diff --git a/noisyplotype.py b/noisyplotype.py
index 1388a0f6f3f5758b04ec09a87fc2b893793c2caa..52ac074e1f17e8325e2bb9654eea2e2a56a01682 100644
--- a/noisyplotype.py
+++ b/noisyplotype.py
@@ -1,27 +1,24 @@
import random
-import csv
import math
import numpy as np
#def generate_chr_for_one_ind (chromosome_size, marker_density, err_rate, mean_depth, max_depth, markers_positions, conversion_factor):
-def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor):
+def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor, errA, errB):
'''
Generate a chromosome for a given size, density of markers, and depth (follwing a gaussian distrib with a mean_depth and sd_depth).
Args:
- chromosome_size (int): The size of the chromosome.
- marker_density (float): The density of markers.
- err_rate (float): The error rate of the chromosome.
mean_depth (float): The mean depth of the chromosome.
- max_depth
- markers_positions
+ markers_positions (lst) : liste of marker positions (picked on a grid)
+ err_rate (float): The error rate of the chromosome.
Returns:
- list: The chromosome.
+ list: The chromosome with each position having a given genotype.
'''
segment = []
+ segment_error = []
# Iterate through the list of markers positions
for i in range(0, len(markers_positions)):
@@ -78,56 +75,99 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor):
# Initialize x and y
x = 0
y = 0
+ x_error = 0
+ y_error = 0
# If the depth of the current marker is 0, genotype is Missing Data
if site_depth == 0:
genotype = "./."
+ genotype_error = "./."
else :
# If the current marker is A and the previous marker is A, genotype if homozygote A (ref, 0/0)
if genotype1 == "A" and genotype2 == "A":
x = site_depth
y = 0
genotype = "0/0"
+ x_error = 0
+ y_error = 0
+ for j in range(0,site_depth):
+ rnd = random.random()
+ # If the random number is greater than errB, we switch increase the wronge allele depth
+ if rnd > errA:
+ x_error = x_error + 0
+ y_error = y_error + 1
+ else :
+ x_error = x_error + 1
+ y_error = y_error + 0
+ if x_error > 0:
+ genotype_error = "0/1"
+ else :
+ genotype_error = "0/0"
+
# If the current marker is B and the previous marker is B, genotype if homozygote B (alt, 1/1)
elif genotype1 == "B" and genotype2 == "B":
x = 1
y = site_depth
genotype = "1/1"
+ x_error = 0
+ y_error = 0
+ for j in range(0,site_depth):
+ rnd = random.random()
+ # If the random number is greater than errB, we switch increase the wronge allele depth
+ if rnd > errB:
+ x_error = x_error + 1
+ y_error = y_error + 0
+ else :
+ x_error = x_error + 0
+ y_error = y_error + 1
+ if x_error > 0:
+ genotype_error = "0/1"
+ else :
+ genotype_error = "1/1"
# If the current marker is neither A nor B, genotype if heterozygous H (0/1)
else:
# Generate a random number between 0 and the depth of the current marker
x = random.randint(0,site_depth)
y = site_depth - x
+ x_error = x
+ y_error = y
# If the depth of x of the current marker is 0, seen as homozygous
if x == 0:
genotype = "1/1"
+ genotype_error = "1/1"
# If the depth of y of the current marker is not 0, seen as homozygous
elif y == 0:
- genotype = "0/0"
+ genotype = "0/0"
+ genotype_error = "0/0"
# If the depth x and y of the current marker is not 0, seen as heterozygous
else :
- genotype = "0/1"
+ genotype = "0/1"
+ genotype_error = "0/1"
#print("reads",genotype1, genotype2)
#print(genotype)
# Append the genotype and the depth of the current marker to the segment list
finalGenotype = str(genotype) + ":" + str(site_depth) + ":" + str(x) + "," + str(y) + ":.:.:.:.:."
+ print(finalGenotype)
segment.append(finalGenotype)
+
+ finalGenotype_error = str(genotype_error) + ":" + str(site_depth) + ":" + str(x_error) + "," + str(y_error) + ":.:.:.:.:."
+ print(finalGenotype_error)
+ segment_error.append(finalGenotype_error)
return segment
-
# Generate a list of individuals
-def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor):
+def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor, errA, errB):
matrix = []
# Calculate the number of marker requiered
markers_nb = round(chromosome_size * marker_density)
# Generate a sorted list of markers positions
- # positions = sorted(random.sample(range(chromosome_size),size))
+ #markers_positions = sorted(random.sample(range(chromosome_size),size))
markers_positions = np.linspace(1, chromosome_size, markers_nb, dtype="int")
for i in range(nb_individuals):
print("individual " + str(i+1))
- matrix.append(generate_chr_for_one_ind(mean_depth, markers_positions, conversion_factor))
+ matrix.append(generate_chr_for_one_ind(mean_depth, markers_positions, conversion_factor, errA, errB))
return matrix, markers_positions
@@ -142,17 +182,19 @@ def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_
# return [count_a, count_b, count_h, 0.45 <= count_h <= 0.55 and 0.2 <= count_b <= 0.3 and 0.2 <= count_a <= 0.3]
# Set the parameters for the script
-nb_individuals = 3
-chromosome_size = 44000000
-cMsize = 180 ## Size of genetic map
+nb_individuals = 1
+chromosome_size = 1000
+cMsize = 5 ## Size of genetic map
conversion_factor = chromosome_size/cMsize ## Corresponds to a bpPercM conversion ! Needs to be fixed... Does not produce the correct division!
-#conversion_factor = 233000
-print(conversion_factor)
-marker_density = 255000/44000000
+#print(conversion_factor)
+marker_density = 0.006
mean_depth = 3
-max_depth = 10 #TODO
-err_rate = 0.02
-pop = generate_individuals(nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor)
+max_depth = 6 #TODO
+errA = 0.005
+errB = 0.005
+
+# #size = 200000
+pop = generate_individuals(nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor, errA, errB)
matrix = pop[0]
markers_positions = pop[1]
@@ -180,7 +222,7 @@ header = [
]
-with open('Newtest.vcf', 'w') as file:
+with open('ABtest.vcf', 'w') as file:
# Write the header of the file
file.writelines(header)