Merged_updates

[Cristianetaniguti]

• Branches indels_support and check_ped_updated merged and submitted to development.
• Github actions tests fix required before merging to development
• @josuechinchilla your updates are now here

[Copilot]

Pull request overview

This PR merges updates from indels_support and check_ped_updated into development, expanding BIGr’s DArTag/MADC handling (sanity checks + additional metadata support), improving documentation, and enhancing some utilities (VCF filtering, concordance reporting).

Changes:

• Add check_madc_sanity() plus a basic test and documentation; integrate sanity checks into madc2vcf_targets().
• Extend madc2vcf_targets() with markers_info to populate CHROM/POS/REF/ALT directly (incl. indel metadata).
• Update check_ped(), filterVCF(), and imputation_concordance() behaviors/docs (new options, new checks, additional outputs).

Reviewed changes

Copilot reviewed 15 out of 16 changed files in this pull request and generated 14 comments.

Show a summary per file

File	Description
tests/testthat/test-check_madc_sanity.R	Adds a test file for check_madc_sanity().
man/madc2vcf_targets.Rd	Expands madc2vcf_targets() documentation for new args and behavior.
man/check_ped.Rd	Updates check_ped() documentation to reflect new behavior/output.
man/check_madc_sanity.Rd	Adds generated Rd for new check_madc_sanity() function.
R/utils.R	Adjusts check_botloci() to rewrite AlleleID after padding changes.
R/madc2vcf_targets.R	Adds MADC sanity checks + markers_info branch for CHROM/POS/REF/ALT.
R/madc2vcf_all.R	Adds input validation + passes botloci into hap-seq helper; padding checks.
R/imputation_concordance.R	Adds plotting/printing options and ggplot2-based plot.
R/filterVCF.R	Adds optional pre-filter quality-rate outputs; refactors messages and export logic.
R/check_ped.R	Refactors pedigree checks and adds interactive/global-env save behavior.
R/check_madc_sanity.R	Implements new MADC sanity-check helper with messages and checks.
NEWS.md	Adds 0.6.3 release notes.
NAMESPACE	Exports check_madc_sanity.
DESCRIPTION	Bumps version/roxygen note; updates cph affiliation.
BIGr.Rproj	Adds ProjectId metadata.
.gitignore	Adds .DS_Store.

Comments suppressed due to low confidence (1)

R/filterVCF.R:488

• There is a large block of commented-out experimental code after the end of filterVCF() (custom VCF reading tests, hard-coded local paths, etc.). Keeping this in the package source makes maintenance harder and bloats the file. Please remove it or move it to a vignette/dev script under dev/ if it needs to be kept for reference.

#This is not reliable, so no longer use this shortcut to get dosage matrix
#test2 <- vcfR2genlight(vcf)


#####Testing custom VCF reading function######
# Open the gzipped VCF file
#con <- gzfile("/Users/ams866/Desktop/output.vcf", "rt")

# Read in the entire file
#lines <- readLines(con)
#close(con)
# Read in the entire file
#lines <- readLines("/Users/ams866/Desktop/output.vcf")
# Filter out lines that start with ##
#filtered_lines <- lines[!grepl("^##", lines)]
# Create a temporary file to write the filtered lines
#temp_file <- tempfile()
#writeLines(filtered_lines, temp_file)
# Read in the filtered data using read.table or read.csv
#vcf_data <- read.table(temp_file, header = TRUE, sep = "\t", comment.char = "", check.names = FALSE)
# Clean up the temporary file
#unlink(temp_file)

##Extract INFO column and Filter SNPs by those values
#Update the filtering options by the items present in the INFO column?

# Load required library
#library(dplyr)

# Split INFO column into key-value pairs
#vcf_data_parsed <- vcf_data %>%
#  mutate(INFO_PARSED = strsplit(INFO, ";")) %>%
#  unnest(INFO_PARSED) %>%
#  separate(INFO_PARSED, into = c("KEY", "VALUE"), sep = "=") %>%
#  spread(KEY, VALUE)

#Filter by DP
#filtered_vcf_data <- vcf_data_parsed %>%
#  filter(as.numeric(DP) > 10)

# View the filtered dataframe
#print(filtered_vcf_data)

##Extracting and filtering by FORMAT column
# Identify the columns that are not sample columns
#non_sample_cols <- c("#CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT")
# Identify the sample columns
#sample_cols <- setdiff(names(vcf_data), non_sample_cols)
# Extract FORMAT keys
#format_keys <- strsplit(as.character(vcf_data$FORMAT[1]), ":")[[1]]
# Split SAMPLE columns based on FORMAT
#vcf_data_samples <- vcf_data %>%
#  mutate(across(all_of(sample_cols), ~strsplit(as.character(.), ":"))) %>%
#  mutate(across(all_of(sample_cols), ~map(., ~setNames(as.list(.), format_keys)))) %>%
#  unnest_wider(all_of(sample_cols), names_sep = "_")

# View the parsed dataframe
#print(head(vcf_data_samples))

# Create separate dataframes for each FORMAT variable
#format_dfs <- lapply(format_keys, function(format_key) {
#  vcf_data_samples %>%
#    select(ID, ends_with(paste0("_", format_key))) %>%
#    column_to_rownames("ID")
#})

# Assign names to the list elements
#names(format_dfs) <- format_keys

# Access the separate dataframes
#gt_df <- format_dfs$GT  # Genotype dataframe
#ad_df <- format_dfs$AD  # Allelic depths dataframe

#*I think the above method is okay if you only need to filter at the INFO level,
#*But I think if you want to filter for FORMAT, that vcfR is probably best,
#*Will need to explore further if I can easily just filter for MPP by checking if it is above a
#*threshold, and then converting the GT and UD values to NA if so...
#*If that is efficient and works, then I will just use this custom VCF method...

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