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skeleton.Rmd
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---
params:
title: "Genomic Array Quality-Control"
author_name: "Firstname Lastname"
author_affiliation: "Institution"
author_email: "some@email.com"
input_files: NULL
output_directory: NULL
cohort_name: NULL
array: NULL
callrate_samples: 0.95
callrate_snps: 0.95
sex_threshold: !r c(0.2, 0.8)
heterozygosity_threshold: 4
maf_threshold: 0.01
hwe_pvalue: 0.0001
includes_relatives: FALSE
mendelian_samples: 0.05
mendelian_snps: 0.1
ibd_threshold: 0.2
population: NULL
pca_components: 10
pca_threshold: 3
max_labels: 15
check_bim_script: !r system.file("perl", "HRC-1000G-check-bim-NoReadKey.pl", package = "dgapaq")
imputation_ref: "1KG"
imputation_panel: "/disks/DATA/ExternalData/1kg/hg19/Reference_genome/1000GP_Phase3_combined.legend"
ref1kg_panel: "/disks/DATA/ExternalData/1kg/samples_description/integrated_call_samples_v3.20130502.ALL.panel"
ref1kg_fasta: "/disks/DATA/ExternalData/1kg/hg19/human_g1k_v37.fasta"
ref1kg_population: "/disks/DATA/ExternalData/1kg/samples_description/1kg_pop_description.tsv"
ref1kg_genotypes: "/disks/DATA/ExternalData/1kg/hg19/Genotypes/RawPLINK/ALL/"
bin_path: !r list(bcftools = "/usr/bin/bcftools", bgzip = "/usr/bin/bgzip", plink = "/usr/bin/plink1.9", gcta = "/usr/bin/gcta64")
title: '`r params[["title"]]`'
author:
- name: '`r params[["author_name"]]`'
affiliation: '`r params[["author_affiliation"]]`'
email: '`r params[["author_email"]]`'
date: '`r format(Sys.time(), "%B %d, %Y")`'
output:
bookdown::html_document2:
theme: simplex
toc: true
toc_depth: 3
toc_float:
collapsed: false
fig_width: 6.3
fig_height: 4.7
number_sections: true
self_contained: true
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE}
options(stringsAsFactors = FALSE)
### Environment ====================================================================================
output_directory <- params[["output_directory"]]
invisible(
sapply(
X = file.path(output_directory, c("tmp", "plink_qc", "vcf_qc")),
FUN = dir.create,
showWarnings = FALSE,
recursive = TRUE,
mode = "0777"
)
)
temp <- file.path(output_directory, "tmp")
dir.create(file.path(temp, "1kgenome_pruned"), showWarnings = FALSE, recursive = TRUE, mode = "0777")
plink <- params[["bin_path"]][["plink"]]
### Load packages ==================================================================================
suppressPackageStartupMessages({
library("bookdown")
library("data.table")
library("ggplot2")
library("ggforce")
library("ggrepel")
library("ggtext")
library("gt")
library("knitr")
library("parallel")
library("patchwork")
library("scales")
library("stats")
library("sessioninfo")
library("writexl")
})
### knitr settings =================================================================================
knitr::opts_chunk$set(
results = "asis",
size = "small",
include = TRUE,
echo = FALSE,
warning = FALSE,
message = FALSE,
dpi = 120,
tidy = FALSE,
crop = TRUE,
autodep = TRUE,
fig.align = "center",
fig.pos = "!H"
)
### Define theme ===================================================================================
ggplot2::theme_set(
ggplot2::theme_light(base_size = 11) +
ggplot2::theme(
plot.title = ggtext::element_markdown(),
plot.title.position = "plot",
plot.subtitle = ggtext::element_markdown(face = "italic", size = ggplot2::rel(0.8)),
plot.caption = ggtext::element_markdown(face = "italic", size = ggplot2::rel(0.5)),
plot.caption.position = "plot",
axis.text.x = ggtext::element_markdown(),
axis.text.x.top = ggtext::element_markdown(),
axis.text.y = ggtext::element_markdown(),
legend.box.just = "left",
legend.key.size = ggplot2::unit(0.5, "lines"),
legend.text = ggtext::element_markdown(size = ggplot2::rel(0.5))
)
)
### Functions ======================================================================================
sys <- function(command) system(command = command, ignore.stdout = TRUE, ignore.stderr = TRUE)
`%>%` <- gt::`%>%`
percent1 <- scales::percent_format(accuracy = 0.1, suffix = " %")
percent2 <- scales::percent_format(accuracy = 0.01, suffix = " %")
comma <- scales::comma_format(accuracy = 1)
scientific <- function(x, ...) gsub("(.*)e([-+]*)0*(.*)", "\\1 × 10<sup>\\2\\3</sup>", scales::scientific(x, ...))
pval_trans <- function(alpha = NULL, md = FALSE, prefix = FALSE) {
scales::trans_new(
name = "pval",
domain = c(0, 1),
transform = function(x) {x[x < .Machine$double.xmin] <- .Machine$double.xmin; -log(x, 10)},
inverse = function(x) {10^-x},
breaks = (function(n = 5) {
function(x) {
max <- floor(-log(min(c(x, alpha), na.rm = TRUE), base = 10))
if (max == 0) 1 else sort(unique(c(10^-seq(0, max, by = floor(max / n) + 1), alpha)))
}
})(),
format = (function(x) {
g <- function(x) {
gsub(
"1 %*% ", "",
gsub(
"(.*)e([-+]*)0*(.*)", "\\1 %*% 10^\\2\\3",
gsub(
"1e+00", "1",
scales::scientific(x),
fixed = TRUE
)),
fixed = TRUE
)
}
highlight_alpha <- function(x, md = FALSE, prefix = FALSE) {
if (md & nchar(system.file(package = "ggtext")) != 0) {
prefix_text <- if (prefix) "α = " else ""
out <- paste0(
"<b style = 'color:firebrick2;'>",
gsub("(.*) [%][*][%] .*\\^(.*)", paste(prefix_text, "\\1 × 10<sup>\\2</sup>"), g(x)),
"</b>"
)
} else {
prefix_text <- if (prefix) "alpha ==" else ""
out <- parse(text = paste(prefix_text, g(x)))
}
out
}
if (!is.null(alpha)) {
ifelse(
test = scales::scientific(x) == scales::scientific(alpha),
yes = highlight_alpha(x, md, prefix),
no = g(x)
)
} else {
parse(text = g(x))
}
})
)
}
```
```{r data}
if (length(list.files(dirname(params[["input_files"]]), pattern = ".bed")) != 0) {
sys(paste(plink,
"--bfile", params[["input_files"]],
"--snps-only",
"--make-bed",
"--out", file.path(temp, "data")
))
} else {
sys(paste(plink,
"--file", params[["input_files"]],
"--snps-only",
"--make-bed",
"--out", file.path(temp, "data")
))
}
```
# Parameters
* Raw data:
`` `r params[["input_files"]]` ``
* 1,000 Genomes files:
`` `r params[["ref1kg_genotypes"]]` ``
* Reference population:
`` `r if (is.null(params[["population"]])) "unspecified" else params[["population"]]` ``
* Sample-based call rate threshold:
`` `r params[["callrate_samples"]]` ``
* Variant-based call rate threshold:
`` `r params[["callrate_snps"]]` ``
* Number of standard deviations (SD) from the mean heterozygosity rate:
`` `r params[["heterozygosity_threshold"]]` ``
* Proportion of identity by descent (IBD) threshold:
`` `r params[["ibd_threshold"]]` ``
* Number of components from the principal component analysis (PCA):
`` `r params[["pca_components"]]` ``
* Number of standard deviations (SD) from the population centroid based on the PCA:
`` `r params[["pca_threshold"]]` ``
* Minor allele frequency (MAF) threshold:
`` `r params[["maf_threshold"]]` ``
* Hardy-Weinberg equilibrium p-value:
`` `r scales::scientific(params[["hwe_pvalue"]])` ``
* Mendelian error rate threshold:
+ Maximum per-trio error rate:
`` `r if (params[["includes_relatives"]]) params[["mendelian_samples"]] else "not applicable"` ``
+ Ma per-variant error rate:
`` `r if (params[["includes_relatives"]]) params[["mendelian_snps"]] else "not applicable"` ``
# Cohort Overview
Genotyped using `r if (is.null(params[["array"]])) "unspecified" else params[["array"]]` array.
```{r cohort-overview, include = FALSE}
fam_data <- data.table::fread(
file = file.path(temp, "data.fam"),
col.names = c("FID", "IID", "father_id", "mother_id", "sex", "phenotype"),
colClasses = c("character", "character", "character", "character", "numeric", "numeric")
)
samples_duplicated <- sum(table(gsub("[_-][^-_]*$", "", fam_data[["IID"]])) > 1)
snps_number <- nrow(data.table::fread(input = file.path(temp, "data.bim"), select = 1))
fam_data[, cohort := params[["cohort_name"]]]
fam_data[, sex_fct := factor(sex, levels = c(1, 2, 0), labels = c("Male", "Female", "Unspecified"))]
```
PLINK files includes:
* **`r comma(nrow(fam_data))`** samples.
+ **Note:** **`r comma(samples_duplicated)`** samples seemed to have duplicates.
* **`r comma(snps_number)`** variants.
```{r cohort-overview-table}
fam_data[
j = .N,
by = .(FID, gsub("[_-][^-_]*$", "", IID), cohort, sex_fct)
][
j = .N, by = c("cohort", "sex_fct")
][
j = .(sex_fct, N)
] %>%
gt::gt(auto_align = "center") %>%
gt::tab_header(
title = "Samples Available",
subtitle = gt::md(paste0("*", params[["cohort_name"]], "*"))
) %>%
gt::fmt_number(columns = "N", decimals = 0) %>%
gt::grand_summary_rows(
columns = gt::vars(N),
fns = list(Total = ~sum(.)),
formatter = gt::fmt_number,
decimals = 0
) %>%
gt::cols_label(sex_fct = "Sex") %>%
gt::opt_row_striping() %>%
gt::opt_all_caps() %>%
print()
```
# Sample-based QC {#sample-qc}
## Sample Call Rate
Call rate was computed using the `--missing` command in PLINK.
```{r sample-call-rate}
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--missing",
"--out", file.path(temp, "data")
))
callrate_samples <- data.table::fread(
file = file.path(temp, "data.imiss"),
colClasses = c("FID" = "character", "IID" = "character")
)[, labs := data.table::fifelse(F_MISS > 1 - params[["callrate_samples"]], IID, NA_character_)][order(F_MISS)]
samples_to_exclude <- callrate_samples[!is.na(labs), .(FID, IID)]
samples_to_exclude[, QC := "Sample_Call_Rate"]
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--freq",
"--out", file.path(temp, "data")
))
freqsnp <- data.table::fread(file.path(temp, "data.frq"))
data.table::fwrite(
x = freqsnp[MAF >= params[["maf_threshold"]], .(SNP)],
file = file.path(temp, "data_maf_geq.txt"),
col.names = FALSE,
row.names = FALSE,
quote = FALSE
)
data_maf_lower <- freqsnp[MAF < params[["maf_threshold"]], .(SNP)]
is_maf_lower <- nrow(data_maf_lower) > 0
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--missing",
"--extract", file.path(temp, "data_maf_geq.txt"),
"--out", file.path(temp, "data_maf_geq")
))
callrate_samples_geq <- data.table::fread(
file = file.path(temp, "data_maf_geq.imiss"),
colClasses = c("FID" = "character", "IID" = "character")
)[order(F_MISS)]
prop_all <- sum(callrate_samples[, "F_MISS"] > 1 - params[["callrate_samples"]]) / nrow(callrate_samples)
prop_maf_geq <- sum(callrate_samples_geq[, "F_MISS"] > 1 - params[["callrate_samples"]]) / nrow(callrate_samples_geq)
prop_diff_maf_geq <- abs((prop_maf_geq - prop_all) / prop_all) >= 0.05
if (is_maf_lower) {
data.table::fwrite(
x = data_maf_lower,
file = file.path(temp, "data_maf_lower.txt"),
col.names = FALSE,
row.names = FALSE,
quote = FALSE
)
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--missing",
"--extract", file.path(temp, "data_maf_lower.txt"),
"--out", file.path(temp, "data_maf_lower")
))
callrate_samples_lower <- data.table::fread(
file = file.path(temp, "data_maf_lower.imiss"),
colClasses = c("FID" = "character", "IID" = "character")
)[order(F_MISS)]
prop_maf_lower <- sum(callrate_samples_lower[, "F_MISS"] > 1 - params[["callrate_samples"]]) / nrow(callrate_samples_lower)
prop_diff_maf_lower <- abs((prop_maf_lower - prop_all) / prop_all) >= 0.05
} else {
prop_diff_maf_lower <- NA
}
callrate_samples_exclude <- callrate_samples[F_MISS > 1 - params[["callrate_samples"]]][order(F_MISS)]
callrate_samples_exclude[, Call_Rate := 1 - F_MISS]
callrate_samples_exclude <- callrate_samples_exclude[, .(FID, IID, Call_Rate)]
cat(
"\n\nA call rate lower than ", percent2(params[["callrate_samples"]]), " was observed for **",
comma(nrow(callrate_samples_exclude)), "** samples, leading to their exclusion.\n\n",
sep = ""
)
```
```{r sample-call-rate-tab}
cols_table <- c(
"Call Rate Threshold",
"Samples to Exclude\n(All Variants)",
paste0("Samples to Exclude\n(Variants with MAF ≥ ", params[["maf_threshold"]], ")")
)
callrate_thresholds <- sort(unique(c(params[["callrate_samples"]], 0.90, 0.95, 0.97, 0.98, 0.99)), decreasing = TRUE)
cbind.data.frame(
percent2(callrate_thresholds),
rowSums(sapply(callrate_samples[["F_MISS"]], ">", 1 - callrate_thresholds)),
rowSums(sapply(callrate_samples_geq[["F_MISS"]], ">", 1 - callrate_thresholds))
) %>%
`colnames<-`(cols_table) %>%
gt::gt(auto_align = "center") %>%
gt::tab_header("Number Of Samples To Exclude Based On Call Rate Thresholds") %>%
gt::tab_style(
style = gt::cell_fill(color = "dodgerblue", alpha = 0.5),
locations = gt::cells_body(
columns = gt::vars(!!!cols_table),
rows = `Call Rate Threshold` == percent2(params[["callrate_samples"]])
)
) %>%
gt::opt_row_striping() %>%
gt::opt_all_caps() %>%
print()
cat("\n")
```
```{r sample-call-rate-fig}
ggplot2::ggplot(
data = callrate_samples,
mapping = ggplot2::aes(x = length(F_MISS):1, y = 1 - F_MISS)
) +
ggplot2::geom_point(
colour = scales::viridis_pal(begin = 0.5, end = 0.5)(1),
shape = 1,
na.rm = TRUE
) +
ggrepel::geom_label_repel(
data = ~ .x[, labs := if (sum(!is.na(labs)) > params[["max_labels"]]) NA else labs],
mapping = ggplot2::aes(label = labs),
min.segment.length = ggplot2::unit(0, "lines"),
force = 10,
fill = "white",
colour = "firebrick2",
segment.colour = "firebrick2",
size = 3,
na.rm = TRUE
) +
ggplot2::geom_hline(
mapping = ggplot2::aes(yintercept = params[["callrate_samples"]]),
colour = "firebrick2",
linetype = 2
) +
ggplot2::scale_x_continuous(labels = comma, trans = "log10") +
ggplot2::scale_y_continuous(
breaks = function(x) unique(c(scales::breaks_extended()(x), params[["callrate_samples"]])),
labels = function(x) ifelse(x == params[["callrate_samples"]], paste0("<b style='color:firebrick2;'>", percent1(x), "</b>"), percent1(x))
) +
ggplot2::labs(x = "Number of Samples", y = "Call Rate", title = "Sample Call Rate")
```
```{r sample-call-rate-exclude, eval = nrow(callrate_samples_exclude) != 0}
callrate_samples_exclude %>%
gt::gt(auto_align = "center") %>%
gt::fmt_percent("Call_Rate", incl_space = TRUE) %>%
gt::cols_label(Call_Rate = "Call Rate") %>%
gt::tab_header(paste(
"Samples To Be Excluded For A Call Rate Threshold Of", percent2(params[["callrate_samples"]])
)) %>%
gt::opt_row_striping() %>%
gt::opt_all_caps() %>%
print()
```
```{r sample-call-rate-note}
is_cr_note <- (!is.na(prop_diff_maf_lower) & prop_diff_maf_lower) |
(!is.na(prop_diff_maf_geq) & prop_diff_maf_geq)
if (is_cr_note) {
cat("**Note:**\n\n")
if (!is.na(prop_diff_maf_lower) & prop_diff_maf_lower) {
cat(
"* The sample-based call rate was different for ",
"variants with MAF < ", params[["maf_threshold"]],
" (", percent2(prop_maf_lower), ") ",
"compared to the call rate based on all variants ",
"(", percent2(prop_all), ").\n\n",
sep = ""
)
}
if (!is.na(prop_diff_maf_geq) & prop_diff_maf_geq) {
cat(
"* The sample-based call rate was different for ",
"variants with MAF ≥ ", params[["maf_threshold"]],
" (", percent2(prop_maf_geq), ") ",
"compared to the call rate based on all variants ",
"(", percent2(prop_all), ").\n\n",
sep = ""
)
}
cat("\n")
}
```
## Genotypic Sex (Homozygosity Rate)
The mean homozygosity rate across the X-chromosome was computed using the `--check-sex` command in PLINK.
```{r sex-check}
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--check-sex", min(params[["sex_threshold"]]), max(params[["sex_threshold"]]),
"--out", file.path(temp, "data")
))
sex_file_check <- file.exists(file.path(temp, "data.sexcheck"))
if (!sex_file_check) {
cat("**Warning:** sex check was not achieved, *i.e.*, no polymorphic X chromosome.\n\n")
}
sexcheck <- merge(
x = data.table::fread(
file = file.path(temp, "data.sexcheck"),
colClasses = c("FID" = "character", "IID" = "character")
),
y = callrate_samples,
by = c("FID", "IID"),
all = TRUE
)
sexcheck <- merge(x = sexcheck, y = fam_data, by = c("FID", "IID"), all.x = TRUE)
sexcheck[, sex_discrepancy := sex != SNPSEX]
sexcheck[, STATUS := data.table::fifelse(sex_discrepancy | is.na(sex_discrepancy) | `F` >= min(params[["sex_threshold"]]) & `F` < max(params[["sex_threshold"]]), "PROBLEM", STATUS)]
sexcheck[, labs := data.table::fifelse(STATUS == "PROBLEM" & F_MISS < 1 - params[["callrate_samples"]], IID, NA_character_)]
sexcheck_problem <- sexcheck[STATUS == "PROBLEM"]
sexcheck_discrepancy <- sexcheck_problem[(sex_discrepancy), .(FID, IID)][, QC := "Sex_Discrepancy"]
sexcheck_missing <- sexcheck_problem[is.na(sex_discrepancy), .(FID, IID)][, QC := "Sex_Missing"]
sexcheck_other <- sexcheck_problem[!(sex_discrepancy), .(FID, IID)][, QC := "Sex_F"]
samples_to_exclude <- rbind(
samples_to_exclude,
sexcheck_discrepancy,
sexcheck_missing,
sexcheck_other
)
if (sex_file_check & nrow(sexcheck_problem) > 0) {
cat(
paste0(
'For **', comma(nrow(sexcheck_problem)), '** samples ',
'(including **', comma(sum(sexcheck_problem[, "sex"] == 0)), '**, with unspecified sex and **', comma(sum(sexcheck_problem[, "SNPSEX"] == 0)), '** samples with undetermined genotypic sex)',
', the sex discrepancy between genotyping data and reported sex could not be resolved.',
'\n\n'
)
)
}
```
```{r sex-check-fig, eval = sex_file_check}
ggplot2::ggplot(
data = sexcheck[!is.na(`F`), ],
mapping = ggplot2::aes(x = `F`, y = 1 - F_MISS)
) +
ggplot2::geom_rect(
mapping = ggplot2::aes(xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = params[["callrate_samples"]]),
fill = "grey95",
colour = "transparent"
) +
ggplot2::annotate(
geom = "rect",
xmin = -Inf, xmax = -0.2, ymin = -Inf, ymax = Inf,
fill = "firebrick2",
alpha = 0.10,
colour = "transparent"
) +
ggplot2::annotate(
geom = "rect",
xmin = -0.2, xmax = min(params[["sex_threshold"]]), ymin = -Inf, ymax = Inf,
fill = "firebrick2",
alpha = 0.2,
colour = "transparent"
) +
ggplot2::annotate(
geom = "rect",
xmin = max(params[["sex_threshold"]]), xmax = 1, ymin = -Inf, ymax = Inf,
fill = "dodgerblue",
alpha = 0.2,
colour = "transparent"
) +
ggplot2::geom_hline(yintercept = params[["callrate_samples"]], colour = "firebrick2", linetype = 2) +
ggplot2::geom_point(
data = ~ .x[!(sex_discrepancy)],
na.rm = TRUE,
shape = 1,
colour = scales::viridis_pal(begin = 0.5, end = 0.5)(1)
) +
ggplot2::geom_point(
data = ~ .x[(sex_discrepancy)],
na.rm = TRUE,
shape = 3,
colour = "firebrick2"
) +
ggrepel::geom_label_repel(
data = ~ .x[(sex_discrepancy)][, labs := if (sum(sex_discrepancy) > params[["max_labels"]]) NA else labs],
mapping = ggplot2::aes(label = labs),
min.segment.length = ggplot2::unit(0, "lines"),
force = 10,
fill = "white",
colour = "firebrick2",
segment.colour = "firebrick2",
size = 3,
na.rm = TRUE
) +
ggplot2::scale_x_continuous(breaks = c(0, params[["sex_threshold"]], 1), labels = percent1) +
ggplot2::scale_y_continuous(
breaks = function(x) unique(c(scales::breaks_extended()(x), params[["callrate_samples"]])),
labels = function(x) ifelse(x == params[["callrate_samples"]], paste0("<b style='color:firebrick2;'>", percent1(x), "</b>"), percent1(x))
) +
ggplot2::labs(
x = "Homozygosity Rate",
y = "Call Rate",
title = "Homozygosity Rate Based on X-chromosome Variants",
subtitle = paste(
"The expected homozygosity rate range:",
"<b style = 'color:firebrick2;'>for female</b> and",
"<b style = 'color:dodgerblue;'>for male</b>.<br>",
"Samples for which sex information is discordant or missing are denoted by \"<b style = 'color:firebrick2;'>+</b>\"."
)
) +
ggplot2::theme(legend.position = "none")
```
```{r sex-check-tab, eval = sex_file_check}
if (nrow(sexcheck_problem) > 0) {
sexcheck_problem[(sex_discrepancy), .(FID, IID, Phenotype = PEDSEX, Genotype = SNPSEX)] %>%
gt::gt(auto_align = "center") %>%
gt::tab_header("Samples To Be Checked") %>%
gt::opt_row_striping() %>%
gt::opt_all_caps() %>%
print()
}
```
## Heterozygosity
The observed heterozygosity rate per sample was computed using the formula:
(Number of non-missing genotypes N(NM) - Number of homozygous genotypes O(Hom)) / N(NM).
N(NM) and O(Hom) were obtained using the `--het` command in PLINK.
```{r heterozygosity, include = FALSE}
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--het",
"--out", file.path(temp, "data_het")
))
heterozygosity <- merge(
x = data.table::fread(
file = file.path(temp, "data_het.het"),
colClasses = c("FID" = "character", "IID" = "character"),
check.names = TRUE
),
y = callrate_samples,
by = c("FID", "IID"),
all = TRUE
)
heterozygosity[, hrate := (N.NM. - O.HOM.) / N.NM., by = F_MISS < 1 - params[["callrate_samples"]]]
heterozygosity[, colour := sqrt( (hrate - mean(hrate, na.rm = TRUE))^2 + (F_MISS - mean(F_MISS, na.rm = TRUE))^2 ), by = F_MISS < 1 - params[["callrate_samples"]]]
heterozygosity[,
labs := data.table::fifelse(
test = F_MISS < 1 - params[["callrate_samples"]] & (
hrate > mean(hrate, na.rm = TRUE) + params[["heterozygosity_threshold"]] * stats::sd(hrate, na.rm = TRUE) |
hrate < mean(hrate, na.rm = TRUE) - params[["heterozygosity_threshold"]] * stats::sd(hrate, na.rm = TRUE)
),
yes = IID,
no = NA_character_
),
by = F_MISS < 1 - params[["callrate_samples"]]
]
samples_to_exclude <- rbind(
samples_to_exclude,
heterozygosity[!is.na(labs), .(FID, IID)][, QC := "Heterozygosity_Check"]
)
if (is_maf_lower) {
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--extract", file.path(temp, "data_maf_lower.txt"),
"--het",
"--out", file.path(temp, "data_het_maf_lower")
))
heterozygosity_maf_lower <- merge(
x = data.table::fread(
file = file.path(temp, "data_het_maf_lower.het"),
colClasses = c("FID" = "character", "IID" = "character"),
check.names = TRUE
),
y = callrate_samples,
by = c("FID", "IID"),
all = TRUE
)
heterozygosity_maf_lower[, hrate := (N.NM. - O.HOM.) / N.NM., by = F_MISS < 1 - params[["callrate_samples"]]]
heterozygosity_maf_lower[,
colour := sqrt( (hrate - mean(hrate, na.rm = TRUE))^2 + (F_MISS - mean(F_MISS, na.rm = TRUE))^2 ),
by = F_MISS < 1 - params[["callrate_samples"]]
]
heterozygosity_maf_lower[,
labs := data.table::fifelse(
test = F_MISS < 1 - params[["callrate_samples"]] & (
hrate > mean(hrate, na.rm = TRUE) + params[["heterozygosity_threshold"]] * stats::sd(hrate, na.rm = TRUE) |
hrate < mean(hrate, na.rm = TRUE) - params[["heterozygosity_threshold"]] * stats::sd(hrate, na.rm = TRUE)
),
yes = IID,
no = NA_character_
),
by = F_MISS < 1 - params[["callrate_samples"]]
]
samples_to_exclude <- rbind(
samples_to_exclude,
heterozygosity_maf_lower[!is.na(labs), .(FID, IID)][, QC := paste0("Heterozygosity_Check_maf_", params[["maf_threshold"]])]
)
}
```
```{r heterozygosity-fig}
plot_het <- function(data, params) {
ggplot2::ggplot(data = data[, labs := if (sum(!is.na(labs)) > params[["max_labels"]]) NA else labs], mapping = ggplot2::aes(x = 1 - F_MISS, y = hrate)) +
ggplot2::geom_rect(
mapping = ggplot2::aes(ymin = -Inf, ymax = Inf, xmin = -Inf, xmax = params[["callrate_samples"]]),
fill = "grey95",
colour = "transparent"
) +
ggplot2::geom_point(mapping = ggplot2::aes(colour = colour), shape = 1, na.rm = TRUE) +
ggplot2::geom_vline(
mapping = ggplot2::aes(xintercept = params[["callrate_samples"]]),
colour = "firebrick2",
linetype = 2
) +
ggplot2::geom_hline(
data = ~ .x[
F_MISS < 1 - params[["callrate_samples"]],
.(
yintercept = mean(hrate, na.rm = TRUE) - params[["heterozygosity_threshold"]] * stats::sd(hrate, na.rm = TRUE)
)
],
mapping = ggplot2::aes(yintercept = yintercept),
colour = "firebrick2",
linetype = 2
) +
ggplot2::geom_hline(
data = ~ .x[
F_MISS < 1 - params[["callrate_samples"]],
.(
yintercept = mean(hrate, na.rm = TRUE) + params[["heterozygosity_threshold"]] * stats::sd(hrate, na.rm = TRUE)
)
],
mapping = ggplot2::aes(yintercept = yintercept),
colour = "firebrick2",
linetype = 2
) +
ggrepel::geom_label_repel(
data = ~ .x[!is.na(labs)],
mapping = ggplot2::aes(label = labs),
min.segment.length = ggplot2::unit(0, "lines"),
force = 10,
fill = "white",
colour = "firebrick2",
segment.colour = "firebrick2",
size = 3,
na.rm = TRUE
) +
ggplot2::scale_colour_viridis_c(begin = 0.2, end = 0.8, trans = "sqrt") +
ggplot2::scale_x_continuous(
breaks = function(x) unique(c(scales::breaks_extended()(x), params[["callrate_samples"]])),
labels = function(x) ifelse(x == params[["callrate_samples"]], paste0("<b style='color:firebrick2;'>", percent1(x), "</b>"), percent1(x)),
guide = ggplot2::guide_axis(angle = 45)
) +
ggplot2::labs(x = "Call Rate", y = "Heterozygosity Rate") +
ggplot2::theme(legend.position = "none")
}
if (is_maf_lower) {
patchwork::wrap_plots(plot_het(heterozygosity, params), plot_het(heterozygosity_maf_lower, params), ncol = 2) +
patchwork::plot_annotation(
title = "Heterozygosity Rate",
tag_levels = "A",
subtitle = paste0(
"The horizontal red lines (\"<b style = 'color:firebrick2;'>---</b>\") denotes the ", params[["heterozygosity_threshold"]],
" times the standard deviations (SD) from the mean heterozygosity rate,<br>",
"based on: <b>A</b>) all variants, <b>B</b>) variants with a MAF < ", params[["maf_threshold"]], "."
)
)
} else {
patchwork::wrap_plots(plot_het(heterozygosity, params), ncol = 1) +
patchwork::plot_annotation(
title = "Heterozygosity Rate",
subtitle = paste0(
"The horizontal red lines (\"<b style = 'color:firebrick2;'>---</b>\") denotes the ", params[["heterozygosity_threshold"]],
" times the standard deviations (SD) from the mean heterozygosity rate, based on all variants."
)
)
}
```
```{r heterozygosity-tab, eval = nrow(heterozygosity[!is.na(labs)]) > 0}
heterozygosity[!is.na(labs), .(FID, IID, O.HOM., E.HOM., N.NM., hrate)] %>%
gt::gt(auto_align = "center") %>%
gt::tab_header(paste(
"Samples To Be Excluded For An Heterozygosity Rate Higher Than", params[["heterozygosity_threshold"]],
"Times The Standard Deviations From The Mean Heterozygosity Rate."
)) %>%
gt::opt_row_striping() %>%
gt::opt_all_caps() %>%
print()
```
## Relatedness
`r if (params[["includes_relatives"]]) cat("**Note:** Not applicable.\n\n")`
In order to identify pairs of samples who look too similar to each other,
pairwise IBD (identity by descent) was computed, using the `--genome` command in PLINK.
Prior to the analysis, the dataset was pruned to exclude high-LD-regions,
that is no variants with MAF < `r params[["maf_threshold"]]` and no pair of variants within 50 Kb have an r² > 0.2.
If the samples in the cohort **`r params[["cohort_name"]]`** are not supposed to be related,
the expected pairwise $\hat{\pi}$ ("PI_HAT") should be less than `r params[["ibd_threshold"]]`.
Pairs of sample with $\hat{\pi}$ ("PI_HAT") greater than `r params[["ibd_threshold"]]` were flagged.
```{r relatedness, eval = !params[["includes_relatives"]], include = FALSE}
## Pruning Data
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--autosome",
"--set-hh-missing",
"--maf", params[["maf_threshold"]],
"--r2",
"--ld-window-r2 0.2",
"--ld-window-kb 50",
"--make-bed",
"--out", file.path(temp, "data_pruned")
))
sys(paste(plink,
"--bfile", file.path(temp, "data_pruned"),
"--freq",
"--out ", file.path(temp, "data_pruned")
))
sys(paste(plink,
"--bfile", file.path(temp, "data_pruned"),
"--read-freq", file.path(temp, "data_pruned.frq"),
"--genome",
"--min", params[["ibd_threshold"]],
"--out", file.path(temp, "data_pruned_freq")
))
relatedness <- unique(
data.table::fread(
file = file.path(temp, "data_pruned_freq.genome"),
colClasses = c(
"FID1" = "character", "IID1" = "character",
"FID2" = "character", "IID2" = "character",
"PI_HAT" = "numeric"
)
)
)
relatedness <- relatedness[!paste(FID1, IID1, sep = "_") %in% samples_to_exclude[, paste(FID, IID, sep = "_")], ]
relatedness <- relatedness[!paste(FID2, IID2, sep = "_") %in% samples_to_exclude[, paste(FID, IID, sep = "_")], ]
```
```{r relatedness-tab, eval = !params[["includes_relatives"]]}
relatedness_tab <- relatedness[, .N, by = cut(
x = PI_HAT,
breaks = c(params[["ibd_threshold"]], 0.3, 0.4, 0.6, 0.8, 1),
include.lowest = TRUE
)][order(cut)]
relatedness_tab[,
cut := factor(cut, levels = levels(cut), labels = paste0(
levels(cut),
c(" = Second Degree Relatives", "", " = First Degree Relatives", "", " = MZ Twins/Duplicates")
))
]
if (nrow(relatedness_tab) > 0) {
relatedness_tab %>%
gt::gt(auto_align = "center") %>%
gt::cols_label(cut = "IBD Windows", N = "Pair Count") %>%
gt::tab_header(paste0(
'The Distribution Of "PI_HAT" (Proportion Of IBD) For Related Samples In The Dataset ',
"(IBD ≥ ", params[["ibd_threshold"]], ")"
)) %>%
gt::opt_row_striping() %>%
gt::opt_all_caps() %>%
print()
}
related_samples <- merge(
x = relatedness[, .(FID1, IID1, FID2, IID2, PI_HAT)],
y = callrate_samples[, .(FID, IID, F_MISS)],
by.x = c("FID1", "IID1"),
by.y = c("FID", "IID")
)
related_samples <- merge(
x = related_samples,
y = callrate_samples[, .(FID, IID, F_MISS)],
by.x = c("FID2", "IID2"),
by.y = c("FID", "IID"),
suffixes = c("1", "2")
)
related_samples[, Call_Rate1 := percent2(1 - F_MISS1)]
related_samples[, Call_Rate2 := percent2(1 - F_MISS2)]
related_samples <- related_samples[, .SD, .SDcols = !c("F_MISS1", "F_MISS2")][order(-PI_HAT), ]
related_samples[,
IBD := cut(
x = PI_HAT,
breaks = c(params[["ibd_threshold"]], 0.3, 0.4, 0.6, 0.8, 1),
include.lowest = TRUE
)
]
related_samples[,
IBD := factor(IBD, levels = levels(IBD), labels = paste0(
levels(IBD),
c(" = Second Degree Relatives", "", " = First Degree Relatives", "", " = MZ Twins/Duplicates")
))
]
if (nrow(related_samples) != 0) {
related_samples %>%
gt::gt(groupname_col = "IBD", auto_align = "center") %>%
gt::tab_header(paste(
"Pairs Of Samples With A Proportion Of IBD Higher Than", params[["ibd_threshold"]]
)) %>%
gt::opt_row_striping() %>%
gt::opt_all_caps() %>%
print()
} else {
cat("\n\nNo pairs of samples found with a proportion of IBD higher than", params[["ibd_threshold"]], ".\n\n")
}
samples_to_exclude <- rbind(
samples_to_exclude,
unique(rbind(
relatedness[PI_HAT >= params[["ibd_threshold"]], .(FID = FID1, IID = IID1)],
relatedness[PI_HAT >= params[["ibd_threshold"]], .(FID = FID2, IID = IID2)]
))[, QC := "Relatedness"]
)
```
## Mendel Errors
The sample-wise Mendel error was estimated using the `--mendel` command in PLINK.
The per-trio error rate threshold is `r params[["mendelian_samples"]]`.
```{r imendel-error}
if (!params[["includes_relatives"]]) cat("**Note:** Not applicable.\n\n")
imendel_file_check <- params[["includes_relatives"]]
```
```{r imendel-error-tab, eval = params[["includes_relatives"]]}
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--mendel",
"--out ", file.path(temp, "data")
))
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--extract", file.path(temp, "data_maf_geq.txt"),
"--mendel",
"--out", file.path(temp, "data_maf_geq")
))
if (is_maf_lower) {
sys(paste(plink,
"--bfile", file.path(temp, "data"),
"--extract", file.path(temp, "data_maf_lower.txt"),
"--mendel",
"--out", file.path(temp, "data_maf_lower")
))
}
imendel_file_check <- file.exists(file.path(temp, "data.imendel"))
if (!imendel_file_check) {
cat("Mendel error rates check was not performed due to missing informations.\n\n")
} else {
mendel_errors <- data.table::fread(
file = file.path(temp, "data.imendel"),
colClasses = c("FID" = "character", "IID" = "character")
)
mendel_errors[, percentage := N / snps_number]
mendel_errors[, labs := data.table::fifelse(percentage > params[["mendelian_samples"]], IID, NA_character_)]
mendel_errors[, labs_fam := data.table::fifelse(percentage > params[["mendelian_samples"]], FID, NA_character_)]
mendel_errors_geq <- data.table::fread(
file = file.path(temp, "data_maf_geq.imendel"),
colClasses = c("FID" = "character", "IID" = "character")
)[, percentage := N / snps_number]
prop_maf_geq <- sum(mendel_errors_geq[["percentage"]] > params[["mendelian_samples"]]) /
nrow(mendel_errors_geq)
prop_all <- sum(mendel_errors[["percentage"]] > params[["mendelian_samples"]]) /
nrow(mendel_errors)