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HaplotypePO_Patrilines.R
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HaplotypePO_Patrilines.R
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rm(list = ls())
library(Eagle)
library(tidyr)
library(readr)
library(genio)
library(AlphaSimR)
library(ggplot2)
library(dplyr)
library(VariantAnnotation)
library(vcfR)
############################################################################
######## GENERIC FUNCTIONS
############################################################################
name_genotypes <- function(map_file) {
# Initialize an empty list to store the transformed rows
transformed_list <- list()
# Loop through each row in the dataframe
for (i in 1:nrow(map_file)) {
# Get the current row
current_row <- map_file[i, ]
# Create two copies of the current row with modified marker values
row_copy_1 <- current_row
row_copy_2 <- current_row
row_copy_1$V2 <- paste(current_row$V2, "1", sep = "_")
row_copy_2$V2 <- paste(current_row$V2, "2", sep = "_")
# Add the modified rows to the list
transformed_list[[length(transformed_list) + 1]] <- row_copy_1
transformed_list[[length(transformed_list) + 1]] <- row_copy_2
}
# Convert the list back to a dataframe
Long_map_file <- do.call(rbind, transformed_list)
# Return the column V2
return(Long_map_file$V2)
}
convert_genotypes <- function(genotypes) {
genotypes[genotypes == '1'] <- 0
genotypes[genotypes == '0'] <- NA
genotypes[genotypes == '2'] <- 1
genotypes[genotypes == 'A'] <- 0
genotypes[genotypes == 'C'] <- 1
return(as.numeric(genotypes))
}
transform_genotype <- function(genotype_matrix) {
# Initialize an empty list to store the new rows
new_rows <- list()
# Get the row names of the genotype matrix
individual_ids <- rownames(genotype_matrix)
# Get the unique markers by removing the last two characters (_1 or _2) from the column names
markers <- unique(sub("_.$", "", colnames(genotype_matrix)))
# Loop through each row of the genotype matrix
for (i in seq_len(nrow(genotype_matrix))) {
# Get the current individual ID
ind_id <- individual_ids[i]
# Loop through each marker
for (marker in markers) {
# Create new row names for _1 and _2
ind_id_1 <- paste(ind_id, "1", sep = "_")
ind_id_2 <- paste(ind_id, "2", sep = "_")
# Extract the values for _1 and _2 markers
value_1 <- genotype_matrix[i, paste(marker, "1", sep = "_")]
value_2 <- genotype_matrix[i, paste(marker, "2", sep = "_")]
# Create new rows and add them to the list
new_rows[[length(new_rows) + 1]] <- data.frame(ID = ind_id_1, Marker = marker, Value = value_1)
new_rows[[length(new_rows) + 1]] <- data.frame(ID = ind_id_2, Marker = marker, Value = value_2)
}
}
# Combine all the new rows into a single data frame
result <- do.call(rbind, new_rows)
# Reshape the result to have individuals as row names and markers as column names
result_wide <- reshape(result, timevar = "Marker", idvar = "ID", direction = "wide")
# Clean up the column names
colnames(result_wide) <- sub("Value\\.", "", colnames(result_wide))
# Set the row names to the IDs
rownames(result_wide) <- result_wide$ID
# Remove the ID column
result_wide$ID <- NULL
return(result_wide)
}
get_genotypes <- function(ped, id) {
row <- ped %>% filter(IID == id)
genotypes <- row[,7:ncol(row)]
return(genotypes)
}
Hap1 <- function(df, map) {
# Get the column names that end in _2
columns_ending_in_1 <- grep("_1$", colnames(df), value = TRUE)
# Subset the dataframe with the selected columns
new_df <- df[, columns_ending_in_1, drop = FALSE]
colnames(new_df) <- map[,2]
return(new_df)
}
Hap2 <- function(df, map) {
# Get the column names that end in _2
columns_ending_in_2 <- grep("_2$", colnames(df), value = TRUE)
# Subset the dataframe with the selected columns
new_df <- df[, columns_ending_in_2, drop = FALSE]
colnames(new_df) <- map[,2]
return(new_df)
}
calcGeno <- function(Hap1, Hap2, map){
Hap1 <- t(Hap1)
Hap2 <- t(Hap2)
Geno <- matrix(data = Hap1 + Hap2, nrow = 1)
colnames(Geno) <- map[,2]
return(Geno)
}
assign_parent_haplo <- function(df, maternal = NULL, paternal = NULL, offspring_id = NULL) {
if(!is.null(maternal)){
row_copy <- paste(offspring_id, "maternal", sep = "_")
}
if(!is.null(paternal)){
row_copy <- paste(offspring_id, "paternal", sep = "_")
}
rownames(df) <- row_copy
# Return the column V2
return(df)
}
get_phased_haplotypes <- function(Geno_Error = NULL){
# Set the working directory and read the pedigree file
setwd("~/Desktop/Slovenia data/Attempt2/Nested/General Data")
pedigree <- read_csv("worker_pedigree.csv")
if (Geno_Error == "GE"){
# Set the working directory and read the phased data
#Geno Error
setwd("~/Desktop/Slovenia data/Attempt2/Nested/SNP4/Geno Err/phased")
Nested_ped_phased <- read.table("phased_SNP4_ped_withGenoError_ACformat_QC.ped", header = FALSE)
map <- read.table("phased_SNP4_ped_withGenoError_ACformat_QC.map", header = FALSE)
} else if (Geno_Error == "nGE") {
#No GenoError
setwd("~/Desktop/Slovenia data/Attempt2/Nested/SNP4/No_GE/phased")
Nested_ped_phased <- read.table("SNP4Nested_nGE_phasingTest.ped", header = FALSE)
map <- read.table("SNP4Nested_nGE_phasingTest.map", header = FALSE)
} else{
stop("No Geno_Error provided, must be GE or nGE")
}
# Extract genotype columns and convert them to 0/1 format
genotypes_columns_phased <- Nested_ped_phased[, 7:ncol(Nested_ped_phased)]
genotypes_01_phased <- apply(genotypes_columns_phased, 2, convert_genotypes)
# Generate IDs for the pedigree
IDs_forPed <- name_genotypes(map_file = map)
rownames(genotypes_01_phased) <- Nested_ped_phased$V2
colnames(genotypes_01_phased) <- IDs_forPed
# Transform genotypes
phased_haplotypes <- transform_genotype(genotypes_01_phased)
return(phased_haplotypes)
}
order_by_prefix <- function(df) {
# Extract the part before the underscore
prefix <- as.numeric(sapply(rownames(df), function(x) strsplit(x, "_")[[1]][1]))
# Order the data frame based on the extracted prefix
df_ordered <- df[order(prefix, rownames(df)), , drop = FALSE]
return(df_ordered)
}
####################################################################################
###### Manually convert vcf to ped file - not using PLINK
#####################################################################################
setwd("~/Desktop/Slovenia data/Test_simulated")
pedigree <- read.csv("worker_pedigree.csv")
#ped order is always mum/dpc/workers
ids_all <- c(unique(pedigree$mother), unique(pedigree$dpc), unique(pedigree$id))
ind_id_1 <- paste(ids_all, "1", sep = "_")
ind_id_2 <- paste(ids_all, "2", sep = "_")
setwd("~/Desktop/Slovenia data/PLINK/Phasing/QC/")
convert_VCF <- function(vcf_file = NULL, map_file = NULL){
chr_map <- read.table(map_file)
phasedVCF=vcf_file
system(paste0('bcftools query -f "%REF %ALT [ %GT]\n" ', phasedVCF, ' > PhasedGT.txt'))
df = read.table("PhasedGT.txt")
colnames(df) = c("REF", "ALT", paste0("Ind", 1:(ncol(df)-2)))
refAlt=df[,1:2]
gt=df[3:ncol(df)]
gt_t = t(gt)
for (col in 1:ncol(gt_t)) {
ref = refAlt$REF[col]
alt = refAlt$ALT[col]
gt_t[,col] = gsub("0", ref, gt_t[,col])
gt_t[,col] = gsub("1", alt, gt_t[,col])
gt_t[,col] = gsub("\\|", " ", gt_t[,col])
}
#ped order is always mum/dpc/workers
rownames(gt_t) <- ids_all
#get true haplotypes for colnames
colnames(gt_t) <- chr_map$V2
return(gt_t)
}
nGE_chr1 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr1.vcf", map_file = "nGE_QC_PHASED_chr1.map")
nGE_chr2 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr2.vcf", map_file = "nGE_QC_PHASED_chr2.map")
nGE_chr3 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr3.vcf", map_file = "nGE_QC_PHASED_chr3.map")
nGE_chr4 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr4.vcf", map_file = "nGE_QC_PHASED_chr4.map")
nGE_chr5 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr5.vcf", map_file = "nGE_QC_PHASED_chr5.map")
nGE_chr6 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr6.vcf", map_file = "nGE_QC_PHASED_chr6.map")
nGE_chr7 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr7.vcf", map_file = "nGE_QC_PHASED_chr7.map")
nGE_chr8 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr8.vcf", map_file = "nGE_QC_PHASED_chr8.map")
nGE_chr9 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr9.vcf", map_file = "nGE_QC_PHASED_chr9.map")
nGE_chr10 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr10.vcf", map_file = "nGE_QC_PHASED_chr10.map")
nGE_chr11 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr11.vcf", map_file = "nGE_QC_PHASED_chr11.map")
nGE_chr12 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr12.vcf", map_file = "nGE_QC_PHASED_chr12.map")
nGE_chr13 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr13.vcf", map_file = "nGE_QC_PHASED_chr13.map")
nGE_chr14 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr14.vcf", map_file = "nGE_QC_PHASED_chr14.map")
nGE_chr15 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr15.vcf", map_file = "nGE_QC_PHASED_chr15.map")
nGE_chr16 <- convert_VCF(vcf_file = "nGE_QC_PHASED_chr16.vcf", map_file = "nGE_QC_PHASED_chr16.map")
nGE_all <- cbind(nGE_chr1,nGE_chr2,nGE_chr3,nGE_chr4,nGE_chr5,nGE_chr6,nGE_chr7,nGE_chr8,
nGE_chr9,nGE_chr10,nGE_chr11,nGE_chr12,nGE_chr13,nGE_chr14,nGE_chr15,nGE_chr16)
rm(nGE_chr1,nGE_chr2,nGE_chr3,nGE_chr4,nGE_chr5,nGE_chr6,nGE_chr7,nGE_chr8,
nGE_chr9,nGE_chr10,nGE_chr11,nGE_chr12,nGE_chr13,nGE_chr14,nGE_chr15,nGE_chr16)
GE_chr1 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr1.vcf", map_file = "GE_QC_PHASED_chr1.map")
GE_chr2 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr2.vcf", map_file = "GE_QC_PHASED_chr2.map")
GE_chr3 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr3.vcf", map_file = "GE_QC_PHASED_chr3.map")
GE_chr4 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr4.vcf", map_file = "GE_QC_PHASED_chr4.map")
GE_chr5 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr5.vcf", map_file = "GE_QC_PHASED_chr5.map")
GE_chr6 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr6.vcf", map_file = "GE_QC_PHASED_chr6.map")
GE_chr7 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr7.vcf", map_file = "GE_QC_PHASED_chr7.map")
GE_chr8 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr8.vcf", map_file = "GE_QC_PHASED_chr8.map")
GE_chr9 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr9.vcf", map_file = "GE_QC_PHASED_chr9.map")
GE_chr10 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr10.vcf", map_file = "GE_QC_PHASED_chr10.map")
GE_chr11 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr11.vcf", map_file = "GE_QC_PHASED_chr11.map")
GE_chr12 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr12.vcf", map_file = "GE_QC_PHASED_chr12.map")
GE_chr13 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr13.vcf", map_file = "GE_QC_PHASED_chr13.map")
GE_chr14 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr14.vcf", map_file = "GE_QC_PHASED_chr14.map")
GE_chr15 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr15.vcf", map_file = "GE_QC_PHASED_chr15.map")
GE_chr16 <- convert_VCF(vcf_file = "GE_QC_PHASED_chr16.vcf", map_file = "GE_QC_PHASED_chr16.map")
GE_all <- cbind(GE_chr1,GE_chr2,GE_chr3,GE_chr4,GE_chr5,GE_chr6,GE_chr7,GE_chr8,
GE_chr9,GE_chr10,GE_chr11,GE_chr12,GE_chr13,GE_chr14,GE_chr15,GE_chr16)
rm(GE_chr1,GE_chr2,GE_chr3,GE_chr4,GE_chr5,GE_chr6,GE_chr7,GE_chr8,
GE_chr9,GE_chr10,GE_chr11,GE_chr12,GE_chr13,GE_chr14,GE_chr15,GE_chr16)
get_out_haplotypes <- function(ped_matrix, ind_id_1, ind_id_2) {
# Initialize matrices to hold haplotype data
haplotype_matrix_0 <- matrix(nrow = nrow(ped_matrix), ncol = ncol(ped_matrix))
haplotype_matrix_1 <- matrix(nrow = nrow(ped_matrix), ncol = ncol(ped_matrix))
# Fill the matrices with haplotype data
for (i in 1:nrow(ped_matrix)) {
for (haplotype in 0:1) {
if (haplotype == 0) {
haplotype_matrix_0[i, ] <- sapply(ped_matrix[i, ], function(gt) {
haplotypes <- unlist(strsplit(as.character(gt), " "))
return(haplotypes[haplotype + 1])
})
} else {
haplotype_matrix_1[i, ] <- sapply(ped_matrix[i, ], function(gt) {
haplotypes <- unlist(strsplit(as.character(gt), " "))
return(haplotypes[haplotype + 1])
})
}
}
}
# Convert matrices to dataframes and set row and column names
colnames(haplotype_matrix_0) <- colnames(ped_matrix)
rownames(haplotype_matrix_0) <- ind_id_1
colnames(haplotype_matrix_1) <- colnames(ped_matrix)
rownames(haplotype_matrix_1) <- ind_id_2
# Combine haplotype matrices into a list
haplotype_matrices <- rbind(haplotype_matrix_0, haplotype_matrix_1)
#order them
haplotype_matrices <- order_by_prefix(haplotype_matrices)
return(haplotype_matrices)
}
nGE_haplotypes <- get_out_haplotypes(ped_matrix = nGE_all, ind_id_1 = ind_id_1, ind_id_2 = ind_id_2)
GE_haplotypes <- get_out_haplotypes(ped_matrix = GE_all, ind_id_1 = ind_id_1, ind_id_2 = ind_id_2)
#convert to 0/1 format
nGE4.0_phasedhaplotypes <- apply(nGE_haplotypes, 2, convert_genotypes)
rownames(nGE4.0_phasedhaplotypes) <- rownames(nGE_haplotypes)
GE4.0_phasedhaplotypes <- apply(GE_haplotypes, 2, convert_genotypes)
rownames(GE4.0_phasedhaplotypes) <- rownames(GE_haplotypes)
GE_phased_map <- read.table("GE_QC_prePhase.map")
nGE_phased_map <- read.table("nGE_QC_prePhase.map")
####################################################################################
# Get the true haplotypes
#####################################################################################
setwd("~/Desktop/Slovenia data/Test_simulated")
load("~/Desktop/Slovenia data/Test_simulated/Pop_withFathers.Rdata")
load("~/Desktop/Slovenia data/Test_simulated/SP_object.Rdata")
load("~/Desktop/Slovenia data/Test_simulated/Pop_withNoFathers.Rdata")
##### ALPHASIMR FORMAT Real haplotypes #####
real_haplotypes <- pullSnpHaplo(PopMerged_noFathers)
real_map <- getGenMap(SP)
####################################################################################
# Get the Slovenian data haplotypes
#####################################################################################
setwd("~/Desktop/Slovenia data/PLINK/Slov/Slov_Raw")
Slov_map <- read.table("SlovRaw.map")
setwd("~/Desktop/Slovenia data/PLINK/Phasing/beagle4.0_phased/Slov")
Slov_vcf <- read.vcfR("Slov4.0_phased.vcf")
Slov_geno <- Slov_vcf@gt
Slov_mothers <- read.table("~/Desktop/Slovenia data/Attempt2/Slov/Not-phased/Colony/data/KnownMothers.txt")
Slov_sire <- read.table("~/Desktop/Slovenia data/PLINK/Slov/Slov AlphaAssign/output/Slov_AlphaAssignOutput.sires")
#ped order is always mum/dpc/workers
Slov_ids_all <- c(unique(Slov_mothers$V2), unique(Slov_sire$candidate), unique(Slov_mothers$V1))
Slov_ind_id_1 <- paste(ids_all, "1", sep = "_")
Slov_ind_id_2 <- paste(ids_all, "2", sep = "_")
#####################################################################################
#### Check the different phasing versions compared to the real and prephased data just to check similarity
#####################################################################################
#function for checking
check_haplotype <- function(real_haplotypes = NULL, results = NULL, pedigree = NULL) {
comparison_results <- data.frame()
cols <- colnames(results)
real_haplotypes_cols <- real_haplotypes[, cols, drop = FALSE]
offspring_ids <- pedigree$id
for (i in seq_along(offspring_ids)) {
offspring_id <- offspring_ids[i]
# Pull rows starting with offspring_id from the real haplotypes and results
real_haplo_maternal <- rownames(real_haplotypes_cols)[grep(paste0("^", offspring_id, "_1"), rownames(real_haplotypes_cols))]
real_haplo_maternal <- real_haplotypes_cols[real_haplo_maternal, , drop = FALSE]
real_haplo_paternal <- rownames(real_haplotypes_cols)[grep(paste0("^", offspring_id, "_2"), rownames(real_haplotypes_cols))]
real_haplo_paternal <- real_haplotypes_cols[real_haplo_paternal, , drop = FALSE]
results_maternal <- rownames(results)[grep(paste0("^", offspring_id, "_1"), rownames(results))]
results_maternal <- results[results_maternal, , drop = FALSE]
results_paternal <- rownames(results)[grep(paste0("^", offspring_id, "_2"), rownames(results))]
results_paternal <- results[results_paternal, , drop = FALSE]
# Compare haplotypes
maternal_comparison <- as.data.frame(matrix(NA, nrow = 1, ncol = 2))
rownames(maternal_comparison) <- rownames(results_maternal)
colnames(maternal_comparison) <- c("_1", "_2")
total_elements <- length(results_maternal)
maternal_comparison$`_1` <- (sum(results_maternal == real_haplo_maternal, na.rm = TRUE)/total_elements) * 100
maternal_comparison$`_2` <- (sum(results_maternal == real_haplo_paternal, na.rm = TRUE)/total_elements) * 100
paternal_comparison <- as.data.frame(matrix(NA, nrow = 1, ncol = 2))
rownames(paternal_comparison) <- rownames(results_paternal)
colnames(paternal_comparison) <- c("_1", "_2")
paternal_comparison$`_1` <- (sum(results_paternal == real_haplo_maternal)/total_elements) * 100
paternal_comparison$`_2` <- (sum(results_paternal == real_haplo_paternal)/total_elements) * 100
# Combine results for current offspring_id
current_comparison <- rbind(maternal_comparison, paternal_comparison)
# Append current_comparison to comparison_results
comparison_results <- rbind(comparison_results, current_comparison)
}
# Check conditions and print messages
comparison_results_maternal <- rownames(comparison_results)[grep(paste0("_1"), rownames(comparison_results))]
comparison_results_maternal <- comparison_results[comparison_results_maternal, , drop = FALSE]
comparison_results_paternal <- rownames(comparison_results)[grep(paste0("_2"), rownames(comparison_results))]
comparison_results_paternal <- comparison_results[comparison_results_paternal, , drop = FALSE]
maternal_correct <- all(comparison_results_maternal[, "_1"] > comparison_results_maternal[, "_2"])
paternal_correct <- all(comparison_results_paternal[, "_1"] < comparison_results_paternal[, "_2"])
print(mean(comparison_results_maternal$`_1`))
print(mean(comparison_results_maternal$`_2`))
print(mean(comparison_results_paternal$`_1`))
print(mean(comparison_results_paternal$`_2`))
return(comparison_results)
}
real_vc_nGE_phased <- check_haplotype(real_haplotypes = real_haplotypes, results = nGE4.0_phasedhaplotypes, pedigree = pedigree)
real_vc_GE_phased <- check_haplotype(real_haplotypes = real_haplotypes, results = GE4.0_phasedhaplotypes, pedigree = pedigree)
nGE_phased_vc_GE_phased <- check_haplotype(real_haplotypes = nGE4.0_phasedhaplotypes, results = GE4.0_phasedhaplotypes, pedigree = pedigree)
real_vs_real <- check_haplotype(real_haplotypes = real_haplotypes, results = real_haplotypes, pedigree = pedigree)
#####################################################################################
#### Haplotype parent-of-origin assignments
#Route 1: use pedigree reconstruction and use both dam and sire pedigree ID to assign haplotype parental origins
#####################################################################################
#Need to make a flipping function first - haplotype 1 and 2 are not assigned parents
#### using TRUE/FALSE to determine parent ##############################
#functions for the flipping functions
weighing_haplotypes <- function(off_hap, par_geno, method = NULL, result = NULL, j){
# Description of the parameters:
# off_hap: Vector of offspring haplotypes.
# par_geno: Vector of parent genotypes.
# method: The method used for scoring; options include mean, weighted_mean, etc.
# j: Chromosome identifier.
# Calculate the logical vector
logical_vec <- (off_hap - par_geno) %in% c(-2, 1)
# Run length encoding
rle_result <- rle(logical_vec)
# Separate the runs of FALSE and TRUE
false_runs <- rle_result$lengths[rle_result$values == FALSE]
true_runs <- rle_result$lengths[rle_result$values == TRUE]
# Define weights as the lengths of the runs
false_weights <- false_runs
true_weights <- true_runs
# Calculate power parameter p as the mean of run lengths if not empty
p <- if(length(false_runs) > 0) {
mean(false_runs) # just make it the mean for now
} else {
1 # default for p if there are no false_runs
}
# Handle scoring methods
if (method == "mean") {
false_score <- mean(false_runs)
true_score <- mean(true_runs)
overall_score <- false_score - true_score
} else if (method == "sum"){ # Total Sum
false_score <- sum(false_runs)
true_score <- sum(true_runs)
overall_score <- false_score - true_score
} else if (method == "quadratic") { # Quadratic Mean
false_score <- sqrt(sum(false_runs^2) / length(false_runs)) # High = more mismatches & less likely to be parent
true_score <- sqrt(sum(true_runs^2) / length(false_runs)) # High = longer runs of matches & more likely to be parent
overall_score <- false_score - true_score # High = more mismatches & less likely to be parent
} else if (method == "logarithmic") { # Logarithmic Mean
false_score <- sum(log(false_runs + 1)) / length(false_runs) # High = more mismatches & less likely to be parent
true_score <- sum(log(true_runs + 1)) / length(false_runs) # High = longer runs of matches & more likely to be parent
overall_score <- false_score - true_score # High = more mismatches & less likely to be parent
} else if (method == "geometric") { # Geometric Mean
false_score <- prod(false_runs)^(1/length(false_runs)) # High = more mismatches & less likely to be parent
true_score <- prod(true_runs)^(1/length(true_runs)) # High = longer runs of matches & more likely to be parent
overall_score <- false_score - true_score # High = more mismatches & less likely to be parent
} else if (method == "composite"){ # Composite Score
false_score <- (sum(false_runs)^2) / length(false_runs) # High = more mismatches & less likely to be parent
true_score <- (sum(true_runs)^2) / length(true_runs) # High = longer runs of matches & more likely to be parent
overall_score <- false_score - true_score # High = more mismatches & less likely to be parent
} else if (method == "exponential"){ # Exponential Mean
false_score <- sum(2^false_runs) / length(false_runs) # High = more mismatches & less likely to be parent
true_score <- sum(2^true_runs) / length(false_runs) # High = longer runs of matches & more likely to be parent
overall_score <- false_score - true_score # High = more mismatches & less likely to be parent
} else if (method == "harmonic"){ # Harmonic Mean
false_score <- length(false_runs) / sum(1 / false_runs) # High = more mismatches & less likely to be parent
true_score <- length(true_runs) / sum(1 / true_runs) # High = longer runs of matches & more likely to be parent
overall_score <- false_score - true_score # High = more mismatches & less likely to be parent
} else if (method == "power_mean" && !is.null(p)){ # Power Mean
false_score <- (sum(false_runs^p) / length(false_runs))^(1/p) # High = more mismatches & less likely to be parent
true_score <- (sum(true_runs^p) / length(true_runs))^(1/p) # High = longer runs of matches & more likely to be parent
overall_score <- false_score - true_score # High = more mismatches & less likely to be parent
} else {
stop("Invalid method specified")
}
weighed_haplotypes <- data.frame(
false_score = false_score,
true_score = true_score,
overall_score = overall_score,
method = method,
chr = j
)
return(weighed_haplotypes)
}
#method options : c("mean","sum","quadratic","logarithmic" ,"geometric","composite", "exponential", "harmonic","power_mean")
check_dist <- function(off_hap, par_geno, j){
logical_vec <- (off_hap - par_geno) %in% c(-2, 1)
# Run length encoding
rle_result <- rle(logical_vec)
# Separate the runs of FALSE and TRUE
false_runs <- rle_result$lengths[rle_result$values == FALSE]
true_runs <- rle_result$lengths[rle_result$values == TRUE]
if(length(false_runs) != 0){
false_runsdf <- data.frame(length = false_runs, type = "FALSE")
} else {
false_runsdf <- data.frame(length = NA, type = "FALSE")
}
if (length(true_runs) != 0) {
true_runsdf <- data.frame(length = true_runs, type = "TRUE")
} else {
true_runsdf <- data.frame(length = NA, type = "TRUE")
}
runs_df <- rbind(false_runsdf, true_runsdf)
runs_df$chr <- j
return(runs_df)
}
Route1_flipping <- function(Beagle_version = NULL, Data_type = NULL, pedigree = NULL, Real_haplotypes = NULL, method = NULL){
if (Real_haplotypes == TRUE & Data_type == "Real" & Beagle_version == "Non"){
real_haplotypes <- real_haplotypes
map <- real_map
real_results_methods <- list()
real_results_lengths <- list()
flipped_haplotypes <- list()
for (i in 1:nrow(pedigree)) {
offspring_id <- pedigree$id[i]
mother_id <- pedigree$mother[i]
dpc_id <- pedigree$dpc[i]
offspring_row <- rownames(real_haplotypes)[sapply(rownames(real_haplotypes), function(x) strsplit(x,'_')[[1]][1]) %in% offspring_id]
offspring_haplotypes_i <- real_haplotypes[offspring_row,]
Offspring_Hap1 <- t(as.data.frame(offspring_haplotypes_i[1,]))
Offspring_Hap2 <- t(as.data.frame(offspring_haplotypes_i[2,]))
mother_row <- rownames(real_haplotypes)[sapply(rownames(real_haplotypes), function(x) strsplit(x,'_')[[1]][1]) %in% mother_id]
mother_haplotypes_i <- real_haplotypes[mother_row,]
Maternal_Hap1 <- t(as.data.frame(mother_haplotypes_i[1,]))
rownames(Maternal_Hap1) <- mother_id
Maternal_Hap2 <- t(as.data.frame(mother_haplotypes_i[2,]))
rownames(Maternal_Hap2) <- mother_id
Maternal_Geno <- matrix(data = Maternal_Hap1 + Maternal_Hap2, nrow = 1)
colnames(Maternal_Geno) <- colnames(Maternal_Hap1)
rownames(Maternal_Geno) <- mother_id
dpc_row <- rownames(real_haplotypes)[sapply(rownames(real_haplotypes), function(x) strsplit(x,'_')[[1]][1]) %in% dpc_id]
dpc_haplotypes_i <- real_haplotypes[dpc_row,]
Dpc_Hap1 <- t(as.data.frame(dpc_haplotypes_i[1,]))
rownames(Dpc_Hap1) <- dpc_id
Dpc_Hap2 <- t(as.data.frame(dpc_haplotypes_i[2,]))
rownames(Dpc_Hap2) <- dpc_id
Dpc_Geno <- matrix(data = Dpc_Hap1 + Dpc_Hap2, nrow = 1)
colnames(Dpc_Geno) <- colnames(Dpc_Hap1)
rownames(Dpc_Geno) <- dpc_id
method_results <- list()
length_results <- list()
pat_results <- list()
mat_results <- list()
for (j in unique(map[, 2])) {
print(x = c(i,j))
Chr_map <- map[map$chr == j, ]
Chr_markerNames <- Chr_map[, 1]
Offspring_Hap1_chr <- Offspring_Hap1[, colnames(Offspring_Hap1) %in% Chr_markerNames, drop = FALSE]
Offspring_Hap2_chr <- Offspring_Hap2[, colnames(Offspring_Hap2) %in% Chr_markerNames, drop = FALSE]
Maternal_Geno_chr <- Maternal_Geno[, colnames(Maternal_Geno) %in% Chr_markerNames, drop = FALSE]
Dpc_Geno_chr <- Dpc_Geno[, colnames(Dpc_Geno) %in% Chr_markerNames, drop = FALSE]
scores_h1_p <- weighing_haplotypes(Offspring_Hap1_chr, Dpc_Geno_chr, method = method, j =j)
scores_h1_m <- weighing_haplotypes(Offspring_Hap1_chr, Maternal_Geno_chr, method = method, j =j)
scores_h2_p <- weighing_haplotypes(Offspring_Hap2_chr, Dpc_Geno_chr, method = method, j =j)
scores_h2_m <- weighing_haplotypes(Offspring_Hap2_chr, Maternal_Geno_chr, method = method, j =j)
# Combine scores for this method
scores <- rbind(scores_h1_p, scores_h1_m, scores_h2_p, scores_h2_m)
scores$Parent <- c("h1_p", "h1_m", "h2_p", "h2_m")
#do check
score_1 <- scores_h1_p$false_score
score_2 <- scores_h1_m$false_score
score_3 <- scores_h2_p$false_score
score_4 <- scores_h2_m$false_score
abs_diff <- abs(c(score_1, score_2, score_3, score_4))
max_diff <- max(abs_diff)
if (max_diff == abs(score_1) & max_diff != abs(score_2) & max_diff != abs(score_3) & max_diff != abs(score_4)) {
message("Hap1 is paternal - assume Hap2 is maternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_2) & max_diff != abs(score_1) & max_diff != abs(score_3) & max_diff != abs(score_4)) {
message("Hap1 is maternal - assume Hap2 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_3) & max_diff != abs(score_2) & max_diff != abs(score_1) & max_diff != abs(score_4)) {
message("Hap2 is paternal - assume Hap1 is maternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_4) & max_diff != abs(score_2) & max_diff != abs(score_1) & max_diff != abs(score_3)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_4) & max_diff == abs(score_3) & abs(score_1) > abs(score_2)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_4) & max_diff == abs(score_3) & abs(score_1) < abs(score_2)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_1) & max_diff == abs(score_2) & abs(score_3) < abs(score_4)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_1) & max_diff == abs(score_2) & abs(score_3) > abs(score_4)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_1) & max_diff == abs(score_2) & max_diff == abs(score_3) & max_diff == abs(score_4)) {
stop("All values are equal")
}
if (max_diff == abs(score_1) & max_diff == abs(score_4) & max_diff != abs(score_2) & max_diff != abs(score_3)) {
message("Hap1 is paternal and Hap2 is maternal - both are max")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_2) & max_diff == abs(score_3) & max_diff != abs(score_1) & max_diff != abs(score_4)) {
message("Hap1 is maternal and Hap2 is paternal - both are max")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
merged_haps <- rbind(Offspring_Hap1_chr, Offspring_Hap2_chr)
identified_dpc_haplo <- grep("_paternal$", rownames(merged_haps), value = TRUE)
identified_maternal_haplo <- grep("_maternal$", rownames(merged_haps), value = TRUE)
dpc_assigned <- merged_haps[identified_dpc_haplo, , drop = FALSE]
maternal_assigned <- merged_haps[identified_maternal_haplo, , drop = FALSE]
pat_results[[j]] <- as.data.frame(dpc_assigned)
mat_results[[j]] <- as.data.frame(maternal_assigned)
# Combine all scores into one data frame
testing_methods <- do.call(rbind, scores)
lengths_h1_p <- check_dist(Offspring_Hap1_chr, Dpc_Geno_chr, j =j)
lengths_h1_p$Parent <- rep("h1_p")
lengths_h1_m <- check_dist(Offspring_Hap1_chr, Maternal_Geno_chr, j =j)
lengths_h1_m$Parent <- rep("h1_m")
lengths_h2_p <- check_dist(Offspring_Hap2_chr, Dpc_Geno_chr, j =j)
lengths_h2_p$Parent <- rep("h2_p")
lengths_h2_m <- check_dist(Offspring_Hap2_chr, Maternal_Geno_chr, j =j)
lengths_h2_m$Parent <- rep("h2_m")
lengths_all <- rbind(lengths_h1_p, lengths_h1_m, lengths_h2_p, lengths_h2_m)
method_results[[j]] <- as.data.frame(testing_methods)
length_results[[j]] <- as.data.frame(lengths_all)
}
real_results_methods[[i]] <- do.call(rbind,method_results)
real_results_lengths[[i]] <- do.call(rbind, length_results)
pat_results <- do.call(cbind, pat_results)
mat_results <- do.call(cbind, mat_results)
results <- rbind(mat_results, pat_results)
flipped_haplotypes[[i]] <- as.data.frame(results)
}
real_results_methods_all <- do.call(rbind, real_results_methods)
real_results_lengths_all <- do.call(rbind, real_results_lengths)
real_results_flipped <- do.call(rbind, flipped_haplotypes)
results <- list(real_results_methods_all = real_results_methods_all, real_results_lengths_all = real_results_lengths_all,
real_results_flipped = real_results_flipped)
}
if (Real_haplotypes == FALSE){
phased_results_methods <- list()
phased_results_lengths <- list()
flipped_haplotypes <- list()
if(Data_type == "Pre_nGE_nQC" & Beagle_version == "Non"){
haplotypes <- nGE_nQC_nPhased_haplotypes
map <- nGE_nQC_nPhased_map
}else if (Data_type == "Pre_GE_nQC" & Beagle_version == "Non"){
haplotypes <- GE4.0
map <- GE_nQC_nPhased_map
} else if (Data_type == "Phased_nGE" & Beagle_version == "4.0"){
haplotypes <- nGE4.0_phasedhaplotypes
map <- nGE_phased_map
} else if (Data_type == "Phased_GE" & Beagle_version == "4.0"){
haplotypes <- GE4.0_phasedhaplotypes
map <- GE_phased_map
} else if (Data_type == "Phased_nGE" & Beagle_version == "4.1"){
haplotypes <- nGE4.1_phasedhaplotypes
map <- nGE_4.1_map
} else if (Data_type == "Phased_GE" & Beagle_version == "4.1"){
haplotypes <- GE4.1_phasedhaplotypes
map <- GE_4.1_map
} else if (Data_type == "Phased_GE" & Beagle_version == "Slov4.0"){
haplotypes <- Slov4.0_phasedhaplotype
map <- Slov_map
} else (stop("Arguments not met"))
for (i in 1:nrow(pedigree)) {
offspring_id <- pedigree$id[i]
mother_id <- pedigree$mother[i]
dpc_id <- pedigree$dpc[i]
offspring_row <- rownames(haplotypes)[sapply(rownames(haplotypes), function(x) strsplit(x, '_')[[1]][1]) %in% offspring_id]
offspring_haplotypes_i <- haplotypes[offspring_row, ]
Offspring_Hap1 <- as.data.frame(offspring_haplotypes_i[1, , drop = FALSE])
Offspring_Hap2 <- as.data.frame(offspring_haplotypes_i[2, , drop = FALSE])
mother_row <- rownames(haplotypes)[sapply(rownames(haplotypes), function(x) strsplit(x, '_')[[1]][1]) %in% mother_id]
mother_haplotypes_i <- haplotypes[mother_row, ]
Maternal_Hap1 <- as.data.frame(mother_haplotypes_i[1, , drop = FALSE])
rownames(Maternal_Hap1) <- mother_id
Maternal_Hap2 <- as.data.frame(mother_haplotypes_i[2, , drop = FALSE])
rownames(Maternal_Hap2) <- mother_id
Maternal_Geno <- matrix(data = Maternal_Hap1 + Maternal_Hap2, nrow = 1)
colnames(Maternal_Geno) <- colnames(Maternal_Hap1)
rownames(Maternal_Geno) <- mother_id
dpc_row <- rownames(haplotypes)[sapply(rownames(haplotypes), function(x) strsplit(x, '_')[[1]][1]) %in% dpc_id]
dpc_haplotypes_i <- haplotypes[dpc_row, ]
Dpc_Hap1 <- as.data.frame(dpc_haplotypes_i[1, , drop = FALSE])
rownames(Dpc_Hap1) <- dpc_id
Dpc_Hap2 <- as.data.frame(dpc_haplotypes_i[2, , drop = FALSE])
rownames(Dpc_Hap2) <- dpc_id
Dpc_Geno <- matrix(data = Dpc_Hap1 + Dpc_Hap2, nrow = 1)
colnames(Dpc_Geno) <- colnames(Dpc_Hap1)
rownames(Dpc_Geno) <- dpc_id
method_results <- list()
length_results <- list()
pat_results <- list()
mat_results <- list()
for (j in unique(map[, 1])) {
print(x = c(i,j))
Chr_map <- map[map$V1 == j, ]
Chr_markerNames <- Chr_map[, 2]
Offspring_Hap1_chr <- Offspring_Hap1[, colnames(Offspring_Hap1) %in% Chr_markerNames, drop = FALSE]
Offspring_Hap2_chr <- Offspring_Hap2[, colnames(Offspring_Hap2) %in% Chr_markerNames, drop = FALSE]
Maternal_Geno_chr <- Maternal_Geno[, colnames(Maternal_Geno) %in% Chr_markerNames, drop = FALSE]
Dpc_Geno_chr <- Dpc_Geno[, colnames(Dpc_Geno) %in% Chr_markerNames, drop = FALSE]
scores_h1_p <- weighing_haplotypes(Offspring_Hap1_chr, Dpc_Geno_chr, method = method, j =j)
scores_h1_m <- weighing_haplotypes(Offspring_Hap1_chr, Maternal_Geno_chr, method = method, j =j)
scores_h2_p <- weighing_haplotypes(Offspring_Hap2_chr, Dpc_Geno_chr, method = method, j =j)
scores_h2_m <- weighing_haplotypes(Offspring_Hap2_chr, Maternal_Geno_chr, method = method, j =j)
# Combine scores for this method
scores <- rbind(scores_h1_p, scores_h1_m, scores_h2_p, scores_h2_m)
scores$Parent <- c("h1_p", "h1_m", "h2_p", "h2_m")
#do check
score_1 <- scores_h1_p$false_score
score_2 <- scores_h1_m$false_score
score_3 <- scores_h2_p$false_score
score_4 <- scores_h2_m$false_score
if (is.nan(score_1)) score_1 <- 0
if (is.nan(score_2)) score_2 <- 0
if (is.nan(score_3)) score_3 <- 0
if (is.nan(score_4)) score_4 <- 0
abs_diff <- abs(c(score_1, score_2, score_3, score_4))
max_diff <- max(abs_diff, na.rm = TRUE)
# if (max_diff == abs(score_4) | max_diff == abs(score_1)) {
# print(c(i,j))
# print(c(score_1, score_2, score_3, score_4))
# stop("Something is funky here")
#
# }
#Only 1 is the absolute clear parent
if (max_diff == abs(score_1) & max_diff != abs(score_2) & max_diff != abs(score_3) & max_diff != abs(score_4)) {
message("Hap1 is paternal - assume Hap2 is maternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_2) & max_diff != abs(score_1) & max_diff != abs(score_3) & max_diff != abs(score_4)) {
message("Hap1 is maternal - assume Hap2 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_3) & max_diff != abs(score_2) & max_diff != abs(score_1) & max_diff != abs(score_4)) {
message("Hap2 is paternal - assume Hap1 is maternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_4) & max_diff != abs(score_2) & max_diff != abs(score_1) & max_diff != abs(score_3)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
#Two on the same haplotype have equally high scores, use the other haplotype to work it out
if (max_diff == abs(score_4) & max_diff == abs(score_3) & abs(score_1) > abs(score_2)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_4) & max_diff == abs(score_3) & abs(score_1) < abs(score_2)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_1) & max_diff == abs(score_2) & abs(score_3) < abs(score_4)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_1) & max_diff == abs(score_2) & abs(score_3) > abs(score_4)) {
message("Hap2 is maternal - assume Hap1 is paternal")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_1) & max_diff == abs(score_2) & max_diff == abs(score_3) & max_diff == abs(score_4)) {
stop("All values are equal")
}
if (max_diff == abs(score_1) & max_diff == abs(score_4) & max_diff != abs(score_2) & max_diff != abs(score_3)) {
message("Hap1 is paternal and Hap2 is maternal - both are max")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, paternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, maternal = TRUE, offspring_id = offspring_id)
}
if (max_diff == abs(score_2) & max_diff == abs(score_3) & max_diff != abs(score_1) & max_diff != abs(score_4)) {
message("Hap1 is maternal and Hap2 is paternal - both are max")
Offspring_Hap1_chr <- assign_parent_haplo(Offspring_Hap1_chr, maternal = TRUE, offspring_id = offspring_id)
Offspring_Hap2_chr <- assign_parent_haplo(Offspring_Hap2_chr, paternal = TRUE, offspring_id = offspring_id)
}
merged_haps <- rbind(Offspring_Hap1_chr, Offspring_Hap2_chr)
identified_dpc_haplo <- grep("_paternal$", rownames(merged_haps), value = TRUE)
identified_maternal_haplo <- grep("_maternal$", rownames(merged_haps), value = TRUE)
dpc_assigned <- merged_haps[identified_dpc_haplo, , drop = FALSE]
maternal_assigned <- merged_haps[identified_maternal_haplo, , drop = FALSE]
pat_results[[j]] <- as.data.frame(dpc_assigned)
mat_results[[j]] <- as.data.frame(maternal_assigned)
# Combine all scores into one data frame
testing_methods <- do.call(rbind, scores)
lengths_h1_p <- check_dist(Offspring_Hap1_chr, Dpc_Geno_chr, j =j)
lengths_h1_p$Parent <- rep("h1_p")
lengths_h1_m <- check_dist(Offspring_Hap1_chr, Maternal_Geno_chr, j =j)
lengths_h1_m$Parent <- rep("h1_m")
lengths_h2_p <- check_dist(Offspring_Hap2_chr, Dpc_Geno_chr, j =j)
lengths_h2_p$Parent <- rep("h2_p")
lengths_h2_m <- check_dist(Offspring_Hap2_chr, Maternal_Geno_chr, j =j)
lengths_h2_m$Parent <- rep("h2_m")
lengths_all <- rbind(lengths_h1_p, lengths_h1_m, lengths_h2_p, lengths_h2_m)
method_results[[j]] <- as.data.frame(testing_methods)
length_results[[j]] <- as.data.frame(lengths_all)
}
phased_results_methods[[i]] <- do.call(rbind,method_results)
phased_results_lengths[[i]] <- do.call(rbind, length_results)
pat_results <- do.call(cbind, pat_results)
mat_results <- do.call(cbind, mat_results)
results <- rbind(mat_results, pat_results)
flipped_haplotypes[[i]] <- as.data.frame(results)
}
results_methods_all <- do.call(rbind, phased_results_methods)
results_lengths_all <- do.call(rbind, phased_results_lengths)
results_flipped <- do.call(rbind, flipped_haplotypes)
results <- list(results_methods_all = results_methods_all, results_lengths_all = results_lengths_all,
results_flipped = results_flipped)
}
return(results)
}
Route1_Real_FLIP <- Route1_flipping(Beagle_version = "Non", Real_haplotypes = TRUE, Data_type = "Real", pedigree = pedigree, method = "power_mean")
Route1_GE_phased_FLIP <- Route1_flipping(Beagle_version = "4.0", Real_haplotypes = FALSE, Data_type = "Phased_GE", pedigree = pedigree, method = "power_mean")
Route1_nGE_phased_FLIP <- Route1_flipping(Beagle_version = "4.0", Real_haplotypes = FALSE, Data_type = "Phased_nGE", pedigree = pedigree, method = "power_mean")
Route1_Slov_FLIP <- Route1_flipping(Beagle_version = "Slov4.0", Real_haplotypes = FALSE, Data_type = "Phased_GE", pedigree = Workers_with_fathers_id, method = "power_mean")
#function for checking
check_haplotype_postFlip <- function(complete_haplotypes = NULL, results = NULL, pedigree = NULL) {
comparison_results <- data.frame()
cols <- colnames(results)
real_haplotypes_cols <- complete_haplotypes[, cols, drop = FALSE]
offspring_ids <- pedigree$id
for (i in seq_along(offspring_ids)) {
offspring_id <- offspring_ids[i]