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4_IBD_Utilitty_Functions.R
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4_IBD_Utilitty_Functions.R
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#
# dCor_Profile() is the key function to calculate the dCor association profile across the region.
# It takes the following arguments as input:
#
# 1. cc_sample: A dataframe with two columns: SeqID and Status
# First column, SeqID, shows the labels of the sequences.
# Second column, Status, represents the case/control status of each sequence. 0 = control, 1 = case
#
dCor_Profile = function(cc_sample, distance_matrix_list){
# Calculate the phenotypic distance matrix by calling pheno_dist_matrix_calc() function.
phenotye_dist_matrix = pheno_dist_matrix_calc(cc_sample, prob = 0.05)
# Calculate the dCor profile along the genome.
n = length(distance_matrix_list)
dCor_Stat = numeric( length = n )
for(i in 1:n){
partition_dist = distance_matrix_list[[i]][rownames(phenotye_dist_matrix),colnames(phenotye_dist_matrix)]
dCor_Stat[i] = perfectphyloR::dCorTest(Dx = partition_dist, Dy = phenotye_dist_matrix, nperm = 0)$Stat
}
# Returns dCor_Stat, a numeric vector, recording the dCor value across the region.
return(dCor_Stat)
}
#
# pheno_dist_matrix_calc() calculates the phenotypic distance matrix. It takes 2 inputs:
#
# 1. A numeric vector y of length n, representing the phenotype of each sequence.
# In the case of dichomotous trait it is coded as 1 = Case and 0 = Control
#
# 2. The probability of disease in the population as prob. This must be a number
# between 0 and 1.
#
# It returns a nxn phenotypic distance matrix according to calculation explained in:
# Burkett et al. 2014.(Link: https://www.karger.com/Article/Abstract/363443 )
#
pheno_dist_matrix_calc = function(cc_sample, prob){
seq_names = cc_sample[,1]
y = cc_sample[,2]
d = matrix(NA, nrow = length(y), ncol = length(y) )
for( i in 1:length(y) ){
for( j in 1:length(y) ){
s_ij = (y[i] - prob) * (y[j] - prob)
d[i,j] = 1 - s_ij
}
}
diag(d) = 0
rownames(d) = seq_names
colnames(d) = seq_names
return(d)
}
#
# dCorN_permute() runs the permutation test for Naive dCor (dCorN).
#
# The arguments of this function are as follows:
#
# 1. nperm = The desired number of permutation.
#
# 2. sample_data = the sample object generated by Sample_from_population() function.
#
# 3. distance_matrix_list = A list of distance matrices.
#
dCorN_permute = function(nperm, sample_data, distance_matrix_list){
# dCorN_permutation is a matrix. Each rows of this matrix represents the dCor profile for each permutation.
# We add one extra row to fill it in with dCorN observed statistic later.
dCorN_permutation = matrix(NA, nrow = nperm + 1, ncol = length(sample_data$Posn$SNV_Position) )
# ccLabel is the original case/control labeling of the individuals.
ccLabel = sample_data$Genos$ccStatus
# Load the permutation indices
load("permute_indx.RData")
# Running the permutation in parallel
library(foreach)
library(doParallel)
# To run parallel on compute canada:
# (Ref: https://docs.computecanada.ca/wiki/R)
#
# Create an array from the NODESLIST environnement variable
nodeslist = unlist(strsplit(Sys.getenv("NODESLIST"), split=" "))
# Create the cluster with the nodes name. One process per count of node name.
# nodeslist = node1 node1 node2 node2, means we are starting 2 processes on node1, likewise on node2.
cl = makeCluster(nodeslist, type = "PSOCK")
registerDoParallel(cl)
# To run parallel on your own PC/Laptop:
# cl = makeCluster(detectCores() - 1)
# registerDoParallel(cl)
res = foreach(i = 1:nperm, .export = c("dCor_Profile","pheno_dist_matrix_calc") ) %dopar%{
SeqID = c(sample_data$CaseHapID, sample_data$ControlHapID)
Status = unlist( lapply( ccLabel[ permute_indx[i,] ], FUN = function(x){rep(x,2)} ) )
pCCLabel = data.frame(SeqID = SeqID, Status = Status)
dCor_Profile(cc_sample = pCCLabel, distance_matrix_list)
}
stopCluster(cl)
for( i in 1:length(res) ){
dCorN_permutation[i,] = res[[i]]
}
colnames(dCorN_permutation) = sample_data$Posn$SNV_Names
return( dCorN_permutation )
}
# Case_reclassify() function takes the sample_data as the input.
# We can use this function to reclassify case sequences based
# on their true carrier status.
# The output of this function is a vector indicating the
# case control status of sequences after reclassification.
Case_reclassify = function(sample_data){
cSNV = sample_data$poly_cSNV
case_sequences = sample_data$CaseHapID
case_seq_carrier_status = colSums( sample_data$Haps$sample_haps[cSNV,case_sequences] )
case_seq_carrier_status[case_seq_carrier_status >= 1] = 1
names(case_seq_carrier_status) = NULL
result = c(case_seq_carrier_status, rep(0, length(sample_data$ControlHapID)) )
names(result) = c(sample_data$CaseHapID ,sample_data$ControlHapID)
return(CarrierStatus = result)
}
#
# Mantel_Profile() is the key function to calculate the Mantel association profile across the region.
# It takes the following arguments as input:
#
# 1. cc_sample: A dataframe with two columns: SeqID and Status
# First column, SeqID, shows the labels of the sequences.
# Second column, Status, represents the case/control status of each sequence. 0 = control, 1 = case
#
# 2. distance_matrix_list: a list of distance matrices calculated based on partitions
#
Mantel_Profile = function(cc_sample, distance_matrix_list){
# Calculate the phenotypic distance matrix by calling pheno_dist_matrix_calc() function.
phenotye_dist_matrix = pheno_dist_matrix_calc(cc_sample, prob = 0.05)
# Calculate the dCor profile along the genome.
n = length(distance_matrix_list)
Mantel_Stat = numeric( length = n )
for(i in 1:n){
partition_dist = distance_matrix_list[[i]][rownames(phenotye_dist_matrix),colnames(phenotye_dist_matrix)]
Mantel_Stat[i] = perfectphyloR::MantelTest(Dx = partition_dist, Dy = phenotye_dist_matrix, nperm = 0)$Stat
}
# Returns Mantel_Stat, a numeric vector, recording the Mantel value across the region.
return(Mantel_Stat)
}
#
# Mantel_permute() runs the permutation test for Mantel.
#
# The arguments of this function are as follows:
#
# 1. nperm = The desired number of permutation.
#
# 2. sample_data = the sample object generated by Sample_from_population() function.
#
# 3. distance_matrix_list = A list of distance matrices.
#
Mantel_permute = function(nperm, sample_data, distance_matrix_list){
# Mantel_permutation is a matrix. Each rows of this matrix represents the Mantel profile for each permutation.
# We add one extra row to fill it in with Mantel observed statistic later.
Mantel_permutation = matrix(NA, nrow = nperm + 1, ncol = length(sample_data$Posn$SNV_Position) )
# ccLabel is the original case/control labeling of the individuals.
ccLabel = sample_data$Genos$ccStatus
# Load the permutation indices
load("permute_indx.RData")
# Running the permutation in parallel
library(foreach)
library(doParallel)
# To run parallel on compute canada:
# (Ref: https://docs.computecanada.ca/wiki/R)
#
# Create an array from the NODESLIST environnement variable
nodeslist = unlist(strsplit(Sys.getenv("NODESLIST"), split=" "))
# Create the cluster with the nodes name. One process per count of node name.
# nodeslist = node1 node1 node2 node2, means we are starting 2 processes on node1, likewise on node2.
cl = makeCluster(nodeslist, type = "PSOCK")
registerDoParallel(cl)
# To run parallel on your own PC/Laptop:
# cl = makeCluster(detectCores() - 1)
# registerDoParallel(cl)
res = foreach(i = 1:nperm, .export = c("Mantel_Profile","pheno_dist_matrix_calc") ) %dopar%{
SeqID = c(sample_data$CaseHapID, sample_data$ControlHapID)
Status = unlist( lapply( ccLabel[ permute_indx[i,] ], FUN = function(x){rep(x,2)} ) )
pCCLabel = data.frame(SeqID = SeqID, Status = Status)
Mantel_Profile(cc_sample = pCCLabel, distance_matrix_list)
}
stopCluster(cl)
for( i in 1:length(res) ){
Mantel_permutation[i,] = res[[i]]
}
colnames(Mantel_permutation) = sample_data$Posn$SNV_Names
return( Mantel_permutation )
}