def main():
parser = argparse.ArgumentParser(
description='LOL HI THERE',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('ssm_fn')
parser.add_argument('params_fn')
args = parser.parse_args()
variants = inputparser.load_ssms(args.ssm_fn)
params = inputparser.load_params(args.params_fn)
clusters = params['clusters']
supervars = clustermaker.make_cluster_supervars(clusters, variants)
superclusters = clustermaker.make_superclusters(supervars)
# Add empty initial cluster, which serves as tree root.
superclusters.insert(0, [])
M = len(superclusters)
iterations = 1000
parallel = 0
parents = [[0, 0, 0], [0, 1, 2]]
for P in parents:
adj = _parents2adj(P)
print_init(supervars, adj)
for method in ('projection', 'rprop', 'graddesc'):
phi, eta = phi_fitter._fit_phis(adj, superclusters, supervars,
method, iterations, parallel)
# Sometimes the `projection` fitter will return zeros, which result in an
# LLH of -inf if the number of variant reads `V` is non-zero, since
# `Binom(X=V > 0, | N=V+R, p=0) = 0`. To avoid this, set a floor of 1e-6
# on phi values.
phi = np.maximum(1e-6, phi)
print_method(method, phi, supervars)
print()
def sort_clusters_by_vaf(clusters, variants):
supervars = clustermaker.make_cluster_supervars(clusters, variants)
supervars = [supervars['S%s' % idx] for idx in range(len(supervars))]
sv_vaf = np.array([S['vaf'] for S in supervars])
mean_vaf = np.mean(sv_vaf, axis=1)
order = np.argsort(-mean_vaf)
return [clusters[idx] for idx in order]
def main():
parser = argparse.ArgumentParser(
description='LOL HI THERE',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--use-supervars', action='store_true')
parser.add_argument('ssm_fn')
parser.add_argument('params_fn')
parser.add_argument('citup_snv_fn')
parser.add_argument('citup_vid_fn')
parser.add_argument('citup_clusters_fn')
args = parser.parse_args()
variants = inputparser.load_ssms(args.ssm_fn)
params = inputparser.load_params(args.params_fn)
clusters = params['clusters']
if args.use_supervars:
supervars = clustermaker.make_cluster_supervars(clusters, variants)
superclusters = clustermaker.make_superclusters(supervars)
garbage = set()
write_snvs(supervars, garbage, args.citup_snv_fn, args.citup_vid_fn)
write_clusters(supervars, garbage, superclusters, args.citup_clusters_fn)
else:
garbage = set(params['garbage'])
write_snvs(variants, garbage, args.citup_snv_fn, args.citup_vid_fn)
write_clusters(variants, garbage, clusters, args.citup_clusters_fn)
def main():
parser = argparse.ArgumentParser(
description='LOL HI THERE',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--uniform-proposal', action='store_true')
parser.add_argument('ssm_fn')
parser.add_argument('params_fn')
parser.add_argument('pastri_allele_counts_fn')
parser.add_argument('pastri_proposal_fn')
args = parser.parse_args()
variants = inputparser.load_ssms(args.ssm_fn)
params = inputparser.load_params(args.params_fn)
clusters = params['clusters']
supervars = clustermaker.make_cluster_supervars(clusters, variants)
matrices = {
'var_reads': extract_matrix(supervars, 'var_reads'),
'total_reads': extract_matrix(supervars, 'total_reads'),
'alpha': extract_matrix(supervars, 'var_reads'),
'beta': extract_matrix(supervars, 'total_reads'),
}
if args.uniform_proposal:
matrices['alpha'][:] = 1
matrices['beta'][:] = 2
C_max = 15
matrices['alpha'] = matrices['alpha'][:C_max, ]
matrices['beta'] = matrices['beta'][:C_max, ]
write_matrices(('A', matrices['var_reads']),
('D', matrices['total_reads']),
outfn=args.pastri_allele_counts_fn)
write_matrices(('Alpha', matrices['alpha']), ('Beta', matrices['beta']),
outfn=args.pastri_proposal_fn)
def main():
parser = argparse.ArgumentParser(
description='LOL HI THERE',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('ssm_fn')
parser.add_argument('params_fn')
parser.add_argument('calder_input_fn')
args = parser.parse_args()
variants = inputparser.load_ssms(args.ssm_fn)
params = inputparser.load_params(args.params_fn)
clusters = params['clusters']
supervars = clustermaker.make_cluster_supervars(clusters, variants)
vids1, var_reads = extract_matrix(supervars, 'var_reads')
vids2, ref_reads = extract_matrix(supervars, 'ref_reads')
assert vids1 == vids2
vids = vids1
_write_inputs(vids, params['samples'], var_reads, ref_reads,
args.calder_input_fn)
def main():
parser = argparse.ArgumentParser(
description='LOL HI THERE',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--use-supervars',
dest='use_supervars',
action='store_true')
parser.add_argument('ssm_fn')
parser.add_argument('params_fn')
parser.add_argument('pwgs_ssm_fn')
parser.add_argument('pwgs_params_fn')
args = parser.parse_args()
variants = inputparser.load_ssms(args.ssm_fn)
params = inputparser.load_params(args.params_fn)
if args.use_supervars:
variants = clustermaker.make_cluster_supervars(params['clusters'],
variants)
write_ssms(variants, args.pwgs_ssm_fn)
write_params(params['samples'], args.pwgs_params_fn)
def main():
all_plot_choices = set((
'tree',
'pairwise_separate',
'pairwise_mle',
'vaf_matrix',
'phi',
'phi_hat',
'phi_interleaved',
'cluster_stats',
'eta',
'diversity_indices',
))
parser = argparse.ArgumentParser(
description='LOL HI THERE',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--seed', type=int)
parser.add_argument('--tree-index', type=int, default=0)
parser.add_argument('--plot',
dest='plot_choices',
type=lambda s: set(s.split(',')),
help='Things to plot; by default, plot everything')
parser.add_argument('--omit-plots',
dest='omit_plots',
type=lambda s: set(s.split(',')),
help='Things to omit from plotting; overrides --plot')
parser.add_argument('--runid')
parser.add_argument(
'--reorder-subclones',
action='store_true',
help=
'Reorder subclones according to depth-first search through tree structure'
)
parser.add_argument(
'--tree-json',
dest='tree_json_fn',
help=
'Additional external file in which to store JSON, which is already stored statically in the HTML file'
)
parser.add_argument('--phi-orientation',
choices=('samples_as_rows', 'populations_as_rows'),
default='populations_as_rows')
parser.add_argument(
'--remove-normal',
action='store_true',
help=
'Remove normal (non-cancerous) population 0 from tree, phi, and eta plots.'
)
parser.add_argument('ssm_fn')
parser.add_argument('params_fn')
parser.add_argument('results_fn')
parser.add_argument('discord_fn')
parser.add_argument('html_out_fn')
args = parser.parse_args()
np.seterr(divide='raise', invalid='raise', over='raise')
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
plot_choices = _choose_plots(args.plot_choices, args.omit_plots,
all_plot_choices)
results = resultserializer.Results(args.results_fn)
variants = inputparser.load_ssms(args.ssm_fn)
params = inputparser.load_params(args.params_fn)
discord = _parse_discord(args.discord_fn)
data = {
K: results.get(K)[args.tree_index]
for K in (
'struct',
'count',
'llh',
'prob',
'phi',
)
}
data['garbage'] = results.get('garbage')
data['clusters'] = results.get('clusters')
data['samples'] = params['samples']
data['clustrel_posterior'] = results.get_mutrel('clustrel_posterior')
if args.reorder_subclones:
data, params = _reorder_subclones(data, params)
if 'hidden_samples' in params:
hidden = set(params['hidden_samples'])
assert hidden.issubset(set(
data['samples'])) and len(hidden) < len(data['samples'])
visible_sampidxs = [
idx for idx, samp in enumerate(data['samples'])
if samp not in hidden
]
else:
visible_sampidxs = None
samp_colours = params.get('samp_colours', None)
pop_colours = params.get('pop_colours', None)
if samp_colours is not None:
assert set([S[0] for S in samp_colours]).issubset(data['samples'])
if pop_colours is not None:
assert len(pop_colours) == len(data['struct']) + 1
supervars = clustermaker.make_cluster_supervars(data['clusters'], variants)
supervars = [supervars[vid] for vid in common.sort_vids(supervars.keys())]
with open(args.html_out_fn, 'w') as outf:
write_header(args.runid, args.tree_index, outf)
if 'tree' in plot_choices:
tree_struct = util.make_tree_struct(
data['struct'],
data['count'],
data['llh'],
data['prob'],
data['phi'],
supervars,
data['clusters'],
data['samples'],
)
tree_struct['discord'] = discord
_write_tree_html(
tree_struct,
args.tree_index,
visible_sampidxs,
samp_colours,
pop_colours,
'eta' in plot_choices,
'diversity_indices' in plot_choices,
'phi' in plot_choices,
'phi_hat' in plot_choices,
'phi_interleaved' in plot_choices,
args.phi_orientation,
args.remove_normal,
outf,
)
if args.tree_json_fn is not None:
_write_tree_json(tree_struct, args.tree_json_fn)
if 'vaf_matrix' in plot_choices:
vaf_plotter.plot_vaf_matrix(
data['clusters'],
variants,
supervars,
data['garbage'],
data['phi'],
data['samples'],
should_correct_vaf=True,
outf=outf,
)
if 'pairwise_mle' in plot_choices:
relation_plotter.plot_ml_relations(data['clustrel_posterior'],
outf)
if 'pairwise_separate' in plot_choices:
relation_plotter.plot_separate_relations(
data['clustrel_posterior'], outf)
if 'cluster_stats' in plot_choices:
write_cluster_stats(data['clusters'], data['garbage'], supervars,
variants, outf)
write_footer(outf)