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Copy pathSCRaMbLE_simulation_chr_len_events_easy.py
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SCRaMbLE_simulation_chr_len_events_easy.py
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from SCRaMbLE_simulation_3 import force_SCRaMLE_lin_cir
from SCRaMbLE_simulation_3 import force_SCRaMLE_lin_cir_events
from comparison_sol import LoxP_unit_count_Dict_list
from comparison_sol import NG50_calculator
from comparison_sol import essential_ratio_calculator
from comparison_sol import half_life
from SCRaMbLE_simulation_chr_len_events_store2 import plot_chr_len
from SCRaMbLE_simulation_chr_len_events_store2 import plot_chr_len_median
from work_parallel_functions import sum_within_list
from work_parallel_functions import standard_deviation_from_points_parallel
#from Mapping_coverage_MM import calculate_LU_CN_percentage
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import random
import statistics
# SCRaMbLEs a synthetic chromosome and outputs chr_len, L_unique, LG50
def SCRaMbLE_SIM_length(syn_chr, events=15, simulations=100, essential=[], CEN=[], circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1]):
chr_len = [[] for _ in range(simulations)]
L_unique = [[] for _ in range(simulations)]
LG50 = [[] for _ in range(simulations)]
chr_len_SD = [0] # The first value is always 0
L_unique_SD = [0]
LG50_SD = [0]
# Percentiles or Quartiles.
chr_len_Q1 = [len(syn_chr)]
chr_len_Q2 = [len(syn_chr)] # Median
chr_len_Q3 = [len(syn_chr)]
L_unique_Q1 = [len(syn_chr)]
L_unique_Q2 = [len(syn_chr)]
L_unique_Q3 = [len(syn_chr)]
LG50_Q1, LG50_Q2, LG50_Q3 = [len(syn_chr)/2], [len(syn_chr)/2], [len(syn_chr)/2]
# Count the events type
Events = [[] for _ in range(simulations)]
# Calculate the ratio of essential LUs
essential_ratio = [[] for _ in range(simulations)]
for s in range(simulations):
print(s)
SCRaMbLEd = syn_chr[:]
for _ in range(events):
# Perform SCRaMbLE on the synthetic chromosome
#SCRaMbLEd = force_SCRaMLE_lin_cir(SCRaMbLEd, Number_events=1, essential=essential, circular=circular, mu=mu, sigma=sigma, CEN=CEN, force=force, probability=probability)
chr_events = force_SCRaMLE_lin_cir_events(SCRaMbLEd, Number_events=1, essential=essential, circular=circular, mu=mu, sigma=sigma, CEN=CEN, force=force, probability=probability, event_type=True)
#print(chr_events)
SCRaMbLEd = chr_events[0]
if chr_events[1] != []:
Events[s].append(chr_events[1][0])
else:
Events[s].append("NULL")
# Get some statistics about the SCRaMbLEd chromosome
NG50 = NG50_calculator(SCRaMbLEd)
chr_len[s].append(len(SCRaMbLEd))
L_unique[s].append(NG50[1])
LG50[s].append(NG50[3])
essential_ratio[s].append(essential_ratio_calculator(chromosome=SCRaMbLEd, essential=essential))
# Calculate the standard deviation
for s in range(events):
CHR_L = [sim_L[s] for sim_L in chr_len]
unique_L = [sim_L[s] for sim_L in L_unique]
LG50_L = [sim_L[s] for sim_L in LG50]
chr_len_SD.append(statistics.stdev(CHR_L))
L_unique_SD.append(statistics.stdev(unique_L))
LG50_SD.append(statistics.stdev(LG50_L))
chr_len_Q1.append(np.percentile(CHR_L, 25))
chr_len_Q2.append(np.percentile(CHR_L, 50))
chr_len_Q3.append(np.percentile(CHR_L, 75))
L_unique_Q1.append(np.percentile(unique_L, 25))
L_unique_Q2.append(np.percentile(unique_L, 50))
L_unique_Q3.append(np.percentile(unique_L, 75))
LG50_Q1.append(np.percentile(LG50_L, 25))
LG50_Q2.append(np.percentile(LG50_L, 50))
LG50_Q3.append(np.percentile(LG50_L, 75))
chr_len_Q1_Q2_Q3 = [chr_len_Q1, chr_len_Q2, chr_len_Q3]
L_unique_Q1_Q2_Q3 = [L_unique_Q1, L_unique_Q2, L_unique_Q3]
LG50_Q1_Q2_Q3 = [LG50_Q1, LG50_Q2, LG50_Q3]
print("chr_len =", len(chr_len), np.percentile(chr_len, 50))#, chr_len)
#print("L_unique =", L_unique)
#print("LG50 =", LG50)
print("chr_len_SD =", len(chr_len_SD), np.percentile(chr_len, 50))#, chr_len_SD)
#print("L_unique_SD =", L_unique_SD)
#print("LG50_SD =", LG50_SD)
#print("chr_len_Q1 =", chr_len_Q1)
print("chr_len_Q1_Q2_Q3 =", len(chr_len_Q1), "=", np.percentile(chr_len_Q1, 50), np.percentile(chr_len_Q2, 50), np.percentile(chr_len_Q3, 50))
#print("Events =", Events)
# How many chromosomes reached the length of zero?
last_chr_len = [sim[-1] for sim in chr_len]
last_chr_len_zero = last_chr_len.count(0)
print("last_chr_len_zero =", 100 * last_chr_len_zero / simulations, "%")
return chr_len, L_unique, LG50, chr_len_SD, L_unique_SD, LG50_SD, Events, essential_ratio, chr_len_Q1_Q2_Q3, L_unique_Q1_Q2_Q3, LG50_Q1_Q2_Q3
def plot_SCRaMbLE_chr_len(syn_chr, events=15, simulations=100, essential=[], CEN=[], circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1], file_name="", SD=False):
# SCRaMbLE the chromosome and generate chr_len, L_unique, LG50
S = SCRaMbLE_SIM_length(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=circular, mu=mu, sigma=sigma, force=force, probability=probability)
chr_len = S[0]
L_unique = S[1]
LG50 = S[2]
#chr_len_Q1, chr_len_Q2, chr_len_Q3 = S[6], S[7], S[8]
chr_len_Q1_Q2_Q3 = S[8]
L_unique_Q1_Q2_Q3 = S[9]
LG50_Q1_Q2_Q3 = S[10]
# Count SCRaMbLE events types
Events = S[6]
DELs = [0 for _ in range(events)]
INVs = [0 for _ in range(events)]
DUPs = [0 for _ in range(events)]
for Sim in Events:
for E in range(len(Sim)):
if Sim[E] == "DEL":
DELs[E] += 1
if Sim[E] == "INV":
INVs[E] += 1
if Sim[E] == "DUP":
DUPs[E] += 1
# Convert the number of SEs into percentages
DELs = [e/simulations for e in DELs]
INVs = [e/simulations for e in INVs]
DUPs = [e/simulations for e in DUPs]
# Calculate the ratio of essential LUs
essential_ratio = S[7]
if SD == True:
chr_len_SD = S[3]
L_unique_SD = S[4]
LG50_SD = S[5]
else:
chr_len_SD = []
L_unique_SD = []
LG50_SD = []
# List of time points
SCRaMbLEd_events = list(range(0, events+1, 1)) # +1 because we record also the initial value
# If you want to plot all the SCRaMbLE chromosomes
#plot_chr_len(SCRaMbLEd_events[1:], chr_len, L_unique=L_unique, LG50=LG50, num_essential=len(essential), simulations=simulations, file_name="")
# Put together all the simulations
chr_len_L = sum_within_list(chr_len)
L_unique_L = sum_within_list(L_unique)
LG50_L = sum_within_list(LG50)
essential_ratio_L = sum_within_list(essential_ratio)
# Add the first value
NG50 = NG50_calculator(syn_chr)
chr_len_L.insert(0, len(syn_chr)*simulations)
L_unique_L.insert(0, NG50[0]*simulations)
LG50_L.insert(0, NG50[3]*simulations)
#essential_ratio_L.insert(0, 0.5*simulations)
# Divide for the number of simulations. You can use also the function divide_by_sim in work_parallel_functions
chr_len_L = [x / simulations for x in chr_len_L]
L_unique_L = [x / simulations for x in L_unique_L]
LG50_L = [x / simulations for x in LG50_L]
essential_ratio_L = [x / simulations for x in essential_ratio_L]
print("chr_len_L =", chr_len_L)
print("chr_len_Q2 =", chr_len_Q1_Q2_Q3[1])
#print("L_unique_L =", L_unique_L)
#print("LG50_L =", LG50_L)
#print("DELs =", DELs)
#print("INVs =", INVs)
#print("DUPs =", DUPs)
# Plot the results
plot_chr_len(SCRaMbLEd_events, chr_len_L, L_unique=L_unique_L, LG50=LG50_L, num_essential=len(essential), simulations=simulations, circular=circular, probability=probability, file_name=file_name, SD_chr_len=chr_len_SD, SD_L_unique=L_unique_SD, SD_LG50=LG50_SD)
plot_chr_len_median(SCRaMbLEd_events, chr_len_Q1_Q2_Q3, L_unique=L_unique_Q1_Q2_Q3, LG50=LG50_Q1_Q2_Q3, num_essential=len(essential), simulations=simulations, circular=circular, probability=probability, file_name=file_name)
# plot the frequencies of the SCRaMbLE events types
plt.figure(figsize=(7, 3.5), dpi=300)
# Font size
SMALL_SIZE = 8
MEDIUM_SIZE = 9
BIGGER_SIZE = 10
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title
plt.rcParams['lines.linewidth'] = 0.65
#print(len(SCRaMbLEd_events), len(DELs))
plt.ylim(0, 1)
plt.plot(SCRaMbLEd_events[1:], DELs, label="DELs", linewidth=0.7)
plt.plot(SCRaMbLEd_events[1:], INVs, label="INVs", linewidth=0.7)
plt.plot(SCRaMbLEd_events[1:], DUPs, label="DUPs", linewidth=0.7)
plt.plot(SCRaMbLEd_events[1:], essential_ratio_L, label="essential ratio") # linewidth=0.65
plt.ylabel("Percentage of event type over " + str(simulations) + " simulations")
plt.xlabel("SCRaMbLEd events")
plt.title("Frequencies of SCRaMbLE event types", fontsize=9)
plt.legend(fontsize=8)
# These are some information to add to the saved files.
# Create a random seed to save the image
random_seed = str(random.random())[2:6]
if circular: # record if the chromosome is linear or circular
lin_cir = "c"
else:
lin_cir = "l"
probability_str = "" # record the probabilities of each event
for i in probability:
probability_str = probability_str + str(i)
file_name1 = "SCRaMbLE_chr_len/SCRaMbLE_events_types_" + lin_cir + "_events_" + str(SCRaMbLEd_events[-1]) + "_sim_" + str(simulations) + "_P" + probability_str + "_" + file_name + random_seed
plt.savefig(file_name1 + ".png", dpi=300, bbox_inches='tight')
plt.savefig(file_name1 + ".svg", format="svg", dpi=300, bbox_inches='tight')
plt.show()
plt.close()
return None
# I split the function plot_SCRaMbLE_chr_len in two pieces
def mean_SCRaMbLE_chr_len(syn_chr, events=15, simulations=100, essential=[], CEN=[], circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1], file_name="", num_essential=0):
if num_essential != 0:
num_essential = 10
essential = sorted(random.sample(syn_chr, k=num_essential))
# Generate the chr len
S = SCRaMbLE_SIM_length(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=circular, mu=mu, sigma=sigma, force=force, probability=probability)
chr_len = S[0]
L_unique = S[1]
LG50 = S[2]
# If you want to plot all the SCRaMbLE chromosomes
#plot_chr_len(SCRaMbLEd_events, chr_len, L_unique=L_unique, LG50=LG50, num_essential=len(essential), simulations=simulations, file_name="")
# Put together all the simulations
chr_len_L = sum_within_list(chr_len)
L_unique_L = sum_within_list(L_unique)
LG50_L = sum_within_list(LG50)
# Add the first value
NG50 = NG50_calculator(syn_chr)
chr_len_L.insert(0, len(syn_chr)*simulations)
L_unique_L.insert(0, NG50[0]*simulations)
LG50_L.insert(0, NG50[3]*simulations)
# Divide for the number of simulations. You can use also the function divide_by_sim in work_parallel_functions
chr_len_L = [x / simulations for x in chr_len_L]
L_unique_L = [x / simulations for x in L_unique_L]
LG50_L = [x / simulations for x in LG50_L]
#print("half_life chr_len_L =", half_life(chr_len_L))
#print("half_life L_unique_L =", half_life(L_unique_L, minus_constant=len(essential)))
#print("half_life LG50_L =", half_life(LG50_L))
return chr_len_L, L_unique_L, LG50_L
def plot_mean_SCRaMbLE_chr_len(syn_chr, events=15, simulations=100, essential=[], CEN=[], circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1], file_name=""):
A_result = mean_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=circular, mu=mu, sigma=sigma, force=force, probability=probability)
# List of time points
SCRaMbLEd_events = list(range(0, events + 1, 1)) # +1 because we record also the initial value
# Plot the results
plot_chr_len(SCRaMbLEd_events, chr_len=A_result[0], L_unique=A_result[1], LG50=A_result[2], num_essential=len(essential), simulations=simulations, circular=circular, probability=probability, file_name=file_name)
return None
# This function plots the chromosome length over a SCRaMbLE evolution using different initial chromosome length.
def chr_len_range_SCRaMbLE(events=15, simulations=100, essential=[], CEN=[], circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1], file_name=""):
#chr_len_range = list(range(25, 201, 25))
chr_len_range = list(range(25, 226, 50))
# chr_len_range = [25, 50, 75, 100, 125, 150, 175, 200]
# chr_len_range = [25, 75, 125, 175, 225]
print("chr_len_range =", chr_len_range)
chr_len_multi = []
L_unique_multi = []
LG50_multi = []
half_life_L_unique_L_multi = []
for i in chr_len_range:
print("Chr len =", i)
# Create the chromosomes
SYN_CHR = list(range(1, i, 1))
# Use this if you want to chose the essential LUs at random
num_essential = 10
essential = sorted(random.sample(SYN_CHR, k=num_essential))
A_result = mean_SCRaMbLE_chr_len(SYN_CHR, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=circular, mu=mu, sigma=sigma, force=force, probability=probability, file_name=file_name, num_essential=10)
chr_len_multi.append(A_result[0])
L_unique_multi.append(A_result[1])
LG50_multi.append(A_result[2])
print(i, "half_life chr_len_L =", half_life(A_result[0]))
print(i, "half_life L_unique_L =", half_life(A_result[1], minus_constant=num_essential))
# print(i, "half_life LG50_L =", half_life(A_result[2]))
half_life_L_unique_L_multi.append(half_life(A_result[1], minus_constant=num_essential))
print()
# List of time points
SCRaMbLEd_events = list(range(0, events + 1, 1)) # +1 because we record also the initial value
# plot the results
plt.figure(figsize=(7, 3.5), dpi=300)
# Font size
SMALL_SIZE = 8
MEDIUM_SIZE = 9
BIGGER_SIZE = 10
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title
plt.rcParams['lines.linewidth'] = 0.65
plt.ylabel("Chr length mean over " + str(simulations) + " simulations")
plt.xlabel("SCRaMbLEd events")
plt.title("Chr length over SCRaMbLE events", fontsize=9)
#plt.plot(0, 0, label="Chr len", color="tab:blue")
plt.plot(0, 0, label="No. unique LUs", color="C1")
# plt.plot(0,0, label="LG50", color="tab:green")
#Labels = ["Chr = 25", "Chr = 50", "Chr = 75", "Chr = 100", "Chr = 125", "Chr = 150", "Chr = 175", "Chr = 200"]
Labels = ["Chr len = 25 LUs", "Chr len = 75 LUs", "Chr len = 125 LUs", "Chr len = 175 LUs", "Chr len = 225 LUs"]
colours = ["C0", "C2", "C3", "C4", "C5"]
#Labels, colours = [], []
#for i in range(len(chr_len_range)):
# Labels.append("Chr len = " + str(chr_len_range[i]) + " LUs")
# colours.append("C" + str(i))
for i in range(len(chr_len_multi)):
plt.plot(SCRaMbLEd_events, chr_len_multi[i], color=colours[i], label=Labels[i])
# plt.errorbar(SCRaMbLEd_events, chr_len[i], yerr=SD_chr_len, elinewidth=0.1)
if L_unique_multi != [[]]:
plt.plot(SCRaMbLEd_events, L_unique_multi[i], color="C1") # color="cornflowerblue"
#if LG50 != [[]]:
# plt.plot(SCRaMbLEd_events, LG50[i], label="LG50", color="tab:green")
plt.hlines(num_essential, 0, SCRaMbLEd_events[-1], colors="red", linestyle=":", label="No. essential LUs")
plt.legend(fontsize=8)
#plt.yscale('log', base=2)
# Plot the half-life of non-essential LUs
for i in range(len(chr_len_range)):
print("half_life_L_unique_L_multi =", chr_len_range[i], half_life_L_unique_L_multi[i])
# plt.hlines(chr_len_range[i]/2, 0, SCRaMbLEd_events[-1], colors="grey", linestyle=":", alpha=0.5)
# plt.hlines(chr_len_range[i]/4, 0, SCRaMbLEd_events[-1], colors="blue", linestyle=":", alpha=0.5)
if half_life_L_unique_L_multi[i] != []:
half = chr_len_range[i] - (chr_len_range[i] - num_essential) / 2
#plt.hlines(half, 0, half_life_L_unique_L_multi[i][0], colors="grey", linestyle=":", alpha=0.5)
plt.scatter(half_life_L_unique_L_multi[i][0], half, s=5, color="black")
# These are some information to add to the saved files.
# Create a random seed to save the image
random_seed = str(random.random())[2:6]
if circular: # record if the chromosome is linear or circular
lin_cir = "c"
else:
lin_cir = "l"
probability_str = "" # record the probabilities of each event
for i in probability:
probability_str = probability_str + str(i)
file_name = "SCRaMbLE_chr_len/chr_length_" + lin_cir + "_events_" + str(SCRaMbLEd_events[-1]) + "_sim_" + str(simulations) + "_P" + probability_str + "_" + file_name + random_seed
plt.savefig(file_name + ".png", dpi=300, bbox_inches='tight')
plt.savefig(file_name + ".svg", format="svg", dpi=300, bbox_inches='tight')
plt.show()
plt.close()
return None
# This function plots the chromosome length over a SCRaMbLE evolution using different number of essential LUs.
def chr_len_essential_range_SCRaMbLE(syn_chr=50, events=15, simulations=100, CEN=[], circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1], file_name=""):
essential_range = list(range(0, 51, 10))
# essential_range = [0, 10, 20, 30, 40, 50]
print("essential_range =", essential_range)
chr_len_multi = []
L_unique_multi = []
LG50_multi = []
for ESSE in essential_range:
print("num essential =", ESSE)
if ESSE == 0:
essential = []
else:
essential = sorted(random.sample(syn_chr, k=ESSE))
A_result = mean_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=circular, mu=mu, sigma=sigma, force=force, probability=probability, file_name=file_name)
chr_len_multi.append(A_result[0])
L_unique_multi.append(A_result[1])
LG50_multi.append(A_result[2])
# List of time points
SCRaMbLEd_events = list(range(0, events + 1, 1)) # +1 because we record also the initial value
# plot the results
plt.figure(figsize=(7, 3.5), dpi=300)
# Font size
SMALL_SIZE = 8
MEDIUM_SIZE = 9
BIGGER_SIZE = 10
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title
plt.rcParams['lines.linewidth'] = 0.65
plt.ylabel("Chr length mean over " + str(simulations) + " simulations")
plt.xlabel("SCRaMbLEd events")
plt.title("Chr length over SCRaMbLE events")
Labels = ["# essential LUs = 0", "# essential LUs = 10", "# essential LUs = 20", "# essential LUs = 30", "# essential LUs = 40", "# essential LUs = 50"]
for i in range(len(chr_len_multi)):
plt.plot(SCRaMbLEd_events, chr_len_multi[i], label=Labels[i])
# if L_unique != [[]]:
# plt.plot(SCRaMbLEd_events, L_unique[i], color="cornflowerblue")
plt.legend(fontsize=8)
# These are some information to add to the saved files.
# Create a random seed to save the image
random_seed = str(random.random())[2:6]
if circular: # record if the chromosome is linear or circular
lin_cir = "c"
else:
lin_cir = "l"
probability_str = "" # record the probabilities of each event
for i in probability:
probability_str = probability_str + str(i)
file_name = "SCRaMbLE_chr_len/chr_length_" + lin_cir + "_events_" + str(SCRaMbLEd_events[-1]) + "_sim_" + str(simulations) + "_P" + probability_str + "_" + file_name + random_seed
plt.savefig(file_name + ".png", dpi=300, bbox_inches='tight')
plt.savefig(file_name + ".svg", format="svg", dpi=300, bbox_inches='tight')
plt.show()
plt.close()
return None
# This function plots the chromosome length over a SCRaMbLE evolution using different probabilities of deletions and duplications.
def chr_len_probabilities_range_SCRaMbLE(syn_chr=50, events=15, simulations=100, essential=[], CEN=[], circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1], file_name="", LOG=True):
probabilities_range = [[0,4,4,1], [0,4,4,2], [0,4,4,4], [0,4,4,8], [0,4,4,16]]
chr_len_multi = []
L_unique_multi = []
LG50_multi = []
for PROB in probabilities_range:
print("Probability =", PROB)
A_result = mean_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=circular, mu=mu, sigma=sigma, force=force, probability=PROB, file_name=file_name)
chr_len_multi.append(A_result[0])
L_unique_multi.append(A_result[1])
LG50_multi.append(A_result[2])
# List of time points
SCRaMbLEd_events = list(range(0, events + 1, 1)) # +1 because we record also the initial value
# plot the results
plt.figure(figsize=(7, 3.5), dpi=300)
# Font size
SMALL_SIZE = 8
MEDIUM_SIZE = 9
BIGGER_SIZE = 10
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title
plt.rcParams['lines.linewidth'] = 0.65
plt.ylabel("Chr length mean over " + str(simulations) + " simulations")
plt.xlabel("SCRaMbLEd events")
plt.title("Chr length over SCRaMbLE events", fontsize=9)
#Labels = ["P(Del)/P(Dup) = 4", "P(Del)/P(Dup) = 2", "P(Del)/P(Dup) = 1", "P(Del)/P(Dup) = 1/2", "P(Del)/P(Dup) = 1/4"]
Labels = ["$P_{DEL}/P_{DUP} = 4$", "$P_{DEL}/P_{DUP} = 2$", "$P_{DEL}/P_{DUP} = 1$", "$P_{DEL}/P_{DUP} = 1/2$", "$P_{DEL}/P_{DUP} = 1/4$"]
for i in range(len(chr_len_multi)):
plt.plot(SCRaMbLEd_events, chr_len_multi[i], label=Labels[i])
# if L_unique != [[]]:
# plt.plot(SCRaMbLEd_events, L_unique[i], color="cornflowerblue")
plt.hlines(num_essential, 0, SCRaMbLEd_events[-1], colors="red", linestyle=":", label="No. essential LUs")
plt.legend(fontsize=8)
# These are some information to add to the saved files.
# Create a random seed to save the image
random_seed = str(random.random())[2:6]
if circular: # record if the chromosome is linear or circular
lin_cir = "c"
else:
lin_cir = "l"
probability_str = "" # record the probabilities of each event
for i in probability:
probability_str = probability_str + str(i)
log_id = ""
if LOG:
plt.yscale('log')
log_id = "_log"
file_name = "SCRaMbLE_chr_len/chr_length_" + lin_cir + "_events_" + str(SCRaMbLEd_events[-1]) + "_sim_" + str(simulations) + "_P" + probability_str + "_" + file_name + random_seed + log_id
plt.savefig(file_name + ".png", dpi=300, bbox_inches="tight")
plt.savefig(file_name + ".svg", format="svg", dpi=300, bbox_inches="tight")
plt.show()
plt.close()
return None
# test the code
if __name__ == "__main__":
import os
# Create the folder where the output will be created
if not os.path.exists("SCRaMbLE_chr_len"):
os.makedirs("SCRaMbLE_chr_len")
# Test the chromosome length evolution under SCRaMbLE evolution
syn_chr = list(range(1, 45, 1))
essential = [2, 7, 9, 10, 12, 20]
#essential = [2, 7, 9, 10, 12, 19, 20, 24] # LUs 19 and 24 are not essential but required for fast growth. Deletion of LU 6 can also generate some slow growth phenotype.
#num_essential = 10
#essential = sorted(random.sample(syn_chr, k=num_essential))
#print("essential =", essential)
CEN = [2]
events = 1000
simulations = 1000
plot_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=True, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1], SD=True)
# Try a different chromosome with 100 LUs
syn_chr = list(range(1, 101, 1))
events = 750
#plot_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=False, mu=0, sigma=10, force=True, probability=[0, 4, 4, 1], SD=True)
num_essential = 5
essential = sorted(random.sample(syn_chr, k=num_essential))
CEN_random = [essential[round(len(essential)/2)]]
print("essential =", essential, CEN_random)
#plot_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN_random, circular=False, mu=0, sigma=10, force=True, probability=[0, 4, 4, 1], SD=True)
#plot_mean_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1])
# Test the impact of initial chromosome length on the chromosome length during SCRaMbLE evolution
num_essential = 10
simulations = 50
# I set the essential LUs at random inside the function
#essential = sorted(random.sample(syn_chr, k=num_essential))
#essential = sorted(random.sample(range(1, 26), k=num_essential))
events = 1000
#chr_len_range_SCRaMbLE(events=events, simulations=simulations, essential=essential, CEN=CEN, circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1])
half_life_L_unique_L_multi = [101, 78, 95, 84, 96] # Average = 90.8 +- 9.47100839404126
#print("half_life_L_unique_L_multi =", half_life_L_unique_L_multi, statistics.mean(half_life_L_unique_L_multi), "+-", statistics.stdev(half_life_L_unique_L_multi))
# chr_len_essential_range_SCRaMbLE
syn_chr = list(range(1, 51, 1))
#chr_len_essential_range_SCRaMbLE(syn_chr, events=events, simulations=simulations, CEN=CEN, circular=False, mu=0, sigma=10, force=True, probability=[0, 2, 2, 1])
# Test the impact of SCRaMbLE event type probabilities on the chromosome length during SCRaMbLE evolution
syn_chr = list(range(1, 51, 1))
num_essential = 10
events = 250
simulations = 100
essential = sorted(random.sample(syn_chr, k=num_essential))
#chr_len_probabilities_range_SCRaMbLE(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=False, mu=0, sigma=10, force=True, LOG=False)
# Neutral Evolution. Zero essential LUs
syn_chr = list(range(1, 101, 1))
essential = []
CEN = []
events = 1000
simulations = 200
#plot_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=False, mu=0, sigma=10, force=True, probability=[0, 10, 10, 9], SD=True)
simulations = 200
#plot_SCRaMbLE_chr_len(syn_chr, events=events, simulations=simulations, essential=essential, CEN=CEN, circular=False, mu=0, sigma=10, force=True, probability=[0, 10, 10, 11], SD=True)
# probability = [0, 10, 10, 9] = [0, 34.48, 34.48, 31.03]. Pdel/Pdup = 34.48/31.03 = 1.111
# probability = [0, 10, 10, 11] = [0, 32.25, 32.25, 35.48]. Pdel/Pdup = 32.25/35.48 = 0.909
"""
25
chr_len = 200 40.0
chr_len_SD = 1001 40.0
chr_len_Q1_Q2_Q3 = 1001 = 28.0 40.0 57.0
last_chr_len_zero = 0.0 %
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40.425, 40.275, 39.87, 40.195, 40.045, 40.55, 40.915, 41.35, 40.995, 41.185, 41.465, 41.72, 42.24, 42.165, 41.89, 41.66, 41.615, 41.725, 41.145, 41.52, 41.425, 41.81, 41.335, 41.095, 41.355, 40.84, 40.89, 41.05, 41.395, 41.225, 41.495, 41.755, 41.92, 41.865, 42.45, 42.255, 42.145, 42.025, 41.885, 41.675, 41.185, 40.89, 41.61, 41.525, 41.25, 41.6, 41.465, 40.96, 40.45, 39.735, 39.925, 40.16, 39.795, 39.675, 39.74, 39.88, 40.255, 39.885, 40.095, 39.675, 39.975, 39.57, 39.64]
25 half_life chr_len_L = []
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25 half_life L_unique_L = [101, 244, 419, 598, 792]
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75
chr_len = 200 58.0
chr_len_SD = 1001 58.0
chr_len_Q1_Q2_Q3 = 1001 = 39.0 53.0 74.0
last_chr_len_zero = 0.0 %
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75 half_life chr_len_L = []
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75 half_life L_unique_L = [78, 216, 392, 617, 953]
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125
chr_len = 200 67.0
chr_len_SD = 1001 67.0
chr_len_Q1_Q2_Q3 = 1001 = 42.75 59.0 80.0
last_chr_len_zero = 0.0 %
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125 half_life chr_len_L = [559]
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125 half_life L_unique_L = [95, 215, 378, 555, 808]
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175
chr_len = 200 74.0
chr_len_SD = 1001 74.0
chr_len_Q1_Q2_Q3 = 1001 = 44.0 63.0 88.25
last_chr_len_zero = 0.0 %
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175 half_life chr_len_L = [367]
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175 half_life L_unique_L = [84, 208, 357, 523, 745]
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225
chr_len = 200 83.0
chr_len_SD = 1001 83.0
chr_len_Q1_Q2_Q3 = 1001 = 54.5 76.5 99.25
last_chr_len_zero = 0.0 %
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225 half_life chr_len_L = [301, 856]
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225 half_life L_unique_L = [96, 224, 363, 532, 727, 970]
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13.775, 13.77, 13.765, 13.755, 13.745, 13.74, 13.73, 13.72, 13.715, 13.705, 13.7, 13.7, 13.695, 13.695, 13.68, 13.67, 13.67, 13.665, 13.64, 13.625, 13.62, 13.61, 13.605, 13.6, 13.585, 13.57, 13.56, 13.555, 13.55, 13.55, 13.535, 13.53, 13.525, 13.51, 13.5, 13.49, 13.485, 13.475, 13.465, 13.455, 13.45, 13.425, 13.415, 13.415, 13.415, 13.41, 13.38, 13.365, 13.355, 13.345, 13.34, 13.32, 13.3, 13.3, 13.3, 13.295, 13.28, 13.27, 13.27, 13.27, 13.25, 13.245, 13.23, 13.22, 13.205, 13.19, 13.185, 13.18, 13.17, 13.15, 13.15, 13.14, 13.13, 13.13, 13.13, 13.12, 13.105, 13.105, 13.1, 13.09, 13.09]
half_life_L_unique_L_multi = 25 [101, 244, 419, 598, 792]
half_life_L_unique_L_multi = 75 [78, 216, 392, 617, 953]
half_life_L_unique_L_multi = 125 [95, 215, 378, 555, 808]
half_life_L_unique_L_multi = 175 [84, 208, 357, 523, 745]
half_life_L_unique_L_multi = 225 [96, 224, 363, 532, 727, 970]
"""