parents = ancestor_list(ID, population_tree)
i = 0
for p_ID in parents:
i += 1
if i > 0:
return p_ID
else:
return ''
for population in config.populations:
popindex += 1
max_barcode = config.max_barcode[population]
clone_list, times, clone_dict, lineage_dict, population_tree = read_clone_data(
population)
population_tree = {"": population_tree}
mutant_tree = population_tree[""]
clone_list.append("")
mean_average_fitness = numpy.zeros(inference_params.INTERVALS_PER_EPOCH *
max_barcode)
for ID in clone_list:
clone = clone_dict[ID]
mean_average_fitness += 100. * clone.evolution_fitness * clone.freqs[:inference_params
.INTERVALS_PER_EPOCH
*
max_barcode]
mean_average_fitness += clone.barcoding_fitness * clone.freqs[:inference_params
.INTERVALS_PER_EPOCH
population = args.pop_name
ENVIRONMENT = 'evolution'
coverage_directory = config.barcode_data_root_directory + population + '/'
count_file = glob.glob(coverage_directory + population +
'*read_coverage.txt')[0]
count_dict = file_parser.parse_count_file(count_file)
counts = numpy.asarray([count_dict[key] for key in sorted(count_dict.keys())])
kappas = file_parser.read_kappas_from_file(config.error_model_directory +
population + '-kappas.tsv')
clone_list, times, clone_dict, lineage_dict, population_tree = read_clone_data(
population, read_fitness=False, assign_colors=False)
max_barcode = config.max_barcode[population]
bc_fitness_dict = {}
bc_fitness_ci_dict = {}
converged = False
it = 0
max_iterations = 100
negative_llhs = [10**40]
for clone_ID in clone_dict.keys():
bc_fitness_dict.update({clone_ID: 0.})
bc_fitness_ci_dict.update({clone_ID: np.zeros(2)})
def main(args):
population = args.pop_name
ENVIRONMENT = 'barcoding'
coverage_directory = config.barcode_data_root_directory + population +'/'
count_file = glob.glob(coverage_directory+population+'*read_coverage.txt')[0]
count_dict = file_parser.parse_count_file(count_file)
counts = numpy.asarray([count_dict[key] for key in sorted(count_dict.keys())])
kappas = file_parser.read_kappas_from_file(config.error_model_directory + population + '-kappas.tsv')
clone_list, times, clone_dict, lineage_dict, population_tree = read_clone_data(population, read_fitness = False, assign_colors = False)
max_barcode = config.max_barcode[population]
bc_fitness_dict = {}
bc_fitness_ci_dict = {}
converged = False
it = 0
max_iterations = 100
negative_llhs = [10**40]
for clone_ID in clone_dict.keys():
bc_fitness_dict.update({clone_ID : 0. })
bc_fitness_ci_dict.update({clone_ID : numpy.zeros(2) })
population_mean_fitness = numpy.zeros(2*max_barcode-2)
barcoding_kappas = kappas[10::11][:max_barcode-1]
bc_count_ratios = counts[11::11]*1./(counts[10::11][:-1])
bc_count_ratios = bc_count_ratios[:max_barcode-1]
num_fitnesses = inference_params.num_fitnesses
fitness_grid = inference_params.fitness_grid[ENVIRONMENT]*inference_params.scale_fitness_per_interval[ENVIRONMENT]
bc_fitness_dict = {}
bc_fitness_ci_dict = {}
total_intervals = inference_params.BARCODING_INTERVALS_PER_EPOCH*(max_barcode -1)
# initialize clone fitnesses to zero
current_clone_fitnesses = numpy.zeros( (len(clone_dict.keys())) )
current_clone_fitness_CI_lower = numpy.zeros( (len(clone_dict.keys())) )
current_clone_fitness_CI_upper = numpy.zeros( (len(clone_dict.keys())) )
# initialize up matrices fo clone counts before and after barcoding
clone_counts_before = numpy.ones( (len(clone_dict.keys()),total_intervals) )
clone_counts_after = numpy.ones( (len(clone_dict.keys()),total_intervals) )
clone_freqs_before = numpy.ones( (len(clone_dict.keys()),total_intervals) )
clone_freqs_after = numpy.ones( (len(clone_dict.keys()),total_intervals) )
for i,key in enumerate(clone_dict.keys()):
lineage = clone_dict[key]
clone_counts_before[i] = (lineage.freqs*counts)[10::11][:-1][:max_barcode-1]
clone_counts_after[i] = (lineage.freqs*counts)[11::11][:max_barcode-1]
clone_freqs_before[i] = (lineage.freqs)[10::11][:-1][:max_barcode-1]
clone_freqs_after[i] = (lineage.freqs)[11::11][:max_barcode-1]
# initialize grid containing expectations of frequencies given fitness vectors
expectation_grid = numpy.ones( (len(clone_dict.keys()),total_intervals,len(fitness_grid)) )
#initialize mask for low frequency counts
mask = clone_counts_before > inference_params.threshold_lineage_size
#now for the coordinate descent
#iterate until convergence or until max_iterations have been completed
it = -1
while it < max_iterations:
it += 1
#loop through clones
for this_clone_index, this_clone in enumerate(clone_dict.keys()):
if sum(mask[this_clone_index]) > 0:
other_clones = numpy.ones(len(clone_dict.keys()),dtype = bool)
other_clones[this_clone_index] = False
other_lineages_unnormalized_frequencies = numpy.einsum('ij,i->ij',clone_freqs_before[other_clones],numpy.exp(current_clone_fitnesses[other_clones]))
other_lineages_total_unnormalized_frequency = numpy.sum(clone_freqs_before[other_clones].T * numpy.exp(current_clone_fitnesses[other_clones]),axis = 1)
this_lineage_unnormalized_frequency = numpy.outer(clone_freqs_before[this_clone_index],numpy.exp(fitness_grid))
total_unnormalized_frequency = numpy.outer(other_lineages_total_unnormalized_frequency,numpy.ones(len(fitness_grid))) + this_lineage_unnormalized_frequency
expectation_grid[other_clones] = numpy.repeat(other_lineages_unnormalized_frequencies[:, :, numpy.newaxis], len(fitness_grid), axis=2)
expectation_grid[this_clone_index] = this_lineage_unnormalized_frequency
expectation_grid = expectation_grid/total_unnormalized_frequency
expectation_grid = numpy.einsum('ijk,j->ijk', expectation_grid, counts[11::11][:max_barcode-1])
expectation_grid[expectation_grid < 1] = 1
clone_counts_after[clone_counts_after<1] = 1
#calculate negative log likelihoods
llhs = (numpy.sqrt(expectation_grid) - numpy.sqrt(clone_counts_after[:,:,numpy.newaxis]))**2
llhs = numpy.einsum('ijk,j->ijk',llhs,1./barcoding_kappas)
llhs += 0.75* numpy.log(clone_counts_after[:,:,numpy.newaxis])
llhs += -0.25* numpy.log(expectation_grid)
llhs += 0.5 * numpy.log(4*numpy.pi*barcoding_kappas[numpy.newaxis,:,numpy.newaxis])
llhs = numpy.einsum('ijk,ij->ijk',llhs,mask)
llhs = numpy.einsum('ijk->k',llhs)
current_clone_fitnesses[this_clone_index] = fitness_grid[llhs == min(llhs)][0]
fitness_CI_range = fitness_grid[llhs < 2+ min(llhs)]
current_clone_fitness_CI_lower[this_clone_index] = fitness_CI_range[0]
current_clone_fitness_CI_upper[this_clone_index] = fitness_CI_range[-1]
if this_clone == "":
# pin ancestor fitness at 0
current_clone_fitnesses -= current_clone_fitnesses[this_clone_index]
current_clone_fitness_CI_lower -= current_clone_fitnesses[this_clone_index]
current_clone_fitness_CI_upper -= current_clone_fitnesses[this_clone_index]
if len(fitness_grid[llhs == min(llhs)]) > 1:
print(len(fitness_grid[llhs == min(llhs)]), "solutions work equally well")
print(sum(mask[this_clone_index]))
else:
# if there are no good barcoding intervals,
# look for ancestor, and copy their fitness onto this child
ancestors = list(ancestor_list(this_clone, population_tree))
if len(ancestors)>0:
parent_ID = ancestors[-1]
else:
parent_ID = ''
parent_index = [i for i, s in enumerate(clone_dict.keys()) if parent_ID == s][0]
current_clone_fitnesses[this_clone_index] = current_clone_fitnesses[parent_index]
current_clone_fitness_CI_lower[this_clone_index] = fitness_grid[0]
current_clone_fitness_CI_upper[this_clone_index] = fitness_grid[-1]
negative_llhs.append(min(llhs))
if abs(negative_llhs[-1] - negative_llhs[-2])<0.001:
print('Converged after', it, 'iterations.')
print('Negative log-likelihood for each iteration:')
print(negative_llhs)
break
for this_clone_index,this_clone in enumerate(clone_dict.keys()):
ID = this_clone
bc_fitness_dict[ID] = current_clone_fitnesses[this_clone_index]
bc_fitness_ci_dict[ID] = [current_clone_fitness_CI_lower[this_clone_index],current_clone_fitness_CI_upper[this_clone_index]]
with open(config.clone_data_directory+'%s-%s_fitnesses.tsv' % (population,ENVIRONMENT), 'w') as csvfile:
out_writer = csv.writer(csvfile, delimiter='\t', quotechar='|', quoting=csv.QUOTE_MINIMAL)
out_writer.writerow(['BC'] + ['Barcoding Fitness (per barcoding procedure)', 'CI_lower', 'CI_upper'])
clone_list.append("")
for ID in clone_list:
lineage = clone_dict[ID]
if lineage.ID == '':
row = ['ancestor']
else:
row = [lineage.ID]
row.extend([bc_fitness_dict[lineage.ID]])
row.extend(bc_fitness_ci_dict[lineage.ID])
out_writer.writerow(row)
def main(args):
population = args.pop_name
ENVIRONMENT = 'evolution'
coverage_directory = config.barcode_data_root_directory + population + '/'
count_file = glob.glob(coverage_directory + population +
'*read_coverage.txt')[0]
count_dict = file_parser.parse_count_file(count_file)
counts = numpy.asarray(
[count_dict[key] for key in sorted(count_dict.keys())])
kappas = file_parser.read_kappas_from_file(config.error_model_directory +
population + '-kappas.tsv')
clone_list, times, clone_dict, lineage_dict, population_tree = read_clone_data(
population, read_fitness=False, assign_colors=False)
max_barcode = config.max_barcode[population]
bc_fitness_dict = {}
bc_fitness_ci_dict = {}
converged = False
it = 0
max_iterations = 100
negative_llhs = [10**40]
for clone_ID in clone_dict.keys():
bc_fitness_dict.update({clone_ID: 0.})
bc_fitness_ci_dict.update({clone_ID: numpy.zeros(2)})
population_mean_fitness = numpy.zeros(
inference_params.INTERVALS_PER_EPOCH * max_barcode)
evolution_kappas = []
evolution_count_ratios = []
evolution_total_counts = []
for epoch in range(0, max_barcode):
begin = inference_params.INTERVALS_PER_EPOCH * epoch
end = inference_params.INTERVALS_PER_EPOCH * epoch + inference_params.EVOLUTION_INTERVALS_PER_EPOCH
evolution_kappas.extend(kappas[begin:end])
evolution_count_ratios.extend(1. * counts[begin + 1:end + 1] /
counts[begin:end])
evolution_total_counts.extend(1. * counts[begin + 1:end + 1])
evolution_kappas = numpy.asarray(evolution_kappas)
evolution_count_ratios = numpy.asarray(evolution_count_ratios)
evolution_total_counts = numpy.asarray(evolution_total_counts)
num_fitnesses = inference_params.num_fitnesses
fitness_grid = inference_params.fitness_grid[
ENVIRONMENT] * inference_params.scale_fitness_per_interval[ENVIRONMENT]
total_intervals = inference_params.EVOLUTION_INTERVALS_PER_EPOCH * max_barcode
# initialize clone fitnesses to zero
current_clone_fitnesses = numpy.zeros((len(clone_dict.keys())))
current_clone_fitness_CI_lower = numpy.zeros((len(clone_dict.keys())))
current_clone_fitness_CI_upper = numpy.zeros((len(clone_dict.keys())))
# initialize up matrices fo clone counts before and after barcoding
clone_counts_before = numpy.ones((len(clone_dict.keys()), total_intervals))
clone_counts_after = numpy.ones((len(clone_dict.keys()), total_intervals))
clone_freqs_before = numpy.ones((len(clone_dict.keys()), total_intervals))
clone_freqs_after = numpy.ones((len(clone_dict.keys()), total_intervals))
# for i in range(0,len(clone_dict.keys())):
for i, lineage in enumerate(clone_dict.values()):
for epoch in range(0, max_barcode):
copy_begin = inference_params.EVOLUTION_INTERVALS_PER_EPOCH * epoch
copy_end = inference_params.EVOLUTION_INTERVALS_PER_EPOCH * (
epoch + 1)
begin = inference_params.INTERVALS_PER_EPOCH * epoch
end = begin + inference_params.EVOLUTION_INTERVALS_PER_EPOCH
clone_counts_before[i][copy_begin:copy_end] = (lineage.freqs *
counts)[begin:end]
clone_counts_after[i][copy_begin:copy_end] = (lineage.freqs *
counts)[begin +
1:end + 1]
clone_freqs_before[i][copy_begin:copy_end] = (
lineage.freqs)[begin:end]
clone_freqs_after[i][copy_begin:copy_end] = (lineage.freqs)[begin +
1:end +
1]
# initialize grid containing expectations of frequencies given fitness vectors
expectation_grid = numpy.ones(
(len(clone_dict.keys()), total_intervals, len(fitness_grid)))
#initialize mask for low frequency counts
mask = clone_counts_before > inference_params.threshold_lineage_size
#now for the coordinate descent
#iterate until convergence or until max_iterations have been completed
it = -1
while it < max_iterations:
it += 1
print("iteration", it)
#loop through clones
for this_clone_index, this_clone in enumerate(clone_dict.keys()):
if sum(mask[this_clone_index]) > 0:
other_clones = numpy.ones(len(clone_dict.keys()), dtype=bool)
other_clones[this_clone_index] = False
other_lineages_unnormalized_frequencies = numpy.einsum(
'ij,i->ij', clone_freqs_before[other_clones],
numpy.exp(current_clone_fitnesses[other_clones]))
other_lineages_total_unnormalized_frequency = numpy.sum(
clone_freqs_before[other_clones].T *
numpy.exp(current_clone_fitnesses[other_clones]),
axis=1)
this_lineage_unnormalized_frequency = numpy.outer(
clone_freqs_before[this_clone_index],
numpy.exp(fitness_grid))
total_unnormalized_frequency = numpy.outer(
other_lineages_total_unnormalized_frequency,
numpy.ones(len(
fitness_grid))) + this_lineage_unnormalized_frequency
expectation_grid[other_clones] = numpy.repeat(
other_lineages_unnormalized_frequencies[:, :,
numpy.newaxis],
len(fitness_grid),
axis=2)
expectation_grid[
this_clone_index] = this_lineage_unnormalized_frequency
expectation_grid = expectation_grid / total_unnormalized_frequency
expectation_grid = numpy.einsum('ijk,j->ijk', expectation_grid,
evolution_total_counts)
expectation_grid[expectation_grid < 1] = 1
clone_counts_after[clone_counts_after < 1] = 1
llhs = (numpy.sqrt(expectation_grid) -
numpy.sqrt(clone_counts_after[:, :, numpy.newaxis]))**2
llhs = numpy.einsum('ijk,j->ijk', llhs, 1. / evolution_kappas)
llhs += 0.75 * numpy.log(clone_counts_after[:, :,
numpy.newaxis])
llhs += -0.25 * numpy.log(expectation_grid)
llhs += 0.5 * numpy.log(
4 * numpy.pi *
evolution_kappas[numpy.newaxis, :, numpy.newaxis])
llhs = numpy.einsum('ijk,ij->ijk', llhs, mask)
llhs = numpy.einsum('ijk->k', llhs)
current_clone_fitnesses[this_clone_index] = fitness_grid[
llhs == min(llhs)][0]
fitness_CI_range = fitness_grid[llhs < 2 + min(llhs)]
current_clone_fitness_CI_lower[
this_clone_index] = fitness_CI_range[0]
current_clone_fitness_CI_upper[
this_clone_index] = fitness_CI_range[-1]
if this_clone == "":
# pin ancestor fitness at 0
current_clone_fitnesses -= current_clone_fitnesses[
this_clone_index]
current_clone_fitness_CI_lower -= current_clone_fitnesses[
this_clone_index]
current_clone_fitness_CI_upper -= current_clone_fitnesses[
this_clone_index]
else:
current_clone_fitness_CI_lower[
this_clone_index] = fitness_grid[0]
current_clone_fitness_CI_upper[
this_clone_index] = fitness_grid[-1]
negative_llhs.append(min(llhs))
if abs(negative_llhs[-1] - negative_llhs[-2]) < 0.001:
print('Converged after', it, 'iterations.')
print('Negative log-likelihood for each iteration:')
print(negative_llhs)
break
for this_clone_index, this_clone in enumerate(clone_dict.keys()):
ID = this_clone
bc_fitness_dict[ID] = current_clone_fitnesses[this_clone_index]
bc_fitness_ci_dict[ID] = [
current_clone_fitness_CI_lower[this_clone_index],
current_clone_fitness_CI_upper[this_clone_index]
]
with open(
config.clone_data_directory + '%s-%s_fitnesses.tsv' %
(population, ENVIRONMENT), 'w') as csvfile:
out_writer = csv.writer(csvfile,
delimiter='\t',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
out_writer.writerow(['BC'] + [
'Evolution Fitness (per 10-generation interval)', 'CI_lower',
'CI_upper'
])
clone_list.append("")
for ID in clone_list:
lineage = clone_dict[ID]
if lineage.ID == '':
row = ['ancestor']
else:
row = [lineage.ID]
row.extend([bc_fitness_dict[lineage.ID]])
row.extend(bc_fitness_ci_dict[lineage.ID])
out_writer.writerow(row)