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
## Warning: package 'vcfR' was built under R version 4.2.2
## 
##    *****       ***   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 2.6-2
library(ggplot2)
library(ggpubr)

Set the working directory

setwd("C:/Users/saman/Desktop/Comp Bio")

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: Scale the data using scale(). When scaling the data this means the SD becomes 1 and the mean become 0.

SNPs_scaled <- scale(SNPs_cleaned)

TODO: Run PCA

TODO: Use prcomp() to run PCA on the scaled data. PCA is principal component analysis to reduce the dimensions of the data.

pca_scaled <- prcomp(SNPs_scaled)

TODO: Create a scree plot

TODO: Use screeplot() to visualize the data to see the variance in the different dimensions

TODO: UPDATE PLOT WITH TITLE

screeplot(pca_scaled, 
          ylab  = "variance",
          main = "Scree plot of PCA SNPs")

TODO: INTERPRET SCREEPLOT - There is little variance within PC2-PC10 but a lot of variance within PC1

TODO: Summarize data

TODO: Use summary() to get data summary and save in object called “summary_out_scaled”

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: INTERPRET VARIANCE EXPLAINED - again there is little variance between PC2-PC10 but a lot of variance within PC1 with a percent variance of over 15%.

TODO: Create a biplot

TODO: Use biplot() to create a biplot of the data

biplot(pca_scaled)

TODO: EXPLAIN WHY THIS IS A BAD IDEA - this is a bad idea because there is so much data that the viewer is unable to see the relationship between the data

TODO: Score PCA data

TODO: Use the vegan package scores() to score the data

pca_scores <- vegan::scores(pca_scaled)

TODO: Create a vector of the population IDs

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: Create a dataframe with data.frame() of the population IDs with the PCA scores

pca_scores2 <- data.frame(pop_id,
                              pca_scores)

TODO: Create a scatter plot

TODO: Use ggscatter to create a scatter plot of the data to view relations between PC1, PC2, and population IDs

TODO: UPDATE PLOT WITH TITLE TODO: UPDATE X and Y AXES WITH AMOUNT OF VARIATION EXPLAINED

ggpubr::ggscatter(data = pca_scores2,
                  y = "PC2",
                  x = "PC1",
                  color = "pop_id",
                  shape = "pop_id",
                  xlab = "Amount of varitation explained",
                  ylab = "Amount of varitaion explained",
                  main = "PC1 Vs PC2 based on population ID")

TODO: INTERPRET PLOT - There is one clear cluster which shows that there is a similarity in the amount of variance in PC1 and PC2 within the Cau and Div populations. There is a potential cluster between Alt and Nel while Sub appears to be in its own cluster.