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Merged
gorgitko merged 2 commits into from
Apr 1, 2026
Merged

Adjust to Sarek 3.8.1 + Excel formatting #32

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20c1b90
use new column name for gnomAD OTH AF
gorgitko Mar 23, 2026
1ba3e0f
update conversion to finalistDX excel:
gorgitko Mar 31, 2026
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71 changes: 50 additions & 21 deletions R/sarek_vcf.R
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Original file line number Diff line number Diff line change
@@ -1,3 +1,9 @@
.COLUMNS_TO_ROUND <- c(
"Variant Frequency",
"Variant occurred [%]",
"Variant occurred (with REF+ALT) [%]"
)

.validate_vcf_files <- function(vcf_files) {
vcf_files_missing <- vcf_files[!fs::file_exists(vcf_files)]
if (!rlang::is_empty(vcf_files_missing)) {
Expand Down Expand Up @@ -235,27 +241,26 @@ calculate_variant_stats <- function(vcf_df, vc_tool = SUPPORTED_VC_TOOLS) {
convert_vcf_df_to_finalist <- function(vcf_df) {
n_samples <- unique(vcf_df$SAMPLE) %>% length()

## count variant occurrences within all samples
n_ids <- vcf_df %>%
## distinct IDs within each sample as there are multiple annotations per a single ID
dplyr::distinct(SAMPLE, CHROM, POS, ID, REF, ALT) %>%
dplyr::group_by(CHROM, POS, ID, REF, ALT) %>%
dplyr::tally() %>%
dplyr::mutate(
`Variant occurred` = glue("{n}/{n_samples}") %>% as.character(),
`Variant occurred [%]` = scales::percent(n / n_samples, suffix = "", accuracy = 0.01)
)
variant_occurred_simple <- .calculate_variant_occurred(
vcf_df,
group_by = c("CHROM", "POS"),
output_column = "Variant occurred"
)
variant_occurred_ref_alt <- .calculate_variant_occurred(
vcf_df,
group_by = c("CHROM", "POS", "REF", "ALT"),
output_column = "Variant occurred (with REF+ALT)"
)

vcf_df <- vcf_df %>%
dplyr::left_join(n_ids, by = c("CHROM", "POS", "ID", "REF", "ALT")) %>%
dplyr::left_join(variant_occurred_simple, by = c("CHROM", "POS")) %>%
dplyr::left_join(variant_occurred_ref_alt, by = c("CHROM", "POS", "REF", "ALT")) %>%
tidyr::extract(sift, c("sift_class", "sift_score"), regex = "^(.+)\\((.*)\\)$", remove = FALSE, convert = TRUE) %>%
tidyr::extract(poly_phen, c("poly_phen_class", "poly_phen_score"), regex = "^(.+)\\((.*)\\)$", remove = FALSE, convert = TRUE) %>%
dplyr::mutate(
Genotype = .parse_genotype(gt_GT),
Canonical = dplyr::if_else(canonical == "YES", "YES", "NO"),
`ClinVar Allele ID` = NA_character_,
`ClinVar disease name` = NA_character_,
`ClinVar review status` = NA_character_,
`LRT pred` = NA_real_,
`CADD phred` = NA_real_,
`DANN score` = NA_real_,
Expand Down Expand Up @@ -319,13 +324,11 @@ convert_vcf_df_to_finalist <- function(vcf_df) {
`gnomAD ASJ AF` = dplyr::any_of(c("gnom_ad_asj_af", "gnom_a_de_asj_af")),
`gnomAD EAS AF` = dplyr::any_of(c("gnom_ad_eas_af", "gnom_a_de_eas_af")),
`gnomAD FIN AF` = dplyr::any_of(c("gnom_ad_fin_af", "gnom_a_de_fin_af")),
`gnomAD OTH AF` = dplyr::any_of(c("gnom_ad_oth_af", "gnom_a_de_oth_af")),
`gnomAD OTH AF` = dplyr::any_of(c("gnom_ad_oth_af", "gnom_a_de_oth_af", "gnom_a_de_remaining_af")),
`gnomAD SAS AF` = dplyr::any_of(c("gnom_ad_sas_af", "gnom_a_de_sas_af")),
`Ensembl transcript ID` = feature,
`Pubmed ID` = pubmed,
SAMPLE_variant_key,
`ClinVar disease name`,
`ClinVar review status`,
`LRT pred`,
`CADD phred`,
`DANN score`,
Expand All @@ -339,8 +342,11 @@ convert_vcf_df_to_finalist <- function(vcf_df) {
`Depth of alternate reverse`,
`ClinVar ID` = dplyr::any_of("clinvar_id"),
`ClinVar HGVS` = dplyr::any_of("clinvar_hgvs"),
`ClinVar Review` = dplyr::any_of("clinvar_review"),
dplyr::any_of(c("clin_sig", "clinvar_clnsig"))
`ClinVar disease name` = dplyr::any_of("clinvar_trait"),
`ClinVar review status` = dplyr::any_of("clinvar_review"),
dplyr::any_of(c("clin_sig", "clinvar_clnsig")),
`Variant occurred (with REF+ALT)`,
`Variant occurred (with REF+ALT) [%]`
)

if ("clinvar_clnsig" %in% colnames(vcf_df)) {
Expand All @@ -349,7 +355,7 @@ convert_vcf_df_to_finalist <- function(vcf_df) {
`Clinical significance` = dplyr::any_of("clinvar_clnsig"),
`Clinical significance (old)` = clin_sig
) %>%
dplyr::relocate(`Clinical significance (old)`, .after = `ClinVar Review`)
dplyr::relocate(`Clinical significance (old)`, .after = `ClinVar review status`)
} else {
vcf_df <- vcf_df %>%
dplyr::rename(
Expand All @@ -370,7 +376,8 @@ convert_vcf_df_to_finalist <- function(vcf_df) {
),
dplyr::across(
c(
`Variant occurred [%]`, `Variant Frequency`, `SIFT score`, `PolyPhen score`, `gnomAD AF`,
`Variant occurred [%]`, `Variant occurred (with REF+ALT) [%]`, `Variant Frequency`,
`SIFT score`, `PolyPhen score`, `gnomAD AF`,
`gnomAD NFE AF`, `MAX AF`, `gnomAD AF_`, `gnomAD AFR AF`, `gnomAD AMR AF`, `gnomAD ASJ AF`, `gnomAD EAS AF`,
`gnomAD FIN AF`, `gnomAD OTH AF`, `gnomAD SAS AF`
),
Expand All @@ -384,6 +391,7 @@ convert_vcf_df_to_finalist <- function(vcf_df) {
)
) %>%
dplyr::mutate(`Variant Frequency` = `Variant Frequency` * 100) %>%
dplyr::mutate(dplyr::across(dplyr::all_of(.COLUMNS_TO_ROUND), ~ round(.x, digits = 2))) %>%
dplyr::mutate(dplyr::across(dplyr::where(is.double), .format_float)) %>%
dplyr::mutate(dplyr::across(dplyr::everything(), ~tidyr::replace_na(as.character(.), "-"))) %>%
dplyr::mutate(dplyr::across(dplyr::everything(), ~dplyr::if_else(. == "", "-", .))) %>%
Expand All @@ -410,13 +418,34 @@ convert_vcf_df_to_finalist <- function(vcf_df) {
}

.format_float <- function(values, sep = ",") {
formatted <- formatC(values, format = "f", digits = 6, flag = "", drop0trailing = FALSE) %>%
formatted <- formatC(values, format = "f", digits = 6, flag = "", drop0trailing = TRUE) %>%
stringr::str_trim()

dplyr::if_else(formatted == "NA", NA_character_, formatted) %>%
stringr::str_replace("\\.", ",")
}

#' @title Count variant occurrences within all samples
.calculate_variant_occurred <- function(vcf_df, group_by, output_column) {
group_vars <- rlang::syms(group_by)

col_pct <- glue("{output_column} [%]")

n_samples <- vcf_df %>%
dplyr::distinct(SAMPLE) %>%
nrow()

vcf_df %>%
dplyr::distinct(SAMPLE, !!!group_vars) %>%
dplyr::group_by(!!!group_vars) %>%
dplyr::tally() %>%
dplyr::mutate(
!!output_column := glue::glue("{n}/{n_samples}") %>% as.character(),
!!col_pct := scales::percent(n / n_samples, suffix = "", accuracy = 0.01)
) %>%
dplyr::select(-n)
}

#' @export
get_per_sample_variant_stats <- function(vcf_df) {
vcf_df %>%
Expand Down
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