Introduction

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. Add meta data
  8. Save the prepared data as a .csv file for the next step (write.csv())

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/adampowley/Desktop/School Stuff/Comp Bio/Final Project"
list.files()
## [1] "1000genomes_people_info2-1.csv"                              
## [2] "16.34203406-34443406.ALL.chr16_GRCh38.genotypes.20170504.vcf"
## [3] "Final_Report.docx"                                           
## [4] "Final_Report.html"                                           
## [5] "Final_Report.Rmd"                                            
## [6] "Full_workflow.html"                                          
## [7] "Full_workflow.Rmd"                                           
## [8] "rsconnect"                                                   
## [9] "SNPs_cleaned.csv"
list.files(pattern = "vcf")
## [1] "16.34203406-34443406.ALL.chr16_GRCh38.genotypes.20170504.vcf"

Data preparation

Loading in data from vcf

To get the data from a stored file into our working code enviroment we use vcFR.

snps <- vcfR::read.vcfR("16.34203406-34443406.ALL.chr16_GRCh38.genotypes.20170504.vcf", convertNA  = T)
## Scanning file to determine attributes.
## File attributes:
##   meta lines: 130
##   header_line: 131
##   variant count: 3
##   column count: 2513
## 
Meta line 130 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
##   Character matrix gt rows: 3
##   Character matrix gt cols: 2513
##   skip: 0
##   nrows: 3
##   row_num: 0
## 
Processed variant: 3
## All variants processed

Extract numeric genotype scores

We use vcfR::extract.gt to convert the biological conventional representations of SNPs into numeric scores which can be processed.

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

Transpose data

As is, the SNPs are columns while the samples are rows. We switch these by transposing the data.

snps_num_t <- t(snps_num)

We store the data in a dataframe to make it easier to manipulate and see the data.

snps_num_df <- data.frame(snps_num_t)

Removing invariate data

Here we create a function to omit data that doesn’t vary enough to be significant to our analysis.

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_df) 
## Dataframe of dim 2504 3 processed...
## 1 columns removed

Find NAs

We need a way to locate the index values of the NA values to deal with them before more complicated analysis.

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)
}

Here we use the function on every row to locate the NA indeces and count their abundance, saving it to a vector.

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

# 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_no_invar)

# 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_no_invar[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
}
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Here we set up a cut off value to visualize the halfway point of SNPS

# 50% of N_SNPs
cutoff50 <- N_SNPs*0.5

Mean value of NAs

percent_NA <- N_NA/N_SNPs*100

# Call which() on percent_NA
which(percent_NA > 50) 
## integer(0)
mean(percent_NA)
## [1] 0
my_meta_N_meanNA_rows <- mean(percent_NA)

Replace missing data

Here we replace the NA’s in each column with the mean for that column.

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
  
}

Scale

We need a way to locate the index values of the NA values to deal with them before more complicated analysis.

snps_scaled <- scale(snps_noNAs)

Add Meta data and name columns

We need a way to locate the index values of the NA values to deal with them before more complicated analysis.

pop_meta <- read.csv(file = "1000genomes_people_info2-1.csv")

sample <- row.names(snps_scaled)
sample_df <- data.frame(sample,snps_scaled)

meta_df <- merge(pop_meta, sample_df, by = "sample")

Save the data

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

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

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

list.files(pattern = ".csv")
## [1] "1000genomes_people_info2-1.csv" "SNPs_cleaned.csv"

Next steps:

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