. The rest are significantly less important.

TODO: View variation

TODO: Use summary() to view a summary of the importance of components. Use the function below to find the variation in percentage. Make sure to add the return function. Assign to a new object. Use barplot() to visualize the data in a graph.

summary_out_scaled <- summary(pca_scaled)
PCA_variation <- function(pca_summary, PCs = 2){
  var_explained <- pca_summary$importance[2,1:PCs]*100
  var_explained <- round(var_explained,1)
  return(var_explained)
}
var_out <- PCA_variation(summary_out_scaled,PCs = 10)
N_columns <- ncol(SNPs_scaled)
barplot(var_out,
        main = "Percent variation Scree plot",
        ylab = "Percent variation explained")
abline(h = 1/N_columns*100, col = 2, lwd = 2)

TODO: The variance explained show us the rotation of graphs and correlation.

TODO: Biplot of PCA data

TODO: Use biplot() to create a plot that shows the principal components and loading vectors in one plot.

biplot(pca_scaled)

TODO: Uing a biplot is a bad idea because there are many vectors and this causes the plot to be difficult to analyze.

TODO: Extract scores

TODO: Extract scores to plot using the vegan function scores(). This will scale and transform the data as well.

pca_scores <- vegan::scores(pca_scaled)

TODO: Put population IDs into a vector using c().

pop_id <- c("Nel","Nel","Nel","Nel","Nel","Nel","Nel","Nel",
"Nel", "Nel", "Nel", "Nel", "Nel", "Nel", "Nel", "Alt",
"Alt", "Alt", "Alt", "Alt", "Alt", "Alt", "Alt", "Alt",
"Alt", "Alt", "Alt", "Alt", "Alt", "Alt", "Sub", "Sub",
"Sub", "Sub", "Sub", "Sub", "Sub", "Sub", "Sub", "Sub",
"Sub", "Cau", "Cau", "Cau", "Cau", "Cau", "Cau", "Cau",
"Cau", "Cau", "Cau", "Cau", "Cau", "Div", "Div", "Div",
"Div", "Div", "Div", "Div", "Div", "Div", "Div", "Div",
"Div", "Div", "Div", "Div")

TODO: Put the PCA scores and population IDs into a data frame using data.frame(). Assign it to a new object. This helps us compare the two sets of data.

pca_scores2 <- data.frame(pop_id,
                              pca_scores)

TODO: Create a scatterplot graph of he dataframe

TODO: Use ggscatter() from the ggpubr package to create a scatterplot of PC1, PC2, and population ID data.

TODO: UPDATE X and Y AXES WITH AMOUNT OF VARIATION EXPLAINED (19.9, 2.3)

ggpubr::ggscatter(data = pca_scores2,
                  y = "PC2",
                  x = "PC1",
                  color = "pop_id",
                  shape = "pop_id",
                  xlab = "PC1 (19.9% variation)",
                  ylab = "PC2 (2.3% variation)",
                  main = "Percent Variation Scatterplot")

TODO: The amount of variation each PC is responsible for is shown on the axes. The plot shows us that categories Alt and Nel have a correlation. Categories Cau and Dic also have a correlation.