Final Workflow

library(vegan)

## Loading required package: permute

## Loading required package: lattice

## This is vegan 2.6-4

library(vcfR)

## 
##    *****       ***   vcfR   ***       *****
##    This is vcfR 1.13.0 
##      browseVignettes('vcfR') # Documentation
##      citation('vcfR') # Citation
##    *****       *****      *****       *****

library(ggplot2)
library(ggpubr)

Confirm working directory and location of files

getwd()

## [1] "C:/Users/Fatsn/OneDrive - University of Pittsburgh/CBioFinal"

list.files(pattern = "vcf")

## [1] "1.1000-241000.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"       
## [2] "16.22875070-23115070.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"
## [3] "all_loci-1.vcf"                                                
## [4] "all_loci.vcf"                                                  
## [5] "vcf_num.csv"                                                   
## [6] "vcf_num_df.csv"                                                
## [7] "vcf_num_df2.csv"

Set SNP data up for R

Load VCF data

vcf_file <- "1.1000-241000.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"

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

## Scanning file to determine attributes.
## File attributes:
##   meta lines: 130
##   header_line: 131
##   variant count: 1042
##   column count: 2513
## 
Meta line 130 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
##   Character matrix gt rows: 1042
##   Character matrix gt cols: 2513
##   skip: 0
##   nrows: 1042
##   row_num: 0
## 
Processed variant 1000
Processed variant: 1042
## All variants processed

Convert Raw VCF to genotype scores

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

Save as csv

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

## [1] "1000genomes_people_info2-1.csv" "cleaned_data.csv"              
## [3] "SNPs_cleaned.csv"               "vcf_num.csv"                   
## [5] "vcf_num_df.csv"                 "vcf_num_df2.csv"               
## [7] "walsh2017morphology.csv"

Transpose original VCF orientatiopn to R orientation

vcf_num_t <- t(vcf_num)

Turn into dataframe

vcf_num_df <- data.frame(vcf_num_t)

Get person names

sample <- row.names(vcf_num_df)

Add into dataframe

vcf_num_df <- data.frame(sample, vcf_num_df)

Save as csv

getwd()

## [1] "C:/Users/Fatsn/OneDrive - University of Pittsburgh/CBioFinal"

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

## [1] "1000genomes_people_info2-1.csv" "cleaned_data.csv"              
## [3] "SNPs_cleaned.csv"               "vcf_num.csv"                   
## [5] "vcf_num_df.csv"                 "vcf_num_df2.csv"               
## [7] "walsh2017morphology.csv"

Clean Data

Merge with population meta data

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

Check that sample is in both data sets

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"

Merge

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

Check dimensions

nrow(vcf_num_df) == nrow(vcf_num_df2)

## [1] TRUE

Check names

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"

Save as csv

write.csv(vcf_num_df2, file = "vcf_num_df2.csv", row.names = F)
list.files(pattern = "csv")

## [1] "1000genomes_people_info2-1.csv" "cleaned_data.csv"              
## [3] "SNPs_cleaned.csv"               "vcf_num.csv"                   
## [5] "vcf_num_df.csv"                 "vcf_num_df2.csv"               
## [7] "walsh2017morphology.csv"

Omit Invarient Features

Load Function

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)                      
}

Run function on numeric data

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

## Dataframe of dim 2504 1042 processed...
## 257 columns removed

dim(vcf_noinvar)

## [1] 2504 1048

Store number of invariant columns

my_meta_N_invar_cols <- 257

Remove 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 loop to search for NAs

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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Check for any rows over 50% NAs

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)

Mean Imputation

mean_imputation <- function(df){
  
  cat("This make take some time...")
  n_cols <- ncol(df)
  
  for(i in 1:n_cols){
  # get the current column
  column_i <- df[, 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))
  
  # report 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
  df[, i] <- column_i
  
  }
  
  return(df)
}

names(vcf_noinvar)[1:10]

##  [1] "sample"    "pop"       "super_pop" "sex"       "lat"       "lng"      
##  [7] "X1"        "X2"        "X3"        "X4"

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

## This make take some time...

##PCA Prep

vcf_scaled <- vcf_noNA

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

#For Final Report
write.csv(vcf_scaled, file = "cleaned_data.csv", row.names = F)

vcf_pca <- prcomp(vcf_scaled[, -c(1:6)])

screeplot(vcf_pca)

Explained Variation

PCA_variation <- function(pca_summary, PCs = 2){
  var_explained <- pca_summary$importance[2,1:PCs] * 100
  var_explained <- round(var_explained,3)
  return(var_explained)
}

vcf_pca_summary <- summary(vcf_pca)

Extract data

var_out <- PCA_variation(vcf_pca_summary, PCs = 500)

Calculate cutoff

N_columns <- ncol(vcf_scaled)

cut_off <- 1/N_columns*100

i_cut_off <- which(var_out < cut_off)

i_cut_off <- min(i_cut_off)

First value below cutoff

my_meta_N_mean_NA_rowsPCs <- i_cut_off

Extract variation by first 3 PCs

my_meta_var_PC123 <- var_out[c(1,2,3)]


barplot(var_out,
        main = "Percet variation(%) Scree plot",
        ylab = "Percent variation (%) explained",
        names.arg = 1:length(var_out))
abline(h = cut_off, col = 2, lwd = 2)
abline(v = i_cut_off)
legend("topright",
       col = c(2,1),
       lty = c(1,1),
       legend = c("Vertical linez; cutoff",
                  "Horizontal line: 1st value below cut off"))

Cumulative Percent Variation

cumulative_variation <- cumsum(var_out)
plot(cumulative_variation, type = "l")

Get scores and combine data

#Getscroes

vcf_pca_scores <- vegan::scores(vcf_pca)

#Combine the scores with the species information into a dataframe

vcf_pca_scores2 <- data.frame(super_pop = vcf_noNA$super_pop,
                              vcf_pca_scores)

my_meta_var_PC123[1]

##   PC1 
## 1.626

my_meta_var_PC123[2]

##   PC2 
## 1.248

my_meta_var_PC123[3]

##   PC3 
## 1.185

##Plot PCA Results

PC1 vs PC2

ggpubr::ggscatter(data = vcf_pca_scores2,
                  y = "PC2",
                  x = "PC1",
                  color = "super_pop",
                  shape = "super_pop",
                  main = "PCA Scatterplot",
                  xlab = "PC1 (1.626% Variation)",
                  ylab = "PC2 (1.248% Variation)")

PC2 vs PC3

ggpubr::ggscatter(data = vcf_pca_scores2,
                  y = "PC3",
                  x = "PC2",
                  color = "super_pop",
                  shape = "super_pop",
                  main = "PCA Scatterplot",
                  xlab = "PC2 (1.248% Variation)",
                  ylab = "PC3 (1.185% Variation)")

Pc1 vs PC3

ggpubr::ggscatter(data = vcf_pca_scores2,
                  y = "PC1",
                  x = "PC3",
                  color = "super_pop",
                  shape = "super_pop",
                  main = "PCA Scatterplot",
                  xlab = "PC1 1.626% Variation)",
                  ylab = "PC3 (1.185% Variation)")

Final Workflow

Abdullah Khan

2022-12-13

Set SNP data up for R

Clean Data

Omit Invarient Features