Introduction

In this worked example you will replicate a PCA on a published dataset.

The example is split into 2 Parts:

In this Data Preparation phase, you will do the following things:

  1. Load the SNP genotypes in .vcf format (vcfR::read.vcfR())
  2. Extract the genotypes into an R-compatible format (vcfR::extract.gt())
  3. Rotate the data into the standard R analysis format (t())
  4. Remove individuals (rows) from the data set that have >50% NAs (using a function I wrote)
  5. Remove SNPs (columns) that are fixed
  6. Impute remaining NAs (using a for() loop)
  7. Save the prepared data as a .csv file for the next step (write.csv())

Biological background

This worked example is based on a paper in the journal Molecular Ecology from 2017 by Jennifer Walsh titled Subspecies delineation amid phenotypic, geographic and genetic discordance in a songbird.

The study investigated variation between two bird species in the genus Ammodramus: A. nenlsoni and A. caudacutus.

The species A. nenlsoni has been divided into 3 sub-species: A. n. nenlsoni, A.n. alterus, and A n. subvirgatus. The other species, A. caudacutus, has been divided into two subspecies, A.c. caudacutus and A.c. diversus.

The purpose of this study was to investigate to what extent these five subspecies recognized by taxonomists are supported by genetic data. The author’s collected DNA from 75 birds (15 per subspecies) and genotyped 1929 SNPs. They then analyzed the data with Principal Components Analysis (PCA), among other genetic analyses.

This tutorial will work through all of the steps necessary to re-analyze Walsh et al.s data

Tasks

In the code below all code is provided. Your tasks will be to do 2 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”

Preliminaries

Load the vcfR and other packages with library().

library(vcfR)    
## 
##    *****       ***   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)

Make sure that your working directory is set to the location of the file all_loci.vcf.

getwd()
## [1] "/Users/madelynhoffman/BIOSC1540-independent-project/software-checkpoints-portfolios"
list.files()
##  [1] "07-mean_imputation.Rmd"              
##  [2] "08-PCA_worked.Rmd"                   
##  [3] "09-PCA_worked_example-SNPs-part1.Rmd"
##  [4] "all_loci-1.vcf"                      
##  [5] "all_loci.vcf"                        
##  [6] "bird_snps_remove_NAs.Rmd"            
##  [7] "code_checkpoint_vcfR.Rmd"            
##  [8] "removing_fixed_alleles.Rmd"          
##  [9] "SNPs_cleaned.csv"                    
## [10] "transpose_VCF_data.Rmd"              
## [11] "vcfR_test.vcf"                       
## [12] "vcfR_test.vcf.gz"                    
## [13] "walsh2017morphology.csv"             
## [14] "working_directory_practice.Rmd"
list.files(pattern = "vcf")
## [1] "all_loci-1.vcf"           "all_loci.vcf"            
## [3] "code_checkpoint_vcfR.Rmd" "vcfR_test.vcf"           
## [5] "vcfR_test.vcf.gz"

Data preparation

Load SNP Genotypes from VCF File

We are taking the data contained in “all_loci.vcf” and loading it into R. This is done with the vcfR package’s function read.vcfR.

snps <- vcfR::read.vcfR("all_loci.vcf", convertNA  = TRUE)
## Scanning file to determine attributes.
## File attributes:
##   meta lines: 8
##   header_line: 9
##   variant count: 1929
##   column count: 81
## 
Meta line 8 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
##   Character matrix gt rows: 1929
##   Character matrix gt cols: 81
##   skip: 0
##   nrows: 1929
##   row_num: 0
## 
Processed variant 1000
Processed variant: 1929
## All variants processed

Extract Genotypes into R-compatible Format

Creating a matrix of SNP data that contains numeric scores - important to have numeric rather than character data for further analysis. Birds homozygous for reference allele get a 0, homozygous for minor allele get 2, and heterozygous get 1 for each SNP.

snps_num <- vcfR::extract.gt(snps, 
           element = "GT",
           IDtoRowNames  = F,
           as.numeric = T,
           convertNA = T,
           return.alleles = F)

Transposing SNP Data

VCF files are organized with SNPs in rows and samples (birds samples) in columns. We must flip the orientation of the matrix so that the SNPs (features) are in columns and samples are in rows since this format is required for many analysis tools.

snps_num_t <- t(snps_num)

We are creating a data frame from the matrix of SNP data.

snps_num_df <- data.frame(snps_num_t)

Removing Individuals with >50% NAs

We are writing a function that takes a vector counts up the number of NAs it contains. The indices of the vector’s NAs are returned.

find_NAs <- function(x){
  NAs_TF <- is.na(x)
  i_NA <- which(NAs_TF == TRUE)
  N_NA <- length(i_NA)
  
  cat("Results:",N_NA, "NAs present\n.")
  return(i_NA)
}

We are using a for loop to search through each row (birds in study) for the number and location of NAs using the function find_NAs created above. This information is stored in a vector for later use in determining whether a sample should be removed.

# N_rows
# number of rows (individuals)
N_rows <- nrow(snps_num_t)

# N_NA
# vector to hold output (number of NAs)
N_NA   <- rep(x = 0, times = N_rows)

# N_SNPs
# total number of columns (SNPs)
N_SNPs <- ncol(snps_num_t)

# the for() loop
for(i in 1:N_rows){
  
  # for each row, find the location of
  ## NAs with snps_num_t()
  i_NA <- find_NAs(snps_num_t[i,]) 
  
  # then determine how many NAs
  ## with length()
  N_NA_i <- length(i_NA)
  
  # then save the output to 
  ## our storage vector
  N_NA[i] <- N_NA_i
}
## Results: 28 NAs present
## .Results: 20 NAs present
## .Results: 28 NAs present
## .Results: 24 NAs present
## .Results: 23 NAs present
## .Results: 63 NAs present
## .Results: 51 NAs present
## .Results: 38 NAs present
## .Results: 34 NAs present
## .Results: 24 NAs present
## .Results: 48 NAs present
## .Results: 21 NAs present
## .Results: 42 NAs present
## .Results: 78 NAs present
## .Results: 45 NAs present
## .Results: 21 NAs present
## .Results: 42 NAs present
## .Results: 34 NAs present
## .Results: 66 NAs present
## .Results: 54 NAs present
## .Results: 59 NAs present
## .Results: 52 NAs present
## .Results: 47 NAs present
## .Results: 31 NAs present
## .Results: 63 NAs present
## .Results: 40 NAs present
## .Results: 40 NAs present
## .Results: 22 NAs present
## .Results: 60 NAs present
## .Results: 48 NAs present
## .Results: 961 NAs present
## .Results: 478 NAs present
## .Results: 59 NAs present
## .Results: 26 NAs present
## .Results: 285 NAs present
## .Results: 409 NAs present
## .Results: 1140 NAs present
## .Results: 600 NAs present
## .Results: 1905 NAs present
## .Results: 25 NAs present
## .Results: 1247 NAs present
## .Results: 23 NAs present
## .Results: 750 NAs present
## .Results: 179 NAs present
## .Results: 433 NAs present
## .Results: 123 NAs present
## .Results: 65 NAs present
## .Results: 49 NAs present
## .Results: 192 NAs present
## .Results: 433 NAs present
## .Results: 66 NAs present
## .Results: 597 NAs present
## .Results: 1891 NAs present
## .Results: 207 NAs present
## .Results: 41 NAs present
## .Results: 268 NAs present
## .Results: 43 NAs present
## .Results: 110 NAs present
## .Results: 130 NAs present
## .Results: 90 NAs present
## .Results: 271 NAs present
## .Results: 92 NAs present
## .Results: 103 NAs present
## .Results: 175 NAs present
## .Results: 31 NAs present
## .Results: 66 NAs present
## .Results: 64 NAs present
## .Results: 400 NAs present
## .Results: 192 NAs present
## .Results: 251 NAs present
## .Results: 69 NAs present
## .Results: 58 NAs present
## .

We are creating a histogram of the frequency of NAs in every row (bird). The cutoff is >50% which is equal to the number of SNPs(.5) and is plotted on the histogram.

# 50% of N_SNPs
cutoff50 <- N_SNPs*0.5

hist(N_NA)            
abline(v = cutoff50, 
       col = 2, 
       lwd = 2, 
       lty = 2)

We are converting the number of NAs in each row into a percentage. Any that are >50% are removed from the data frame.

percent_NA <- N_NA/N_SNPs*100

# Call which() on percent_NA
i_NA_50percent <- which(percent_NA > 50) 

snps_num_t02 <- snps_num_t[-i_NA_50percent, ]

Cleaning Row Names & Tabulating Species

The row names are shortened so that they only include the birds’ species. Species are tabulated so we can see how many of each are remaining after removing ones with too many NAs.

row_names <- row.names(snps_num_t02) # Key

row_names02 <- gsub("sample_","",row_names)

sample_id <- gsub("^([ATCG]*)(_)(.*)",
                  "\\3",
                  row_names02)
pop_id <- gsub("[01-9]*",    
               "",
               sample_id)

table(pop_id)  
## pop_id
## Alt Cau Div Nel Sub 
##  15  12  15  15  11

Removing Invariant Columns

We create a function that looks at each column finding SNPs that are fixed for the particular sample of birds sampled in the study. Any column with a fixed SNP will have a standard deviation of 0 & cannot be scaled.

invar_omit <- function(x){
  cat("Dataframe of dim",dim(x), "processed...\n")
  sds <- apply(x, 2, sd, na.rm = TRUE)
  i_var0 <- which(sds == 0)
 
  
  cat(length(i_var0),"columns removed\n")
  
  if(length(i_var0) > 0){
     x <- x[, -i_var0]
  }
  
  ## add return()  with x in it
  return(x)                      
}


snps_no_invar <- invar_omit(snps_num_t02) 
## Dataframe of dim 68 1929 processed...
## 591 columns removed

Impute Remaining NAs

Use mean imputation to take care of any remaining NAs in the data. This imputation is done by simply calculating the mean for each SNP and replacing the NAs in the SNP column with the mean value.

snps_noNAs <- snps_no_invar

N_col <- ncol(snps_no_invar)
for(i in 1:N_col){
  
  # get the current column
  column_i <- snps_noNAs[, i]
  
  # get the mean of the current column
  mean_i <- mean(column_i, na.rm = TRUE)
  
  # get the NAs in the current column
  NAs_i <- which(is.na(column_i))
  
  # record the number of NAs
  N_NAs <- length(NAs_i)

  # replace the NAs in the current column
  column_i[NAs_i] <- mean_i
  
  # replace the original column with the
  ## updated columns
  snps_noNAs[, i] <- column_i
  
}

Save the data

Save the data as a .csv file which can be loaded again later.

write.csv(snps_noNAs, file = "SNPs_cleaned.csv",
          row.names = F)

Check for the presence of the file with list.files()

list.files(pattern = ".csv")
## [1] "SNPs_cleaned.csv"        "walsh2017morphology.csv"

Next steps:

In Part 2, we will re-load the SNPs_cleaned.csv file and carry an an analysis with PCA.