Data Preparation: Analysis of 1000 Genomes Data

Load necessary packages.

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)

Converting raw VCF file to genotype scores (chr –> num).

getwd()

## [1] "/Users/madelynhoffman/BIOSC1540-independent-project/my-snps"

list.files(pattern = "vcf")

## [1] "4.13111147-13351147.ALL.chr4_GRCh38.genotypes.20170504.vcf.gz"
## [2] "Proof-of-loaded-vcf-file.png"                                 
## [3] "vcf_num_df.csv"                                               
## [4] "vcf_num_df2.csv"                                              
## [5] "vcf_num.csv"

my_vcf = "4.13111147-13351147.ALL.chr4_GRCh38.genotypes.20170504.vcf.gz"

vcf <- vcfR::read.vcfR(my_vcf, convertNA = T)

## Scanning file to determine attributes.
## File attributes:
##   meta lines: 130
##   header_line: 131
##   variant count: 7573
##   column count: 2513
## 
Meta line 130 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
##   Character matrix gt rows: 7573
##   Character matrix gt cols: 2513
##   skip: 0
##   nrows: 7573
##   row_num: 0
## 
Processed variant 1000
Processed variant 2000
Processed variant 3000
Processed variant 4000
Processed variant 5000
Processed variant 6000
Processed variant 7000
Processed variant: 7573
## All variants processed

vcf_num <- vcfR::extract.gt(vcf,
                            element = "GT",
                            IDtoRowNames = FALSE,
                            as.numeric = TRUE,
                            convertNA = TRUE)

write.csv(vcf_num, file = "vcf_num.csv", row.names = FALSE)
list.files()

##  [1] "1000genomes_people_info.csv"                                  
##  [2] "4.13111147-13351147.ALL.chr4_GRCh38.genotypes.20170504.vcf.gz"
##  [3] "mgh31_hoffman_data_preparation.Rmd"                           
##  [4] "mgh31_hoffman_final-report.Rmd"                               
##  [5] "my-snps.Rproj"                                                
##  [6] "prepared-data.csv"                                            
##  [7] "Proof-of-loaded-vcf-file.png"                                 
##  [8] "vcf_num_df.csv"                                               
##  [9] "vcf_num_df2.csv"                                              
## [10] "vcf_num.csv"

Transposing the dataframe. Add person (sample) names as a column in the data frame.

vcf_num_t <- t(vcf_num)

vcf_num_df <- data.frame(vcf_num_t)

sample <- row.names(vcf_num_df)
vcf_num_df <- data.frame(sample, vcf_num_df)
getwd()

## [1] "/Users/madelynhoffman/BIOSC1540-independent-project/my-snps"

write.csv(vcf_num_df, file = "vcf_num_df.csv", row.names = FALSE)
list.files()

##  [1] "1000genomes_people_info.csv"                                  
##  [2] "4.13111147-13351147.ALL.chr4_GRCh38.genotypes.20170504.vcf.gz"
##  [3] "mgh31_hoffman_data_preparation.Rmd"                           
##  [4] "mgh31_hoffman_final-report.Rmd"                               
##  [5] "my-snps.Rproj"                                                
##  [6] "prepared-data.csv"                                            
##  [7] "Proof-of-loaded-vcf-file.png"                                 
##  [8] "vcf_num_df.csv"                                               
##  [9] "vcf_num_df2.csv"                                              
## [10] "vcf_num.csv"

Merge data with population meta data.

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

names(pop_meta)

## [1] "pop"       "super_pop" "sample"    "sex"       "lat"       "lng"

names(vcf_num_df)[1:10]

##  [1] "sample" "X1"     "X2"     "X3"     "X4"     "X5"     "X6"     "X7"    
##  [9] "X8"     "X9"

vcf_num_df2 <- merge(pop_meta, vcf_num_df, by = "sample")
nrow(vcf_num_df) == nrow(vcf_num_df2)

## [1] TRUE

names(vcf_num_df2)[1:15]

##  [1] "sample"    "pop"       "super_pop" "sex"       "lat"       "lng"      
##  [7] "X1"        "X2"        "X3"        "X4"        "X5"        "X6"       
## [13] "X7"        "X8"        "X9"

getwd()

## [1] "/Users/madelynhoffman/BIOSC1540-independent-project/my-snps"

write.csv(vcf_num_df2, file = "vcf_num_df2.csv", row.names = FALSE)

Omit invariant features.

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]
  }
  return(x)
}

names(vcf_num_df2)[1:15]

##  [1] "sample"    "pop"       "super_pop" "sex"       "lat"       "lng"      
##  [7] "X1"        "X2"        "X3"        "X4"        "X5"        "X6"       
## [13] "X7"        "X8"        "X9"

vcf_noinvar <- vcf_num_df2
vcf_noinvar[, -c(1:6)] <- invar_omit(vcf_noinvar[, -c(1:6)])

## Dataframe of dim 2504 7573 processed...
## 1912 columns removed

vcf_noinvar <- vcf_noinvar[, 1:5667]

dim(vcf_num_df2) == dim(vcf_noinvar)

## [1]  TRUE FALSE

my_meta_N_invar_cols <- 1912

Removing low-quality data.

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)
}

N_rows <- nrow(vcf_noinvar)
N_NA   <- rep(x = 0, times = N_rows)
N_SNPs <- ncol(vcf_noinvar)

for(i in 1:N_rows){
  i_NA <- find_NAs(vcf_noinvar[i,]) 
  N_NA_i <- length(i_NA)
  N_NA[i] <- N_NA_i
}

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## .

cutoff50 <- N_SNPs*0.5
percent_NA <- N_NA/N_SNPs*100
any(percent_NA > 50)

## [1] FALSE

mean(percent_NA)

## [1] 0

my_meta_N_meanNA_rows <- mean(percent_NA)

Imputation

mean_imputation <- function(df){
  n_cols <- ncol(df)
  
  for(i in 1:n_cols){
  column_i <- df[, i]
  
  mean_i <- mean(column_i, na.rm = TRUE)
  
  NAs_i <- which(is.na(column_i))
  
  N_NAs <- length(NAs_i)
  
  column_i[NAs_i] <- mean_i
  
  df[, i] <- column_i
  }
  return(df)
}

vcf_noNA <- vcf_noinvar
vcf_noNA[, -c(1:6)] <- mean_imputation(vcf_noNA[, -c(1:6)])

Scaling the data to prepare for principal component analysis (PCA).

vcf_scaled <- vcf_noNA
vcf_scaled[, -c(1:6)] <- scale(vcf_scaled[, -c(1:6)])

Saving prepared data.

write.csv(vcf_scaled, file = "prepared-data.csv", row.names = FALSE, )

Data Preparation: Analysis of 1000 Genomes Data

Madelyn Hoffman

2022-12-16