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.
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## [771] "O Manipulating eukaryotic genomes 2022 pre.pdf"
## [772] "O Manipulating eukaryotic genomes 2022.pptx"
## [773] "Organic Chemistry Structure and Function by K. Peter C. Vollhardt (z-lib.org).pdf"
## [774] "Packet1PERadiologyMonitoringFo.pdf"
## [775] "paley essay (1).pdf"
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## [780] "PCA-missing_data.Rmd"
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## [824] "PHYS2201-notes-ch2.pdf"
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## [827] "Plagiarism – The Writing Center • University of North Carolina at Chapel Hill.pdf"
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## [834] "Population Genetics Invasion Lecture Nov. 15.pdf"
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## [858] "Prelab Activity 3 .pdf"
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## [863] "Preziosi&Farago_ Art is Not What You Think It Is.pdf"
## [864] "Princess Bride pt.2 (1).docx"
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## [871] "Propp Morphology pp 24-59 (1).pdf"
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## [873] "ProtonVPN.dmg"
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## [983] "Sharpley Tourism Notes iMac.pdf"
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## [991] "SOC 0005- A Growing Trend of Leaving America.doc"
## [992] "Soc of Tourism - Approaches, Issues & Findings (Cohen).pdf"
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## [10] "vcfR_test.vcf"
## [11] "vcfR_test.vcf.gz"Data preparation
TODO: Loading SNP data
TODO: Assign the data from the vsf file that is read by they function read.vcfR and assign it to the object snps. There are 1929 variants in this vcf file.
snps <- vcfR::read.vcfR("all_loci.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 processedTODO: Extract the GT infomation from the VCF File
TODO: Use the fucntion extract.gt on the object snps to get the gt score/numeric score of the data and assign it to a new object called snps_num.
snps_num <- vcfR::extract.gt(snps,
element = "GT",
IDtoRowNames = F,
as.numeric = T,
convertNA = T,
return.alleles = F)TODO: Transposee the Data
TODO: Use the t function to transpose the data to change the SNP data into numerica data which allows us to analyze it . We will assign the new transposed data to a new object called snps_num_t.
snps_num_t <- t(snps_num) TODO: Converting the matrix object into data frame object whihc allows us to create things such as plots and run analysis on.
snps_num_df <- data.frame(snps_num_t) TODO: Make a function to remvoe NAs
TODO: We will use the fucntion function to make a new function called find_NAs that will will be use to find the numebr of NAs and whee they are located. The function will tell us what row that the NAs are in.
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)
}TODO: This chunk will tell us the number of number of NA’s that are present in each of the rows. The funtion for will allow us to make a loop that will go through each of the rows one by one and tell us the number of na’s per row. This will also give us new objects that have just the rows, column, and snps.
# 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
## .TODO: We create an object that has a numeric value that is equal to 50% of the number of coulmns. We then make a histagram to represent the range/variation of teh number of na’s in each row in the entire dataframe.
# 50% of N_SNPs
cutoff50 <- N_SNPs*0.5
hist(N_NA)
abline(v = cutoff50,
col = 2,
lwd = 2,
lty = 2)
TODO: We will make an object that has the perctange of na’s per column and then use that to make another object that gives us columns that have less that 50% of NA data by using the which function to identify them.
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, ]TODO: Identifying the Sampling ID
TODO: We will replace the sampling names to the popualtion codes so that we are able to find the frequency of sampels taken froma specific population/ population code. We will do this by using the gsub function to find and replace the same sampling names to the same population codes.
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 11TODO: Make a Function to removve Invariant Columns
TODO: Use the fucntion fucntion to make a new function called invar_omit whoch will identify the invariant columns are remove them. This will produce a new data frame called snps_no_invar that will be free of invariant columns.
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 removedTODO: Replace NA Columns with Mean data
TODO: We will use mean impuation to replace the na’s that are in the columns with the mean of the column that was calculated wth out the NA’s in the column. We will use the for function to create a loop so it runs for the entire dataframe.
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] "ClinicalOutcomesFollowingRotat_DataDictionary_2020-10-16.csv"
## [2] "ClinicalOutcomesFollowingRotat_DataDictionary_2020-10-28 (1).csv"
## [3] "ClinicalOutcomesFollowingRotat_DataDictionary_2020-10-28.csv"
## [4] "ClinicalOutcomesFollowingRotat_DataDictionary_2020-10-30 (1).csv"
## [5] "ClinicalOutcomesFollowingRotat_DataDictionary_2020-10-30.csv"
## [6] "SNPs_cleaned.csv"
## [7] "walsh2017morphology.csv"Next steps:
In Part 2, we will re-load the SNPs_cleaned.csv file and
carry an an analysis with PCA.