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 analyzes.

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/victorsyi/Desktop/COMPBIO"
list.files()
##  [1] "07-mean_imputation.docx"             
##  [2] "07-mean_imputation.html"             
##  [3] "07-mean_imputation.Rmd"              
##  [4] "08-PCA_worked.html"                  
##  [5] "08-PCA_worked.Rmd"                   
##  [6] "09-PCA_worked_example-SNPs-part1.Rmd"
##  [7] "10-PCA_worked_example-SNPs-part2.Rmd"
##  [8] "all_loci-1.vcf"                      
##  [9] "all_loci.vcf"                        
## [10] "bird_snps_remove_NAs.html"           
## [11] "bird_snps_remove_NAs.Rmd"            
## [12] "center_function.R"                   
## [13] "code_checkpoint_vcfR.html"           
## [14] "code_checkpoint_vcfR.Rmd"            
## [15] "COMPBIO.Rproj"                       
## [16] "feature_engineering.Rmd"             
## [17] "removing_fixed_alleles.html"         
## [18] "removing_fixed_alleles.Rmd"          
## [19] "rsconnect"                           
## [20] "SNPs_cleaned.csv"                    
## [21] "transpose_VCF_data.html"             
## [22] "transpose_VCF_data.Rmd"              
## [23] "vcfR_test.vcf"                       
## [24] "vcfR_test.vcf.gz"                    
## [25] "vegan_pca_with_msleep-STUDENT.html"  
## [26] "vegan_pca_with_msleep-STUDENT.Rmd"   
## [27] "walsh2017morphology.csv"             
## [28] "walsh2017morphology.RData"           
## [29] "working_directory_practice.html"     
## [30] "working_directory_practice.Rmd"
list.files(pattern = "vcf")
## [1] "all_loci-1.vcf"            "all_loci.vcf"             
## [3] "code_checkpoint_vcfR.html" "code_checkpoint_vcfR.Rmd" 
## [5] "vcfR_test.vcf"             "vcfR_test.vcf.gz"

Data preparation

TODO: Load .vcf file

TODO: Load the file and assure there is a number next to “Processed variant:”. Make sure our file outputs correctly. Assign the output to an object called “snps”.

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

TODO: Extract genotypes

TODO: Extract genotypes using “extract.gt()”. This allows us to see original genotypes and see the diploid and allele information about the genotypes.

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

TODO: Transpose the data

TODO: Use the “t()” function to re-orient our data to where SNPs are in columns and samples are in rows. Save the transposed data to the object called “snps_num_t”.

snps_num_t <- t(snps_num)

TODO: Turn this data into a dataframe using “data.frame()” to view the data differently. Assign the data frame to an object called “snps_num_df”.

snps_num_df <- data.frame(snps_num_t)

TODO: Find NAs

TODO: Use function() to find NAs in single column or vector as well as find index values.Assign to an object called find_NAs.

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)
}

TODO: Write a “for()” loop. Collect the number of rows using “nrow()”. Make a vector to store how many NAs are in each row using “rep()”. Find the total number of columns using “ncol()”. Use the for loop.

# 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
## .

TODO: Remove any rows that had >50% NAs. Use abline() to display the cutoff on the histogram.

# 50% of N_SNPs
cutoff50 <- N_SNPs*0.5

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

TODO: Convert the number of NAs to a percent. Use which() to determine the index value of each row with >50% NAs. Remove these rows from the data. Assign the new data without the NAs an object called “snps_num_t02”.

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, ]

TODO: Determine samplong locations

TODO: Using regular expressions function, we access certain information. Use row.names() function to find row names. Use gsub() function to remove unecessary information like “sample…” . Use gsub() to get rid of As, Cs, Ts, and Gs. This gives us a unique set of codes and numbers. Use table() to summarize the output.

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

TODO: Remove invariant columns

TODO: Use function() to remove invariant columns from the dataframe. Make sure to add return(x). Run invar_omit() on the dataframe to test on the data. Assign this new data to an object called “snps_no_invar”.

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

TODO: Use a for-loop to remove samples with many NAs

TODO: Duplicate the data by assigning “snps_no_invar” to new object labeled “snps_noNAs”. Use mean() to get the mean, which() to get the NAs, and length() to record the number of NAs. Replace the NAs with the mean and the old column with the new data. Create a new object for it.

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.