Final Project Workflow
Nithin
2022-12-15
Install and Load Packages
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 2.6-4library(ggplot2)
library(ggpubr)Make sure that your working directory is set to the location of the desired file
getwd()## [1] "/Users/nithinkodali/Desktop/compb"list.files()## [1] "1000genomes_people_info2-1.csv"
## [2] "1540_final_project_Final_Report_template.pdf"
## [3] "18.18000-258000.ALL.chr18_GRCh38.genotypes.20170504.vcf"
## [4] "ALL.chr22_GRCh38.genotypes.20170504.vcf"
## [5] "Dimension Reduction.docx"
## [6] "Final_Project_Workflow.Rmd"
## [7] "final_report_template.Rmd"
## [8] "for_pca.csv"
## [9] "gwas_pheno_env.csv"
## [10] "My_SNPs.vcf"
## [11] "pheno.csv"
## [12] "rsconnect"
## [13] "SNP_12.vcf.gz"
## [14] "SNPs_cleaned.csv"
## [15] "vcf_num_df_merged.csv"
## [16] "vcf_num_df.csv"
## [17] "vcf_num.csv"
## [18] "vcf_scaled_cleaned.csv"
## [19] "walsh2017morphology.csv"
## [20] "working_directory_practice.html"
## [21] "working_directory_practice.Rmd"list.files(pattern = "vcf")## [1] "18.18000-258000.ALL.chr18_GRCh38.genotypes.20170504.vcf"
## [2] "ALL.chr22_GRCh38.genotypes.20170504.vcf"
## [3] "My_SNPs.vcf"
## [4] "SNP_12.vcf.gz"
## [5] "vcf_num_df_merged.csv"
## [6] "vcf_num_df.csv"
## [7] "vcf_num.csv"
## [8] "vcf_scaled_cleaned.csv"Data preparation
Read Vcf file
Load the data from the Vcf file
vcf <- vcfR::read.vcfR("SNP_12.vcf.gz", convertNA = TRUE)## Scanning file to determine attributes.
## File attributes:
## meta lines: 130
## header_line: 131
## variant count: 8084
## column count: 2513
##
Meta line 130 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 8084
## Character matrix gt cols: 2513
## skip: 0
## nrows: 8084
## 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 8000
Processed variant: 8084
## All variants processedGet Genotype Scores
Use vcfR::extract.gt() to get the genotype scores
vcf_num <- vcfR::extract.gt(vcf,
element = "GT",
IDtoRowNames = F,
as.numeric = T,
convertNA = T,
return.alleles = F)Save the csv file
getwd()## [1] "/Users/nithinkodali/Desktop/compb"# write.csv(vcf_num, file = "vcf_num.csv", row.names = FALSE)
list.files()## [1] "1000genomes_people_info2-1.csv"
## [2] "1540_final_project_Final_Report_template.pdf"
## [3] "18.18000-258000.ALL.chr18_GRCh38.genotypes.20170504.vcf"
## [4] "ALL.chr22_GRCh38.genotypes.20170504.vcf"
## [5] "Dimension Reduction.docx"
## [6] "Final_Project_Workflow.Rmd"
## [7] "final_report_template.Rmd"
## [8] "for_pca.csv"
## [9] "gwas_pheno_env.csv"
## [10] "My_SNPs.vcf"
## [11] "pheno.csv"
## [12] "rsconnect"
## [13] "SNP_12.vcf.gz"
## [14] "SNPs_cleaned.csv"
## [15] "vcf_num_df_merged.csv"
## [16] "vcf_num_df.csv"
## [17] "vcf_num.csv"
## [18] "vcf_scaled_cleaned.csv"
## [19] "walsh2017morphology.csv"
## [20] "working_directory_practice.html"
## [21] "working_directory_practice.Rmd"Transpose Data
Transpose the data with t() so that it has the proper orientation. Convert the matrix to a dataframe Get sample names and add it to the dataframe
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)Save the csv
getwd()## [1] "/Users/nithinkodali/Desktop/compb"# write.csv(vcf_num_df, file = "vcf_num_df.csv", row.names = FALSE)
list.files()## [1] "1000genomes_people_info2-1.csv"
## [2] "1540_final_project_Final_Report_template.pdf"
## [3] "18.18000-258000.ALL.chr18_GRCh38.genotypes.20170504.vcf"
## [4] "ALL.chr22_GRCh38.genotypes.20170504.vcf"
## [5] "Dimension Reduction.docx"
## [6] "Final_Project_Workflow.Rmd"
## [7] "final_report_template.Rmd"
## [8] "for_pca.csv"
## [9] "gwas_pheno_env.csv"
## [10] "My_SNPs.vcf"
## [11] "pheno.csv"
## [12] "rsconnect"
## [13] "SNP_12.vcf.gz"
## [14] "SNPs_cleaned.csv"
## [15] "vcf_num_df_merged.csv"
## [16] "vcf_num_df.csv"
## [17] "vcf_num.csv"
## [18] "vcf_scaled_cleaned.csv"
## [19] "walsh2017morphology.csv"
## [20] "working_directory_practice.html"
## [21] "working_directory_practice.Rmd"Clean data
Merge data with population meta data
Load the 1000 Genomes Meta Data
pop_meta <- read.csv(file = "1000genomes_people_info2-1.csv")
dim(vcf_num_df)## [1] 2504 8085Merge the data sets and make sure sample appears in both meta data and SNP data
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_df_merged <- merge(pop_meta, vcf_num_df, by = "sample")
nrow(vcf_num_df) == nrow(vcf_num_df_merged)## [1] TRUESave the csv
getwd()## [1] "/Users/nithinkodali/Desktop/compb"# write.csv(vcf_num_df_merged, file = "vcf_num_df_merged.csv", row.names = FALSE)
list.files()## [1] "1000genomes_people_info2-1.csv"
## [2] "1540_final_project_Final_Report_template.pdf"
## [3] "18.18000-258000.ALL.chr18_GRCh38.genotypes.20170504.vcf"
## [4] "ALL.chr22_GRCh38.genotypes.20170504.vcf"
## [5] "Dimension Reduction.docx"
## [6] "Final_Project_Workflow.Rmd"
## [7] "final_report_template.Rmd"
## [8] "for_pca.csv"
## [9] "gwas_pheno_env.csv"
## [10] "My_SNPs.vcf"
## [11] "pheno.csv"
## [12] "rsconnect"
## [13] "SNP_12.vcf.gz"
## [14] "SNPs_cleaned.csv"
## [15] "vcf_num_df_merged.csv"
## [16] "vcf_num_df.csv"
## [17] "vcf_num.csv"
## [18] "vcf_scaled_cleaned.csv"
## [19] "walsh2017morphology.csv"
## [20] "working_directory_practice.html"
## [21] "working_directory_practice.Rmd"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]
}
## add return() with x in it
return(x)
}Check columns with charecter data and run invar omit() on numeric data Store number of invariant columns
names(vcf_num_df_merged)[1:10]## [1] "sample" "pop" "super_pop" "sex" "lat" "lng"
## [7] "X1" "X2" "X3" "X4"vcf_noinvar <- vcf_num_df_merged
vcf_noinvar[,-c(1:6)] <- invar_omit(vcf_noinvar[,-c(1:6)])## Dataframe of dim 2504 8084 processed...
## 2007 columns removedmeta_data_N_invar_cols <- 2007Remove low-quality data
load 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)
}Search for NAs
# N_rows
# number of rows (individuals)
N_rows <- nrow(vcf_noinvar)
# 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(vcf_noinvar)
# 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(vcf_noinvar[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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## .Check if any row has >50% NAs
cutoff50 <- N_SNPs*0.5
percent_NA <- N_NA/N_SNPs*100
any(percent_NA > 50) ## [1] FALSEmean(percent_NA)## [1] 0.001041597mmeta_N_meanNA_rows <- mean(percent_NA) # store mean number of NAs in each sampleImputation of NAs
Load imputation function from Dr.Brouwer
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)
}Run this function on numeric columns
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)])Prepare for PCA
Scale the data
Use scale() to scale the data
vcf_scaled <- vcf_noNA
vcf_scaled[,-c(1:6)] <- scale(vcf_noNA[,-c(1:6)])
write.csv(vcf_scaled, file = "vcf_scaled_cleaned.csv", row.names = FALSE)Run the PCA
vcf_pca <- prcomp(vcf_scaled[,-c(1:6)])PCA diagnostics
Examine the default screeplot
screeplot(vcf_pca)
Calculate the variation
Load PCA variation function
PCA_variation <- function(pca_summary, PCs = 2){
var_explained <- pca_summary$importance[2,1:PCs]*100
var_explained <- round(var_explained,3)
return(var_explained)
}Extract the PCA variation and calculate teh percent variation
vcf_pca_summary <- summary(vcf_pca)Extract the raw variation data
variation_out <- PCA_variation(vcf_pca_summary, PCs = 1700)Calculate the cutoff rule
N_columns <- ncol(vcf_scaled)
cut_off <- 1 / N_columns * 100
i_cut_off <- which(variation_out < cut_off)
i_cut_off <- min(i_cut_off)
meta_N_meanNA_rowsPCs <- i_cut_offmeta_var_PC123 <- variation_out[c(1,2,3)]Make the barplot with the cutoffs
barplot(variation_out,
main = "Percent variation (%) Scree Plot",
xlab = "PCs",
ylab = "Percent variation (%) explained",
names.arg = 1:length(variation_out))
abline(h = cut_off, col = 2, lwd = 2)
abline(v = i_cut_off)
legend("topright",
col = c(2,1),
lty = c(1,1),
legend = c("Vertical line: cutoff",
"Horizontal line: 1st value below cut off"))
Plot the cummulative percentage variation
cumulative_variation <- cumsum(variation_out)
plot(cumulative_variation,
type = "l",
main = "Cumulative variation (%) Plot",
ylab = "Cumulative variation (%) explained",
xlab = "PCs")
Plot PCA results
Calculate scores
Get scores Combine scores and elvaluate the variation
vcf_pca_scores <- vegan::scores(vcf_pca)
vcf_pca_scores2 <- data.frame(super_pop = vcf_noNA$super_pop, vcf_pca_scores)
meta_var_PC123[1]## PC1
## 1.431meta_var_PC123[2]## PC2
## 1.244meta_var_PC123[3]## PC3
## 1.118Plot the results
Plot PC1 vs. PC2
ggpubr::ggscatter(data = vcf_pca_scores2,
y = "PC2",
x = "PC1",
color = "super_pop",
shape = "super_pop",
main = "PC1 vs. PC2",
xlab = "PC1 (1.43% of variation)",
ylab = "PC2 (1.24% of variation)")
Plot PC2 vs. PC3
ggpubr::ggscatter(data = vcf_pca_scores2,
y = "PC3",
x = "PC2",
color = "super_pop",
shape = "super_pop",
main = "PC2 vs. PC3",
xlab = "PC2 (1.24% of variation)",
ylab = "PC3 (1.12% of variation)")
Plot PC1 vs. PC3
ggpubr::ggscatter(data = vcf_pca_scores2,
y = "PC3",
x = "PC1",
color = "super_pop",
shape = "super_pop",
main = "PC1 vs. PC3",
xlab = "PC1 (1.43% of variation)",
ylab = "PC3 (1.12% of variation)")