Worked Example: PCA on SNPs data from a vcf file Part 1 - Data Preparation
2022-11-19
Introduction
In this worked example you will replicate a PCA on a published dataset.
The example is split into 2 Parts:
- Part 1: Data Preparation (this file)
- Part 2: Data analysis with PCA
In this Data Preparation phase, you will do the following things:
- Load the SNP genotypes in .vcf format
(
vcfR::read.vcfR()) - Extract the genotypes into an R-compatible format
(
vcfR::extract.gt()) - Rotate the data into the standard R analysis format
(
t()) - Remove individuals (rows) from the data set that have >50% NAs (using a function I wrote)
- Remove SNPs (columns) that are fixed
- Impute remaining NAs (using a
for()loop) - Save the prepared data as a
.csvfile for the next step (write.csv())
Biological background
This worked example is based on a paper in the journal Molecular Ecology from 2017 by Jennifer Walsh titled Subspecies delineation amid phenotypic, geographic and genetic discordance in a songbird.
The study investigated variation between two bird species in the genus Ammodramus: A. nenlsoni and A. caudacutus.
The species A. nenlsoni has been divided into 3 sub-species: A. n. nenlsoni, A.n. alterus, and A n. subvirgatus. The other species, A. caudacutus, has been divided into two subspecies, A.c. caudacutus and A.c. diversus.
The purpose of this study was to investigate to what extent these five subspecies recognized by taxonomists are supported by genetic data. The author’s collected DNA from 75 birds (15 per subspecies) and genotyped 1929 SNPs. They then analyzed the data with Principal Components Analysis (PCA), among other genetic analyzes.
This tutorial will work through all of the steps necessary to re-analyze Walsh et al.s data
Tasks
In the code below all code is provided. Your tasks will be to do 2 things:
- Give a meaningful title to all sections marked “TODO: TITLE”
- Write 1 to 2 sentences describing what is being done and why in all sections marked “TODO: EXPLAIN”
Preliminaries
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
file all_loci.vcf.
getwd()## [1] "/Users/danyajung/Desktop/CB /all_loci.vcf"list.files()## [1] "07-mean_imputation-2.html"
## [2] "07-mean_imputation-2.Rmd"
## [3] "08-PCA_worked.html"
## [4] "08-PCA_worked.Rmd"
## [5] "09-PCA_worked_example-SNPs-part1.Rmd"
## [6] "10-PCA_worked_example-SNPs-part2.Rmd"
## [7] "11.21443531-21683531.ALL.chr11_GRCh38.genotypes.20170504.vcf"
## [8] "11.21443531-21683531.ALL.chr11_GRCh38.genotypes.20170504.vcf.zip"
## [9] "1540_final_project_Final_Report_template.pdf"
## [10] "1540_final_report_flowchart.pdf"
## [11] "2.136483646-136733646.ALL.chr2_GRCh38.genotypes.20170504.vcf.gz"
## [12] "all_loci-2.vcf"
## [13] "all_loci.csv"
## [14] "all_loci.numbers"
## [15] "all_loci.txt"
## [16] "all_loci.vcf.txt"
## [17] "allomtery_3_scatterplot3d (1).Rmd"
## [18] "bird_snps_remove_NAs.Rmd"
## [19] "center_function.R"
## [20] "cluster_analysis_portfolio.Rmd"
## [21] "feature_engineering_intro_2_functions-part2.Rmd"
## [22] "feature_engineering.Rmd"
## [23] "final_report_template.Rmd"
## [24] "fst_exploration_in_class.Rmd"
## [25] "portfolio_ggpubr_intro-2-2.Rmd"
## [26] "portfolio_ggpubr_intro-2.Rmd"
## [27] "portfolio_ggpubr_log_transformation-2.Rmd"
## [28] "portfolio_ggpubr_log_transformation.Rmd"
## [29] "RPubs Portfolio.Rmd"
## [30] "RPubs Portfolio.Rmd 2"
## [31] "RPubs Portfolio.Rmd.zip"
## [32] "RStudio-2022.07.2-576.dmg"
## [33] "test.Rmd"
## [34] "transpose_VCF_data-2.Rmd"
## [35] "transpose_VCF_data-3.Rmd"
## [36] "true.txt"
## [37] "walsh2017morphology.csv"
## [38] "walsh2017morphology.numbers"
## [39] "walsh2017morphology.RData"
## [40] "working_directory_practice-2.Rmd"
## [41] "working_directory_practice-3.Rmd"
## [42] "working_directory_practice-4.html"
## [43] "working_directory_practice-4.Rmd"list.files(pattern = "vcf")## [1] "11.21443531-21683531.ALL.chr11_GRCh38.genotypes.20170504.vcf"
## [2] "11.21443531-21683531.ALL.chr11_GRCh38.genotypes.20170504.vcf.zip"
## [3] "2.136483646-136733646.ALL.chr2_GRCh38.genotypes.20170504.vcf.gz"
## [4] "all_loci-2.vcf"
## [5] "all_loci.vcf.txt"Data preparation
Data preparation
vcfR::read.vcfR() reads all the data from the specified vcf file and the convertNA argument instructs to convert all missing data to a NA.
snps <- vcfR::read.vcfR("all_loci-2.vcf", convertNA = TRUE)## Scanning file to determine attributes.
## File attributes:
## meta lines: 8
## header_line: 9
## variant count: 1929
## column count: 81
##
Meta line 8 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 1929
## Character matrix gt cols: 81
## skip: 0
## nrows: 1929
## row_num: 0
##
Processed variant 1000
Processed variant: 1929
## All variants processedExtract GT data
extract.gt() to extract the GT data from the vcf file.
snps_num <- vcfR::extract.gt(snps,
element = "GT",
IDtoRowNames = F,
as.numeric = T,
convertNA = T,
return.alleles = F)Transpose
t() transpose the data to get the proper orientation.
snps_num_t <- t(snps_num) data.frame() to convert the snps_num_t to a dataframe object labled snps_num_df.
snps_num_df <- data.frame(snps_num_t) Find NAs
function() returns a vector of NAs using the function is.na determining if it is an NA and the logical operation which(), the function also prints the number of NAs present.
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)
}using nrow() on snps_num_t data frame helps declare the number of rows in the snps_num_t.
# N_rows
# number of rows (individuals)
N_rows <- nrow(snps_num_t)
# 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_num_t)
# 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_num_t[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
}## Results: 28 NAs present
## .Results: 20 NAs present
## .Results: 28 NAs present
## .Results: 24 NAs present
## .Results: 23 NAs present
## .Results: 63 NAs present
## .Results: 51 NAs present
## .Results: 38 NAs present
## .Results: 34 NAs present
## .Results: 24 NAs present
## .Results: 48 NAs present
## .Results: 21 NAs present
## .Results: 42 NAs present
## .Results: 78 NAs present
## .Results: 45 NAs present
## .Results: 21 NAs present
## .Results: 42 NAs present
## .Results: 34 NAs present
## .Results: 66 NAs present
## .Results: 54 NAs present
## .Results: 59 NAs present
## .Results: 52 NAs present
## .Results: 47 NAs present
## .Results: 31 NAs present
## .Results: 63 NAs present
## .Results: 40 NAs present
## .Results: 40 NAs present
## .Results: 22 NAs present
## .Results: 60 NAs present
## .Results: 48 NAs present
## .Results: 961 NAs present
## .Results: 478 NAs present
## .Results: 59 NAs present
## .Results: 26 NAs present
## .Results: 285 NAs present
## .Results: 409 NAs present
## .Results: 1140 NAs present
## .Results: 600 NAs present
## .Results: 1905 NAs present
## .Results: 25 NAs present
## .Results: 1247 NAs present
## .Results: 23 NAs present
## .Results: 750 NAs present
## .Results: 179 NAs present
## .Results: 433 NAs present
## .Results: 123 NAs present
## .Results: 65 NAs present
## .Results: 49 NAs present
## .Results: 192 NAs present
## .Results: 433 NAs present
## .Results: 66 NAs present
## .Results: 597 NAs present
## .Results: 1891 NAs present
## .Results: 207 NAs present
## .Results: 41 NAs present
## .Results: 268 NAs present
## .Results: 43 NAs present
## .Results: 110 NAs present
## .Results: 130 NAs present
## .Results: 90 NAs present
## .Results: 271 NAs present
## .Results: 92 NAs present
## .Results: 103 NAs present
## .Results: 175 NAs present
## .Results: 31 NAs present
## .Results: 66 NAs present
## .Results: 64 NAs present
## .Results: 400 NAs present
## .Results: 192 NAs present
## .Results: 251 NAs present
## .Results: 69 NAs present
## .Results: 58 NAs present
## .cutoff50 is labeled for the number of rows value that is reduced by half and a histogram is created of the storage vector showing the frequency of NAs.
# 50% of N_SNPs
cutoff50 <- N_SNPs*0.5
hist(N_NA)
abline(v = cutoff50,
col = 2,
lwd = 2,
lty = 2)
Multification with 100 to find the percentage of NAs out of all the data in the data frame and rows that with >50 percentage is decalred as the vector_i_NA_50percent.
percent_NA <- N_NA/N_SNPs*100
# Call which() on percent_NA
i_NA_50percent <- which(percent_NA > 50)
snps_num_t02 <- snps_num_t[-i_NA_50percent, ]Row Names
row.names() are added to the dataframe using the snps_num_t02. gsub() changes the row names where things in the first parameter are removed.
row_names <- row.names(snps_num_t02) # Key
row_names02 <- gsub("sample_","",row_names)
sample_id <- gsub("^([ATCG]*)(_)(.*)",
"\\3",
row_names02)
pop_id <- gsub("[01-9]*",
"",
sample_id)
table(pop_id) ## pop_id
## Alt Cau Div Nel Sub
## 15 12 15 15 11Remove invariant columns
invar_omit removed the invariant columns by calculating the standard deviation.
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_t02) ## Dataframe of dim 68 1929 processed...
## 591 columns removedMean Impitation
mean() takes the cleaned data and replace NAs with the mean of its 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
}Save the data
Save the data as a .csv file which can be loaded again later.
write.csv(snps_noNAs, file = "SNPs_cleaned.csv",
row.names = F)Check for the presence of the file with list.files()
list.files(pattern = ".csv")## [1] "all_loci.csv" "SNPs_cleaned.csv"
## [3] "walsh2017morphology.csv"Next steps:
In Part 2, we will re-load the SNPs_cleaned.csv file and
carry an an analysis with PCA.