Akash Patel Final Project Workflow
2022-12-07
Load necessary R 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 2.6-4library(ggplot2)
library(ggpubr)# Confirm working directory and location of files
getwd()## [1] "/Users/akashpatel/Desktop/Computational Biology Code:Screenshots"list.files(pattern = "vcf")## [1] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf"
## [2] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"
## [3] "all_loci.vcf"
## [4] "code_checkpoint_vcfR.html"
## [5] "code_checkpoint_vcfR.Rmd"
## [6] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf"
## [7] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf.gz"
## [8] "vcf_num_df.csv"
## [9] "vcf_num_df2.csv"
## [10] "vcf_num.csv"
## [11] "vcfR_test.vcf"
## [12] "vcfR_test.vcf.gz"#Set up SNP data up for R
my_vcf <- "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf.gz"#Load the vcf data
vcf <- vcfR::read.vcfR(my_vcf, convertNA = T)## Scanning file to determine attributes.
## File attributes:
## meta lines: 130
## header_line: 131
## variant count: 6955
## column count: 2513
##
Meta line 130 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 6955
## Character matrix gt cols: 2513
## skip: 0
## nrows: 6955
## row_num: 0
##
Processed variant 1000
Processed variant 2000
Processed variant 3000
Processed variant 4000
Processed variant 5000
Processed variant 6000
Processed variant: 6955
## All variants processed# Convert Raw VCF file to genotype scores(Allele Counts)
vcf_num <- vcfR::extract.gt(vcf,
element = "GT",
IDtoRowNames = F,
as.numeric = T,
convertNA = T)# Save the csv
write.csv(vcf_num, file = "vcf_num.csv", row.names = F)#Confirm Presence of File
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.html"
## [7] "09-PCA_worked_example-SNPs-part1.Rmd"
## [8] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf"
## [9] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"
## [10] "10-PCA_worked_example-SNPs-part2.html"
## [11] "10-PCA_worked_example-SNPs-part2.Rmd"
## [12] "1000genomes_people_info2-1.csv"
## [13] "akash.Rmd"
## [14] "akashpatel_data.csv"
## [15] "AkashPatelFinalProjectWorkflow.html"
## [16] "AkashPatelFinalProjectWorkflow.Rmd"
## [17] "all_loci.vcf"
## [18] "allomtery_3_scatterplot3d (1).Rmd"
## [19] "bird_snps_remove_NAs.html"
## [20] "bird_snps_remove_NAs.Rmd"
## [21] "center_function.R"
## [22] "cluster_analysis_portfolio.Rmd"
## [23] "code_checkpoint_vcfR.html"
## [24] "code_checkpoint_vcfR.Rmd"
## [25] "Comp Bio Test 3.Rmd"
## [26] "Computational Biology Code:Screenshots.Rproj"
## [27] "feature_engineering_intro_2_functions-part2.Rmd"
## [28] "feature_engineering.Rmd"
## [29] "Final Project.Rmd"
## [30] "final_report_template.Rmd"
## [31] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf"
## [32] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf.gz"
## [33] "FinalProjectCheck.Rmd"
## [34] "fst_exploration_in_class-STUDENT.html"
## [35] "fst_exploration_in_class-STUDENT.Rmd"
## [36] "fst_exploration_in_class.Rmd"
## [37] "genome_scan_fst-2.Rmd"
## [38] "gwas_pheno_env.csv"
## [39] "Junk.Rmd"
## [40] "Loading your VCF final project file into R.Rmd"
## [41] "Loading-your-VCF-final-project-file-into-R.html"
## [42] "PCA-missing_data.Rmd"
## [43] "pheno.csv"
## [44] "portfolio_ggpubr_intro-2.Rmd"
## [45] "portfolio_ggpubr_log_transformation.Rmd"
## [46] "removing_fixed_alleles.html"
## [47] "removing_fixed_alleles.Rmd"
## [48] "rsconnect"
## [49] "Screen Shot 2022-09-29 at 5.32.05 PM.png"
## [50] "Screen Shot 2022-09-29 at 5.32.27 PM.png"
## [51] "Screen Shot 2022-09-29 at 5.33.34 PM.png"
## [52] "Screen Shot 2022-10-05 at 11.28.51 PM.png"
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## [58] "Screen Shot 2022-10-17 at 11.27.24 PM.png"
## [59] "Screen Shot 2022-10-17 at 11.38.48 PM.png"
## [60] "Screen Shot 2022-11-26 at 9.01.14 PM.png"
## [61] "SNPs_cleaned.csv"
## [62] "test.html"
## [63] "test.Rmd"
## [64] "TestOct25.docx"
## [65] "TestOct25.Rmd"
## [66] "transpose_VCF_data.html"
## [67] "transpose_VCF_data.Rmd"
## [68] "vcf_num_df.csv"
## [69] "vcf_num_df2.csv"
## [70] "vcf_num.csv"
## [71] "vcfR_test.vcf"
## [72] "vcfR_test.vcf.gz"
## [73] "vegan_PCA_amino_acids-STUDENT.html"
## [74] "vegan_PCA_amino_acids-STUDENT.Rmd"
## [75] "vegan_pca_with_msleep-STUDENT.html"
## [76] "vegan_pca_with_msleep-STUDENT.Rmd"
## [77] "walsh2017morphology.csv"
## [78] "Weekly Worksheet 12.docx"
## [79] "Weekly Worksheet 13.docx"
## [80] "working_directory_practice.html"
## [81] "working_directory_practice.Rmd"#Tranpose original VCF orientation to R dataframe orientation
vcf_num_t <- t(vcf_num)#Make into a data frame
vcf_num_df <- data.frame(vcf_num_t)#Get person (sample) names
sample <- row.names(vcf_num_df)#Add sample into to dataframe
vcf_num_df <- data.frame(sample, vcf_num_df)#Check working directory
getwd()## [1] "/Users/akashpatel/Desktop/Computational Biology Code:Screenshots"#Save the csv
write.csv(vcf_num_df, file = "vcf_num_df.csv", row.names = F)#Confirm the presence of file
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.html"
## [7] "09-PCA_worked_example-SNPs-part1.Rmd"
## [8] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf"
## [9] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"
## [10] "10-PCA_worked_example-SNPs-part2.html"
## [11] "10-PCA_worked_example-SNPs-part2.Rmd"
## [12] "1000genomes_people_info2-1.csv"
## [13] "akash.Rmd"
## [14] "akashpatel_data.csv"
## [15] "AkashPatelFinalProjectWorkflow.html"
## [16] "AkashPatelFinalProjectWorkflow.Rmd"
## [17] "all_loci.vcf"
## [18] "allomtery_3_scatterplot3d (1).Rmd"
## [19] "bird_snps_remove_NAs.html"
## [20] "bird_snps_remove_NAs.Rmd"
## [21] "center_function.R"
## [22] "cluster_analysis_portfolio.Rmd"
## [23] "code_checkpoint_vcfR.html"
## [24] "code_checkpoint_vcfR.Rmd"
## [25] "Comp Bio Test 3.Rmd"
## [26] "Computational Biology Code:Screenshots.Rproj"
## [27] "feature_engineering_intro_2_functions-part2.Rmd"
## [28] "feature_engineering.Rmd"
## [29] "Final Project.Rmd"
## [30] "final_report_template.Rmd"
## [31] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf"
## [32] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf.gz"
## [33] "FinalProjectCheck.Rmd"
## [34] "fst_exploration_in_class-STUDENT.html"
## [35] "fst_exploration_in_class-STUDENT.Rmd"
## [36] "fst_exploration_in_class.Rmd"
## [37] "genome_scan_fst-2.Rmd"
## [38] "gwas_pheno_env.csv"
## [39] "Junk.Rmd"
## [40] "Loading your VCF final project file into R.Rmd"
## [41] "Loading-your-VCF-final-project-file-into-R.html"
## [42] "PCA-missing_data.Rmd"
## [43] "pheno.csv"
## [44] "portfolio_ggpubr_intro-2.Rmd"
## [45] "portfolio_ggpubr_log_transformation.Rmd"
## [46] "removing_fixed_alleles.html"
## [47] "removing_fixed_alleles.Rmd"
## [48] "rsconnect"
## [49] "Screen Shot 2022-09-29 at 5.32.05 PM.png"
## [50] "Screen Shot 2022-09-29 at 5.32.27 PM.png"
## [51] "Screen Shot 2022-09-29 at 5.33.34 PM.png"
## [52] "Screen Shot 2022-10-05 at 11.28.51 PM.png"
## [53] "Screen Shot 2022-10-06 at 12.32.06 PM.png"
## [54] "Screen Shot 2022-10-08 at 1.05.27 PM.png"
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## [56] "Screen Shot 2022-10-08 at 12.32.11 PM.png"
## [57] "Screen Shot 2022-10-17 at 11.01.25 PM.png"
## [58] "Screen Shot 2022-10-17 at 11.27.24 PM.png"
## [59] "Screen Shot 2022-10-17 at 11.38.48 PM.png"
## [60] "Screen Shot 2022-11-26 at 9.01.14 PM.png"
## [61] "SNPs_cleaned.csv"
## [62] "test.html"
## [63] "test.Rmd"
## [64] "TestOct25.docx"
## [65] "TestOct25.Rmd"
## [66] "transpose_VCF_data.html"
## [67] "transpose_VCF_data.Rmd"
## [68] "vcf_num_df.csv"
## [69] "vcf_num_df2.csv"
## [70] "vcf_num.csv"
## [71] "vcfR_test.vcf"
## [72] "vcfR_test.vcf.gz"
## [73] "vegan_PCA_amino_acids-STUDENT.html"
## [74] "vegan_PCA_amino_acids-STUDENT.Rmd"
## [75] "vegan_pca_with_msleep-STUDENT.html"
## [76] "vegan_pca_with_msleep-STUDENT.Rmd"
## [77] "walsh2017morphology.csv"
## [78] "Weekly Worksheet 12.docx"
## [79] "Weekly Worksheet 13.docx"
## [80] "working_directory_practice.html"
## [81] "working_directory_practice.Rmd"#Clean data
#Load population meta data
pop_meta <- read.csv(file = "1000genomes_people_info2-1.csv")#Merge meta data with SNP data
names(pop_meta)## [1] "pop" "super_pop" "sample" "sex" "lat" "lng"# make sure column sample appears in the meta data and SNP data
names(vcf_num_df)[1:10]## [1] "sample" "X1" "X2" "X3" "X4" "X5" "X6" "X7"
## [9] "X8" "X9"#Merge the two sets of data
vcf_num_df2 <- merge(pop_meta, vcf_num_df, by = "sample")#Check the dimensions before and after the merge
nrow(vcf_num_df) == nrow(vcf_num_df2)## [1] TRUE#Check the names of the new dataframe
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"#Check working directory
getwd()## [1] "/Users/akashpatel/Desktop/Computational Biology Code:Screenshots"#Save the csv
write.csv(vcf_num_df2, file = "vcf_num_df2.csv", row.names = F)#Confirm the presence of file
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.html"
## [7] "09-PCA_worked_example-SNPs-part1.Rmd"
## [8] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf"
## [9] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"
## [10] "10-PCA_worked_example-SNPs-part2.html"
## [11] "10-PCA_worked_example-SNPs-part2.Rmd"
## [12] "1000genomes_people_info2-1.csv"
## [13] "akash.Rmd"
## [14] "akashpatel_data.csv"
## [15] "AkashPatelFinalProjectWorkflow.html"
## [16] "AkashPatelFinalProjectWorkflow.Rmd"
## [17] "all_loci.vcf"
## [18] "allomtery_3_scatterplot3d (1).Rmd"
## [19] "bird_snps_remove_NAs.html"
## [20] "bird_snps_remove_NAs.Rmd"
## [21] "center_function.R"
## [22] "cluster_analysis_portfolio.Rmd"
## [23] "code_checkpoint_vcfR.html"
## [24] "code_checkpoint_vcfR.Rmd"
## [25] "Comp Bio Test 3.Rmd"
## [26] "Computational Biology Code:Screenshots.Rproj"
## [27] "feature_engineering_intro_2_functions-part2.Rmd"
## [28] "feature_engineering.Rmd"
## [29] "Final Project.Rmd"
## [30] "final_report_template.Rmd"
## [31] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf"
## [32] "FinalProject14.27608412-27848412.ALL.chr14_GRCh38.genotypes.20170504.vcf.gz"
## [33] "FinalProjectCheck.Rmd"
## [34] "fst_exploration_in_class-STUDENT.html"
## [35] "fst_exploration_in_class-STUDENT.Rmd"
## [36] "fst_exploration_in_class.Rmd"
## [37] "genome_scan_fst-2.Rmd"
## [38] "gwas_pheno_env.csv"
## [39] "Junk.Rmd"
## [40] "Loading your VCF final project file into R.Rmd"
## [41] "Loading-your-VCF-final-project-file-into-R.html"
## [42] "PCA-missing_data.Rmd"
## [43] "pheno.csv"
## [44] "portfolio_ggpubr_intro-2.Rmd"
## [45] "portfolio_ggpubr_log_transformation.Rmd"
## [46] "removing_fixed_alleles.html"
## [47] "removing_fixed_alleles.Rmd"
## [48] "rsconnect"
## [49] "Screen Shot 2022-09-29 at 5.32.05 PM.png"
## [50] "Screen Shot 2022-09-29 at 5.32.27 PM.png"
## [51] "Screen Shot 2022-09-29 at 5.33.34 PM.png"
## [52] "Screen Shot 2022-10-05 at 11.28.51 PM.png"
## [53] "Screen Shot 2022-10-06 at 12.32.06 PM.png"
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## [58] "Screen Shot 2022-10-17 at 11.27.24 PM.png"
## [59] "Screen Shot 2022-10-17 at 11.38.48 PM.png"
## [60] "Screen Shot 2022-11-26 at 9.01.14 PM.png"
## [61] "SNPs_cleaned.csv"
## [62] "test.html"
## [63] "test.Rmd"
## [64] "TestOct25.docx"
## [65] "TestOct25.Rmd"
## [66] "transpose_VCF_data.html"
## [67] "transpose_VCF_data.Rmd"
## [68] "vcf_num_df.csv"
## [69] "vcf_num_df2.csv"
## [70] "vcf_num.csv"
## [71] "vcfR_test.vcf"
## [72] "vcfR_test.vcf.gz"
## [73] "vegan_PCA_amino_acids-STUDENT.html"
## [74] "vegan_PCA_amino_acids-STUDENT.Rmd"
## [75] "vegan_pca_with_msleep-STUDENT.html"
## [76] "vegan_pca_with_msleep-STUDENT.Rmd"
## [77] "walsh2017morphology.csv"
## [78] "Weekly Worksheet 12.docx"
## [79] "Weekly Worksheet 13.docx"
## [80] "working_directory_practice.html"
## [81] "working_directory_practice.Rmd"Omit invariant features (skipping subsetting)
#Omit invariant features (skipping subsetting)
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)
}names(vcf_num_df2)[1:10]## [1] "sample" "pop" "super_pop" "sex" "lat" "lng"
## [7] "X1" "X2" "X3" "X4"# we don't want to put these columns through invar_omit(). We'll skip them using negative indexing
# new dataframe to store output
vcf_noinvar <- vcf_num_df2[,-c(1:6)]
#run invar_omit() on numeric data
vcf_noinvar <- invar_omit(vcf_noinvar)## Dataframe of dim 2504 6955 processed...
## 1652 columns removedvcf_noinvar <- data.frame(vcf_num_df2[,c(1:6)],vcf_noinvar)dim(vcf_noinvar)## [1] 2504 5309Create an object to store the number of invariant columns removed
#Create an object to store the number of invariant columns removed
my_meta_N_invar_cols <- 1652#Remove low-quality data
# if you were working with data that was low-quality then you'd want to consider removing rows or SNPs with many 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)
}#for() loop to 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 vcf_noinvar()
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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## .Results: 1 NAs present
## .Results: 1 NAs present
## .#Check if any row has <50% NAs
cutoff50 <- N_SNPs*0.5
percent_NA <- N_NA/N_SNPs*100
any(percent_NA > 50)## [1] FALSE#What is the average number of NAs per row? It is probably 0 or close to 0
mean(percent_NA)## [1] 0.002640341#Save the mean percent of NAs per row
my_meta_N_meanNA_rows <- mean(percent_NA)#Imputation of NAs
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" "X3" "X4" "X6"vcf_noNA <- vcf_noinvar
vcf_noNA[, -c(1:6)] <- mean_imputation(vcf_noinvar[,-c(1:6)])## This make take some time...#Prepare for PCA
# scale the data
# new copy of data
vcf_scaled <- vcf_noNA
# scale
vcf_scaled[, -c(1:6)] <- scale(vcf_noNA[, -c(1:6)])write.csv(vcf_scaled, file = "akashpatel_data.csv", row.names = F)#Run the PCA
vcf_pca <- prcomp(vcf_scaled[, -c(1:6)])#PCA diagnostics
# Examine the default screeplot
screeplot(vcf_pca)
#Calculate explained variation
PCA_variation <- function(pca_summary, PCs = 2){
var_explained <- pca_summary$importance[2,1:PCs] * 100
var_explained <- round(var_explained,3)
return(var_explained)
}#Extract PCA variation and calculate percentage variation
vcf_pca_summary <- summary(vcf_pca)
var_out <- PCA_variation(vcf_pca_summary, PCs = 500)#Calculate the cut off rule of thumb
#number of dimensions in data
N_columns <- ncol(vcf_scaled)
#The value of the cutoff
cut_off <- 1/N_columns*100#Calculate the number of PCs which exceed the cutoff
#which values below the cutoff
i_cut_off <- which(var_out < cut_off)
#what is the first value below the cutoff
i_cut_off <- min(i_cut_off)## Warning in min(i_cut_off): no non-missing arguments to min; returning Inf#Save the first value below the cutoff
my_meta_N_mean_NA_rowsPCs <- i_cut_off#Extract the amount of variation explained by the first 3 PCs
my_meta_var_PC123 <- var_out[c(1,2,3)]
#Plot percentage variation
#make barplot
barplot(var_out,
main = "Percet variation(%) Scree plot",
ylab = "Percent variation (%) explained",
names.arg = 1:length(var_out))
abline(h = cut_off, col = 2, lwd = 2)
abline(v = i_cut_off)
legend("topright",
col = c(2,1),
lty = c(1,1),
legend = c("Vertical linez; cutoff",
"Horizontal line: 1st value below cut off"))
#Plot cumulative percentage variation
cumulative_variation <- cumsum(var_out)
plot(cumulative_variation, type = "l")
#Plot PCA results
#Calculate scores
#Get the scroes
# call vegan::scores()
vcf_pca_scores <- vegan::scores(vcf_pca)
#Combine the scores with the species information into a dataframe
#call data.frames()
vcf_pca_scores2 <- data.frame(super_pop = vcf_noNA$super_pop,
vcf_pca_scores)
#look information on the variation explained by the PCs
my_meta_var_PC123[1]## PC1
## 2.701my_meta_var_PC123[2]## PC2
## 2.385my_meta_var_PC123[3]## PC3
## 2.039# Plot the results
#Plot the scores, with super population color-coded
#Plot PC1 vs PC2
ggpubr::ggscatter(data = vcf_pca_scores2,
y = "PC2",
x = "PC1",
color = "super_pop",
shape = "super_pop",
main = "PCA Scatterplot",
xlab = "PC1 (2.701% Variation)",
ylab = "PC2 (2.385% Variation)")
#Plot PC2 vs PC3
ggpubr::ggscatter(data = vcf_pca_scores2,
y = "PC3",
x = "PC2",
color = "super_pop",
shape = "super_pop",
main = "PCA Scatterplot",
xlab = "PC2 (2.385% Variation)",
ylab = "PC3 (2.039% Variation)")
#Plot PC1 vs PC3
ggpubr::ggscatter(data = vcf_pca_scores2,
y = "PC3",
x = "PC1",
color = "super_pop",
shape = "super_pop",
main = "PCA Scatterplot",
xlab = "PC1 (2.701% VARIATION)",
ylab = "PC3 (2.039% VARIATION)")