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Add CONET data sims
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@pedrofale
pedrofale committed May 29, 2024
1 parent 20d0b75 commit 025a7b7
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232 changes: 232 additions & 0 deletions reproducibility/simulations/benchmark.smk
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Expand Up @@ -64,6 +64,8 @@ CONET_OUTPUT = os.path.join(config["output"], "conet")
CONET_GINKGO_OUTPUT = os.path.join(config["output"], "conet_ginkgo")
CONET_TREE_OUTPUT = os.path.join(config["output"], "conet_tree_distances")
CONET_GINKGO_TREE_OUTPUT = os.path.join(config["output"], "conet_ginkgo_tree_distances")
CONET_CBS_OUTPUT = os.path.join(config["output"], "conet_cbs")
CONET_CBS_TREE_OUTPUT = os.path.join(config["output"], "conet_cbs_tree_distances")

try:
other_cnv_methods = config["other_methods"]
Expand Down Expand Up @@ -557,6 +559,79 @@ rule conet_ginkgo_inference:
shell:
"python {params.script} --bin_path {params.bin_path} --counts {input.d_mat} --cnvs {input.init_cnvs} --intermediate_dir {params.intermediate_dir} --seed {wildcards.rep_id} --out_cnvs {output.conet_inferred_cnvs} --out_tree {output.conet_inferred_tree} --out_attachments {output.conet_inferred_attachments}"

rule conet_cbs_inference:
params:
script = config["conet"]["script"],
n_nodes = n_nodes,
scratch = config["conet"]["scratch"],
mem = config["conet"]["mem"],
time = config["conet"]["time"],
script_inp = str(n_nodes)+"nodes_{rep_id}",
intermediate_dir = CONET_CBS_OUTPUT +'/'+ str(n_nodes) + 'nodes' + '/' + '{rep_id}' + '/',
bin_path = config["conet"]["bin_path"],
em_iters = config["conet"]["em_iters"],
pt_iters = config["conet"]["pt_iters"],
input:
d_mat = SIM_OUTPUT+ '_' + sim_prefix +'/'+ str(n_nodes) + 'nodes' + '/' + '{rep_id}' + '_d_mat.txt',
output:
conet_inferred_cnvs = CONET_CBS_OUTPUT +'/'+ str(n_nodes) + 'nodes' + '/'+ '{rep_id}' + '_conet_cbs_inferred.txt',
conet_inferred_tree = CONET_CBS_OUTPUT +'/'+ str(n_nodes) + 'nodes' + '/'+ '{rep_id}' + '_conet_cbs_tree.txt',
conet_inferred_attachments = CONET_CBS_OUTPUT +'/'+ str(n_nodes) + 'nodes' + '/' + '{rep_id}' + '_conet_cbsattachments.txt'
threads: 10
# shell:
# "python {params.script} --bin_path {params.bin_path} --counts {input.d_mat} --cnvs {input.init_cnvs} --intermediate_dir {params.intermediate_dir} --seed {wildcards.rep_id} --em_iters {params.em_iters} --pt_iters {params.pt_iters} --out_cnvs {output.conet_inferred_cnvs} --out_tree {output.conet_inferred_tree} --out_attachments {output.conet_inferred_attachments}"
run:
real_ind = list(map(lambda x: x.start, list(tree.nodes)))
real_ind.extend(list(map(lambda x: x.end, list(tree.nodes))))
real_ind = list(set(real_ind))
real_ind.sort()
if wildcards.known_breakpoints:
# Extract real breakpoints indices
candidates = real_ind
save_counts_in_CONET_format(dirpath + "counts_synthetic", corr_reads, candidates)
else:
save_counts_in_CONET_format(dirpath + "counts_synthetic", corr_reads, [])
subprocess.run(["Rscript", "CBS_MergeLevels.R", "--mincells=5",
f"--output={dirpath}cbs_out", f"--dataset={dirpath}counts_synthetic"])
X = np.loadtxt(dirpath + "cbs_out", delimiter=",")
candidates = []
for i in range(X.shape[0]):
if X[i, 4] == 1.0:
candidates.append(i)
save_counts_in_CONET_format(dirpath + "counts_synthetic", corr_reads, candidates)
print(f"Found {len(candidates)} breakpoint candidates...")

# Convert model data to CONET format
data_converter = dc.DataConverter(dirpath + "counts_synthetic",
delimiter=';',
default_bin_length=1,
event_length_normalizer=corr_reads.shape[1], # number of loci
add_chromosome_ends=False,
neutral_cn=2.0)
data_converter.create_CoNET_input_files(dirpath, add_chr_ends_to_indices=False)

# Perform CONET inference
conet = c.CONET(str(Path(conf.conet_binary_dir) / Path("CONET")))
params = cp.CONETParameters(tree_structure_prior_k1=0.01,
data_dir=dirpath, counts_penalty_s1=100000, counts_penalty_s2=100000,
param_inf_iters=500000, seed=random.randint(0, 1000), mixture_size=2, pt_inf_iters=1000000,
use_event_lengths_in_attachment=False,
event_length_penalty_k0=1)
conet.infer_tree(params)
print(f"CONET inference finished on model {i}")
result = ir.InferenceResult(conf.conet_binary_dir, corr_reads.T)

inferred_cn = result.get_inferred_copy_numbers(2, conf.bins, conf.cells)
inferred_brkp = result.bp_matrix.astype(int)
inferred_nodes = set(result.tree.nodes)
inferred_edges = set(result.tree.edges)

real_cn = data[0]
real_brkp = data[4][:, real_ind].astype(int)
real_nodes = set((n.start, n.end) for n in tree.nodes)
real_edges = set(((e[0].start, e[0].end), (e[1].start, e[1].end)) for e in tree.edges)


rule run_medalt:
params:
script = config["medalt"]["script"],
Expand Down Expand Up @@ -610,3 +685,160 @@ rule compute_medalt_ginkgo_tree_distances:
medalt_tree_distance = f'{MEDALT_TREE_OUTPUT}/{str(n_nodes)}nodes_' + '{regions}regions_{reads}reads/{rep_id}_medalt_ginkgo_tree_distance.txt'
shell:
"python {params.script} {input.medalt_tree} {input.cnvs} {output.medalt_tree_distance}"

rule compute_deltas:
input:
simulations = expand(f'{SIM_OUTPUT}_{sim_prefix}/{str(n_nodes)}nodes_' + '{regions}regions_{reads}reads/{rep_id}_d_mat.txt',
regions=[x for x in n_regions], reads=[x for x in n_reads], rep_id=[x for x in range(0,all_n_tps)]),
best_full_tree = expand(f'{TREES_OUTPUT}_best_full_tree/{str(n_nodes)}nodes_' + '{regions}regions_{reads}reads/{rep_id}_full_tree.txt',
regions=[x for x in n_regions], reads=[x for x in n_reads], rep_id=[x for x in range(0,all_n_tps)], tree_rep_id=[x for x in range(0,tree_rep)]),

best_cluster_tree = expand(f'{TREES_OUTPUT}_best_cluster_tree/{str(n_nodes)}nodes_' + '{regions}regions_{reads}reads/{rep_id}_cluster_tree.txt',
regions=[x for x in n_regions], reads=[x for x in n_reads], rep_id=[x for x in range(0,all_n_tps)]),

best_full_tree_sum = expand(f'{TREES_OUTPUT}_best_full_tree_sum/{str(n_nodes)}nodes_' + '{regions}regions_{reads}reads/{rep_id}_full_tree.txt',
regions=[x for x in n_regions], reads=[x for x in n_reads], rep_id=[x for x in range(0,all_n_tps)], tree_rep_id=[x for x in range(0,tree_rep)]),

best_cluster_tree_sum = expand(f'{TREES_OUTPUT}_best_cluster_tree_sum/{str(n_nodes)}nodes_' + '{regions}regions_{reads}reads/{rep_id}_cluster_tree.txt',
regions=[x for x in n_regions], reads=[x for x in n_reads], rep_id=[x for x in range(0,all_n_tps)]),

other_cnvs = expand(os.path.join(config["output"], '{method}') +'/'+ str(n_nodes) + 'nodes_' + '{regions}'+'regions_'+ '{reads}'+'reads'+ '/' + '{rep_id}' + '_{method}_inferred.txt',
regions=[x for x in n_regions], reads=[x for x in n_reads], rep_id=[x for x in range(0,all_n_tps)], method=other_cnv_methods),
output:
out_fname = os.path.join(output_path, 'deltas_sims.csv')
run:
rows = []
rows.append('index,rep_id,method,delta')
i = 0
for true_cnvs in input.simulations:
# True
rep_id = true_cnvs.split('/')[-1].split('_')[0]
gt = np.loadtxt(true_cnvs, delimiter=',')
if gt.shape[0] == n_bins: # should be cells by bins
gt = np.transpose(gt)

# Diploid
i+=1
method = 'diploid'
inf = np.ones(gt.shape) * 2
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

# i += 1
# method = 'ginkgo'
# inf_cnvs = f'{GINKGO_OUTPUT}/{n_nodes}nodes/{rep_id}_ginkgo_inferred.txt'
# inf = np.loadtxt(inf_cnvs, delimiter=' ')
# error = np.sqrt(np.mean((gt-inf)**2))
# rows.append(f'{i},{rep_id},{method},{error}')
#
# i += 1
# method = 'conet_ginkgo'
# inf_cnvs = f'{CONET_GINKGO_OUTPUT}/{n_nodes}nodes/{rep_id}_conet_ginkgo_inferred.txt'
# inf = np.loadtxt(inf_cnvs, delimiter=',')
# error = np.sqrt(np.mean((gt-inf)**2))
# rows.append(f'{i},{rep_id},{method},{error}')

# i += 1
# method = 'conet_knownbp'
# inf_cnvs = f'{CONET_KNOWNBP_OUTPUT}/{n_nodes}nodes/{rep_id}_conet_knownbp_inferred.txt'
# inf = np.loadtxt(inf_cnvs, delimiter=';').T
# error = np.sqrt(np.mean((gt-inf)**2))
# rows.append(f'{i},{rep_id},{method},{error}')

for other_cnvs_file in other_cnvs:
i += 1
method = other_cnvs_file.split('')
inf = np.loadtxt(other_cnvs_file, delimiter=',')
if inf.shape != gt.shape:
inf = inf.T
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

for fairness in ['unfair', 'fair']:
i += 1
method = f'cluster_tree_{fairness}'
inf_cnvs = f'{TREES_OUTPUT}_best_cluster_tree/{fairness}/{n_nodes}nodes/{rep_id}_cluster_tree_cnvs.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

i += 1
method = f'cluster_tree_sum_{fairness}'
inf_cnvs = f'{TREES_OUTPUT}_best_cluster_tree_sum/{fairness}/{n_nodes}nodes/{rep_id}_cluster_tree_cnvs.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

i += 1
method = f'full_tree_{fairness}'
inf_cnvs = f'{TREES_OUTPUT}_best_full_tree/{fairness}/{n_nodes}nodes/{rep_id}_full_tree_cnvs.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

i += 1
method = f'full_tree_sum_{fairness}'
inf_cnvs = f'{TREES_OUTPUT}_best_full_tree_sum/{fairness}/{n_nodes}nodes/{rep_id}_full_tree_cnvs.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

# i += 1
# method = f'cluster_tree_{fairness}_knownbps'
# inf_cnvs = f'{TREES_OUTPUT}_best_cluster_tree/{fairness}/{n_nodes}nodes/{rep_id}_cluster_tree_cnvs_knownbps.csv'
# inf = np.loadtxt(inf_cnvs, delimiter=',')
# error = np.sqrt(np.mean((gt-inf)**2))
# rows.append(f'{i},{rep_id},{method},{error}')
#
# i += 1
# method = f'cluster_tree_sum_{fairness}_knownbps'
# inf_cnvs = f'{TREES_OUTPUT}_best_cluster_tree_sum/{fairness}/{n_nodes}nodes/{rep_id}_cluster_tree_cnvs_knownbps.csv'
# inf = np.loadtxt(inf_cnvs, delimiter=',')
# error = np.sqrt(np.mean((gt-inf)**2))
# rows.append(f'{i},{rep_id},{method},{error}')
#
# i += 1
# method = f'full_tree_{fairness}_knownbps'
# inf_cnvs = f'{TREES_OUTPUT}_best_full_tree/{fairness}/{n_nodes}nodes/{rep_id}_full_tree_cnvs_knownbps.csv'
# inf = np.loadtxt(inf_cnvs, delimiter=',')
# error = np.sqrt(np.mean((gt-inf)**2))
# rows.append(f'{i},{rep_id},{method},{error}')
#
# i += 1
# method = f'full_tree_sum_{fairness}_knownbps'
# inf_cnvs = f'{TREES_OUTPUT}_best_full_tree_sum/{fairness}/{n_nodes}nodes/{rep_id}_full_tree_cnvs_knownbps.csv'
# inf = np.loadtxt(inf_cnvs, delimiter=',')
# error = np.sqrt(np.mean((gt-inf)**2))
# rows.append(f'{i},{rep_id},{method},{error}')

i += 1
method = f'cluster_tree_{fairness}_adapted'
inf_cnvs = f'{TREES_OUTPUT}_best_cluster_tree/{fairness}/{n_nodes}nodes/{rep_id}_cluster_tree_cnvs_adapted.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

i += 1
method = f'cluster_tree_sum_{fairness}_adapted'
inf_cnvs = f'{TREES_OUTPUT}_best_cluster_tree_sum/{fairness}/{n_nodes}nodes/{rep_id}_cluster_tree_cnvs_adapted.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

i += 1
method = f'full_tree_{fairness}_adapted'
inf_cnvs = f'{TREES_OUTPUT}_best_full_tree/{fairness}/{n_nodes}nodes/{rep_id}_full_tree_cnvs_adapted.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')

i += 1
method = f'full_tree_sum_{fairness}_adapted'
inf_cnvs = f'{TREES_OUTPUT}_best_full_tree_sum/{fairness}/{n_nodes}nodes/{rep_id}_full_tree_cnvs_adapted.csv'
inf = np.loadtxt(inf_cnvs, delimiter=',')
error = np.sqrt(np.mean((gt-inf)**2))
rows.append(f'{i},{rep_id},{method},{error}')


with open(output.out_fname, 'w') as f:
f.write('\n'.join(rows))
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