def run_correlation(run_parameters):
""" perform gene prioritization
Args:
run_parameters: parameter set dictionary.
"""
run_parameters["results_tmp_directory"] = kn.create_dir(run_parameters["results_directory"], 'tmp')
results_tmp_directory = run_parameters["results_tmp_directory" ]
phenotype_name_full_path = run_parameters["phenotype_name_full_path" ]
spreadsheet_name_full_path = run_parameters["spreadsheet_name_full_path"]
spreadsheet_df = kn.get_spreadsheet_df(spreadsheet_name_full_path)
phenotype_df = kn.get_spreadsheet_df(phenotype_name_full_path )
phenotype_df = phenotype_df.T
number_of_jobs = len(phenotype_df.index)
jobs_id = range(0, number_of_jobs)
zipped_arguments = dstutil.zip_parameters( run_parameters
, spreadsheet_df
, phenotype_df
, jobs_id
)
dstutil.parallelize_processes_locally( run_correlation_worker
, zipped_arguments
, number_of_jobs
)
write_phenotype_data_all(run_parameters )
kn.remove_dir (results_tmp_directory)
def run_bootstrap_net_correlation(run_parameters):
""" perform gene prioritization using bootstrap sampling and network smoothing
Args:
run_parameters: parameter set dictionary.
"""
run_parameters["results_tmp_directory"] = kn.create_dir(run_parameters["results_directory"], 'tmp')
gg_network_name_full_path = run_parameters['gg_network_name_full_path']
network_mat, unique_gene_names = kn.get_sparse_network_matrix(gg_network_name_full_path)
network_mat = normalize(network_mat, norm="l1", axis=0)
phenotype_df = kn.get_spreadsheet_df(run_parameters["phenotype_name_full_path"])
spreadsheet_df = kn.get_spreadsheet_df(run_parameters["spreadsheet_name_full_path"])
spreadsheet_genes_as_input = spreadsheet_df.index.values
phenotype_df = phenotype_df.T
spreadsheet_df = kn.update_spreadsheet_df(spreadsheet_df, unique_gene_names)
spreadsheet_df = zscore_dataframe(spreadsheet_df)
sample_smooth, iterations = kn.smooth_matrix_with_rwr(spreadsheet_df.as_matrix(), network_mat.T, run_parameters)
spreadsheet_df = pd.DataFrame(sample_smooth, index=spreadsheet_df.index, columns=spreadsheet_df.columns)
baseline_array = np.ones(network_mat.shape[0]) / network_mat.shape[0]
baseline_array = kn.smooth_matrix_with_rwr(baseline_array, network_mat, run_parameters)[0]
number_of_jobs = len(phenotype_df.index)
jobs_id = range(0, number_of_jobs)
zipped_arguments = dstutil.zip_parameters(run_parameters, spreadsheet_df, phenotype_df, network_mat,
spreadsheet_genes_as_input, baseline_array, jobs_id)
dstutil.parallelize_processes_locally(run_bootstrap_net_correlation_worker, zipped_arguments, number_of_jobs)
write_phenotype_data_all(run_parameters)
kn.remove_dir(run_parameters["results_tmp_directory"])
def run_similarity(run_parameters):
""" Performs similarity analysis and saves the similarity matrix.
Args:
run_parameters: parameter set dictionary.
"""
expression_name = run_parameters["spreadsheet_name_full_path"]
signature_name = run_parameters["signature_name_full_path"]
similarity_measure = run_parameters["similarity_measure"]
expression_df = kn.get_spreadsheet_df(expression_name)
signature_df = kn.get_spreadsheet_df(signature_name)
samples_names = expression_df.columns
signatures_names = signature_df.columns
signatures_names = [i.split('.')[0] for i in signatures_names]
signature_df.columns = signatures_names
similarity_mat = generate_similarity_mat(expression_df, signature_df,
similarity_measure)
# similarity_mat = map_similarity_range(similarity_mat, 0)
similarity_df = pd.DataFrame(similarity_mat,
index=samples_names,
columns=signatures_names)
save_final_samples_signature(similarity_df, run_parameters)
save_best_match_signature(similarity_df, run_parameters)
def run_select_subtype_df(run_parameters):
""" Subset samples based on some row value, e.g., patients with longer survival.
Output can be a smaller spreadsheet with fewer columns.
From a genes x samples spreadsheet and a samples x phenotypes spreadsheet,
return both spreadsheets with only the samples corresponding to a category in a phenotype.
Args: run_parameters with keys:
"results_directory", "spreadsheet_file_name", "phenotype_file_name",
"phenotype_id", "select_category"
"""
results_directory = run_parameters['results_directory']
spreadsheet_file_name = run_parameters['spreadsheet_file_name']
phenotype_file_name = run_parameters['phenotype_file_name']
phenotype_id = run_parameters['phenotype_id']
select_category = run_parameters['select_category']
spreadsheet_df = kn.get_spreadsheet_df(spreadsheet_file_name)
phenotype_df = kn.get_spreadsheet_df(phenotype_file_name)
spreadsheet_df, phenotype_df = select_subtype_df(spreadsheet_df,
phenotype_df,
phenotype_id,
select_category)
transform_name = "phenotype_category"
write_transform_df(spreadsheet_df, spreadsheet_file_name, transform_name,
results_directory)
write_transform_df(phenotype_df, phenotype_file_name, transform_name,
results_directory)
def run_correlation(run_parameters):
""" perform feature prioritization
Args:
run_parameters: parameter set dictionary.
"""
max_cpu = run_parameters["max_cpu"]
run_parameters["results_tmp_directory"] = kn.create_dir(
run_parameters["results_directory"], 'tmp')
phenotype_df = kn.get_spreadsheet_df(
run_parameters["phenotype_name_full_path"])
spreadsheet_df = kn.get_spreadsheet_df(
run_parameters["spreadsheet_name_full_path"])
phenotype_df = phenotype_df.T
len_phenotype = len(phenotype_df.index)
array_of_jobs = range(0, len_phenotype)
for i in range(0, len_phenotype, max_cpu):
jobs_id = array_of_jobs[i:i + max_cpu]
number_of_jobs = len(jobs_id)
zipped_arguments = dstutil.zip_parameters(run_parameters,
spreadsheet_df, phenotype_df,
jobs_id)
dstutil.parallelize_processes_locally(run_correlation_worker,
zipped_arguments, number_of_jobs)
write_phenotype_data_all(run_parameters)
kn.remove_dir(run_parameters["results_tmp_directory"])
def run_lasso_predict(run_parameters):
gene_file = run_parameters['spreadsheet_name_full_path' ]
sign_file = run_parameters['response_name_full_path' ]
test_file = run_parameters['test_spreadsheet_name_full_path']
gene_df = kn.get_spreadsheet_df(gene_file)
sign_df = kn.get_spreadsheet_df(sign_file)
test_df = kn.get_spreadsheet_df(test_file)
row_names = test_df.columns
gene_mat = gene_df.values
sign_mat = sign_df.values[0]
test_mat = test_df.values
min_alpha = run_parameters['min_alpha']
max_alpha = run_parameters['max_alpha']
n_alpha = run_parameters['n_alpha']
intercept = run_parameters['fit_intercept']
normalization = run_parameters['normalize']
max_iter = run_parameters['max_iter']
tolerance = run_parameters['tolerance']
alpha_grid = np.linspace(min_alpha, max_alpha, num=n_alpha)
reg_model = linear_model.LassoCV(
alphas=alpha_grid, fit_intercept=intercept, \
normalize=normalization, max_iter=max_iter, tol=tolerance, cv=5)
reg_model.fit( gene_mat.T, sign_mat)
filename = os.path.join(run_parameters['results_directory'], 'lasso_model.pkl')
pickle.dump(reg_model, open(filename, 'wb'))
response_predict = reg_model.predict(test_mat.T)
predict_df = pd.DataFrame(response_predict.T, index=row_names, columns=['predict'])
write_predict_data(predict_df, run_parameters)
def run_cc_net_similarity(run_parameters):
""" wrapper: call sequence to perform signature analysis with
random walk smoothing and bootstrapped similarity and save results.
Args:
run_parameters: parameter set dictionary.
"""
tmp_dir = 'tmp_cc_similarity_'
run_parameters = update_tmp_directory(run_parameters, tmp_dir)
expression_name = run_parameters["spreadsheet_name_full_path"]
signature_name = run_parameters["signature_name_full_path" ]
gg_network_name = run_parameters['gg_network_name_full_path' ]
similarity_measure = run_parameters["similarity_measure" ]
number_of_bootstraps = run_parameters['number_of_bootstraps' ]
processing_method = run_parameters['processing_method' ]
expression_df = kn.get_spreadsheet_df(expression_name)
signature_df = kn.get_spreadsheet_df(signature_name )
samples_names = expression_df.columns
signatures_names = signature_df.columns
signatures_names = [i.split('.')[0] for i in signatures_names]
signature_df.columns = signatures_names
network_mat, unique_gene_names = kn.get_sparse_network_matrix(gg_network_name)
# network_mat = kn.normalize_sparse_mat_by_diagonal(network_mat)
expression_df = kn.update_spreadsheet_df(expression_df, unique_gene_names)
signature_df = kn.update_spreadsheet_df(signature_df, unique_gene_names)
expression_mat = expression_df.as_matrix()
signature_mat = signature_df.as_matrix()
expression_mat, iterations = kn.smooth_matrix_with_rwr(expression_mat, network_mat, run_parameters)
signature_mat, iterations = kn.smooth_matrix_with_rwr(signature_mat, network_mat, run_parameters)
expression_df.iloc[:] = expression_mat
signature_df.iloc[:] = signature_mat
if processing_method == 'serial':
for sample in range(0, number_of_bootstraps):
run_cc_similarity_signature_worker(expression_df, signature_df, run_parameters, sample)
elif processing_method == 'parallel':
find_and_save_cc_similarity_parallel(expression_df, signature_df, run_parameters, number_of_bootstraps)
else:
raise ValueError('processing_method contains bad value.')
# consensus_df = form_consensus_df(run_parameters, expression_df, signature_df)
similarity_df = assemble_similarity_df(expression_df, signature_df, run_parameters)
similarity_df = pd.DataFrame(similarity_df.values, index=samples_names, columns=signatures_names)
save_final_samples_signature(similarity_df, run_parameters)
save_best_match_signature(similarity_df, run_parameters)
kn.remove_dir(run_parameters["tmp_directory"])
def run_bootstrap_correlation(run_parameters):
""" perform gene prioritization using bootstrap sampling
Args:
run_parameters: parameter set dictionary.
"""
max_cpu = run_parameters["max_cpu"]
run_parameters["results_tmp_directory"] = kn.create_dir(
run_parameters["results_directory"], 'tmp')
results_tmp_directory = run_parameters["results_tmp_directory"]
n_bootstraps = run_parameters["number_of_bootstraps"]
results_tmp_directory = run_parameters["results_tmp_directory"]
phenotype_df = kn.get_spreadsheet_df(
run_parameters["phenotype_name_full_path"])
spreadsheet_df = kn.get_spreadsheet_df(
run_parameters["spreadsheet_name_full_path"])
phenotype_df = phenotype_df.T
#-----------------------------------------------------------------------------------------
# Partition the phenotype dataframe (partition size = MaxCPU)
#-----------------------------------------------------------------------------------------
len_phenotype = len(phenotype_df.index)
array_of_jobs = range(0, len_phenotype)
if (len_phenotype <= max_cpu):
jobs_id = array_of_jobs
number_of_jobs = len(jobs_id)
#-----------------------------------------------------------------------------------------
zipped_arguments = dstutil.zip_parameters(run_parameters,
spreadsheet_df, phenotype_df,
n_bootstraps, jobs_id)
dstutil.parallelize_processes_locally(run_bootstrap_correlation_worker,
zipped_arguments, number_of_jobs)
write_phenotype_data_all(run_parameters)
#-----------------------------------------------------------------------------------------
else:
for i in range(0, len_phenotype, max_cpu):
jobs_id = array_of_jobs[i:i + max_cpu]
number_of_jobs = len(jobs_id)
#-----------------------------------------------------------------------------------------
zipped_arguments = dstutil.zip_parameters(run_parameters,
spreadsheet_df, phenotype_df,
n_bootstraps, jobs_id)
dstutil.parallelize_processes_locally(run_bootstrap_correlation_worker,
zipped_arguments, number_of_jobs)
write_phenotype_data_all(run_parameters)
#-----------------------------------------------------------------------------------------
kn.remove_dir(results_tmp_directory)
def run_elastic_predict(run_parameters):
''' Using Elastic net model to predict response data against feature data
Args:
run_parameters: dictionary of run parameters
'''
gene_file = run_parameters['spreadsheet_name_full_path']
sign_file = run_parameters['response_name_full_path']
test_file = run_parameters['test_spreadsheet_name_full_path']
gene_df = kn.get_spreadsheet_df(gene_file)
sign_df = kn.get_spreadsheet_df(sign_file)
test_df = kn.get_spreadsheet_df(test_file)
row_names = test_df.columns
gene_mat = gene_df.values
sign_mat = sign_df.values[0]
test_mat = test_df.values
eps = run_parameters['eps']
min_alpha = run_parameters['min_alpha']
max_alpha = run_parameters['max_alpha']
n_alpha = run_parameters['n_alpha']
min_l1 = run_parameters['min_l1']
max_l1 = run_parameters['max_l1']
n_l1 = run_parameters['n_l1']
intercept = run_parameters['fit_intercept']
normalize = run_parameters['normalize']
max_iter = run_parameters['max_iter']
tolerance = run_parameters['tolerance']
alpha_grid = np.linspace(min_alpha, max_alpha, num=n_alpha)
l1_grid = np.linspace(min_l1, max_l1, num=n_l1)
reg_model = linear_model.ElasticNetCV(
l1_ratio=l1_grid, alphas=alpha_grid, fit_intercept=intercept, eps = eps,\
normalize=normalize, max_iter=max_iter, tol=tolerance, cv=5)
reg_model.fit(gene_mat.T, sign_mat)
filename = os.path.join(run_parameters['results_directory'],
'elastic_net_model.pkl')
pickle.dump(reg_model, open(filename, 'wb'))
response_predict = reg_model.predict(test_mat.T)
predict_df = pd.DataFrame(response_predict.T,
index=row_names,
columns=['predict'])
write_predict_data(predict_df, run_parameters)
def run_cc_similarity(run_parameters):
""" Performs similarity analysis with bootstraps and saves the similarity matrix.
Args:
run_parameters: parameter set dictionary.
"""
tmp_dir = 'tmp_cc_similarity'
run_parameters = update_tmp_directory(run_parameters, tmp_dir)
expression_name = run_parameters["spreadsheet_name_full_path"]
signature_name = run_parameters["signature_name_full_path"]
similarity_measure = run_parameters["similarity_measure"]
number_of_bootstraps = run_parameters['number_of_bootstraps']
processing_method = run_parameters['processing_method']
expression_df = kn.get_spreadsheet_df(expression_name)
signature_df = kn.get_spreadsheet_df(signature_name)
samples_names = expression_df.columns
signatures_names = signature_df.columns
signatures_names = [i.split('.')[0] for i in signatures_names]
signature_df.columns = signatures_names
expression_mat = expression_df.as_matrix()
signature_mat = signature_df.as_matrix()
if processing_method == 'serial':
for sample in range(0, number_of_bootstraps):
run_cc_similarity_signature_worker(expression_df, signature_df,
run_parameters, sample)
elif processing_method == 'parallel':
find_and_save_cc_similarity_parallel(expression_df, signature_df,
run_parameters,
number_of_bootstraps)
else:
raise ValueError('processing_method contains bad value.')
# consensus_df = form_consensus_df(run_parameters, expression_df, signature_df)
similarity_df = assemble_similarity_df(expression_df, signature_df,
run_parameters)
similarity_df = pd.DataFrame(similarity_df.values,
index=samples_names,
columns=signatures_names)
save_final_samples_signature(similarity_df, run_parameters)
save_best_match_signature(similarity_df, run_parameters)
kn.remove_dir(run_parameters["tmp_directory"])
def phenotype_expander(run_parameters):
""" Run phenotype expander on the whole dataframe of phenotype data.
Save the results to tsv file.
"""
phenotype_df = kn.get_spreadsheet_df(
run_parameters['phenotype_name_full_path'])
output_dict = run_pre_processing_phenotype_expander(
phenotype_df, run_parameters['threshold'])
result_df = pd.DataFrame(index=phenotype_df.index)
for key, df_list in output_dict.items():
if key == ColumnType.CATEGORICAL:
for item in df_list:
col_df = phenotype_df.loc[:, item.columns[0]].dropna()
uniq_array = np.unique(col_df.values)
col_names = [
item.columns[0] + '_' + str(i) for i in uniq_array
]
cur_df = pd.DataFrame(columns=col_names, index=col_df.index)
cur_append_df = pd.DataFrame(columns=col_names,
index=phenotype_df.index)
for i, val in enumerate(uniq_array):
cur_df.loc[col_df == val, col_names[i]] = 1
cur_df.loc[col_df != val, col_names[i]] = 0
cur_append_df.loc[cur_df.index, :] = cur_df
result_df = pd.concat([result_df, cur_append_df], axis=1)
file_name = kn.create_timestamped_filename("phenotype_expander_result",
"tsv")
file_path = os.path.join(run_parameters["results_directory"], file_name)
result_df.index.name = "sample_id"
result_df.to_csv(file_path, header=True, index=True, sep='\t', na_rep='NA')
def run_kaplan_meier(button):
""" callback for kaplan_meier_execute_button """
if get_km_file_button.file_selector.value == LIST_BOX_UPDATE_MESSAGE:
if get_km_file_button.description == 'Clear':
get_km_file_button.view_box.value = ''
get_km_file_button.view_box.description = ''
get_km_file_button.description = 'View'
refresh_files_list(get_km_file_button)
return
if button.description == 'Clear':
button.description = button.original_description
button.im_view_box.value = BLAK_IMAGE
button.view_box.value = ''
return
else:
button.description = 'Clear'
phenotype_df = kn.get_spreadsheet_df(
os.path.join(input_data_dir, get_km_file_button.file_selector.value))
cluster_id_name = button.cluster_id_listbox.value
event_name = button.event_id_listbox.value
time_name = button.time_id_listbox.value
disp_kaplan_meier(phenotype_df, cluster_id_name, event_name, time_name,
button)
def run_merge_df(run_parameters):
""" Merge two phenotype matrices that correspond to same columns (Union)
Args: run_parameters with keys:
"results_directory", "spreadsheet_1_file_name", "spreadsheet_2_file_name"
"""
results_directory = run_parameters['results_directory']
spreadsheet_1_file_name = run_parameters['spreadsheet_1_file_name']
spreadsheet_2_file_name = run_parameters['spreadsheet_2_file_name']
spreadsheet_1_df = kn.get_spreadsheet_df(spreadsheet_1_file_name)
spreadsheet_2_df = kn.get_spreadsheet_df(spreadsheet_2_file_name)
result_df = merge_df(spreadsheet_1_df, spreadsheet_2_df)
transform_name = "merge"
write_transform_df(result_df, spreadsheet_1_file_name, transform_name,
results_directory)
def run_link_hclust(run_parameters):
#-----------------------------------------------------
""" wrapper: call sequence to perform hierchical clustering using linkage and save the results.
Args:
run_parameters: parameter set dictionary.
"""
np.random.seed()
nearest_neighbors = run_parameters['nearest_neighbors']
number_of_clusters = run_parameters['number_of_clusters']
affinity_metric = run_parameters['affinity_metric']
linkage_criterion = run_parameters['linkage_criterion']
spreadsheet_name_full_path = run_parameters['spreadsheet_name_full_path']
spreadsheet_df = kn.get_spreadsheet_df(spreadsheet_name_full_path)
spreadsheet_mat = spreadsheet_df.values
number_of_samples = spreadsheet_mat.shape[1]
labels, distance_matrix = perform_link_hclust(spreadsheet_mat,
number_of_clusters,
nearest_neighbors,
affinity_metric,
linkage_criterion)
sample_names = spreadsheet_df.columns
save_clustering_scores(distance_matrix, sample_names, labels,
run_parameters)
save_final_samples_clustering(sample_names, labels, run_parameters)
save_spreadsheet_and_variance_heatmap(spreadsheet_df, labels,
run_parameters)
return labels
def run_kmeans(run_parameters):
#-----------------------------------------------------
""" wrapper: call sequence to perform kmeans clustering and save the results.
Args:
run_parameters: parameter set dictionary.
"""
number_of_clusters = run_parameters['number_of_clusters']
spreadsheet_name_full_path = run_parameters['spreadsheet_name_full_path']
spreadsheet_df = kn.get_spreadsheet_df(spreadsheet_name_full_path)
spreadsheet_mat_T = spreadsheet_df.values.T
number_of_samples = spreadsheet_mat_T.shape[0]
distance_matrix = pairwise_distances(spreadsheet_mat_T)
labels = kn.perform_kmeans(spreadsheet_mat_T, number_of_clusters)
sample_names = spreadsheet_df.columns
save_clustering_scores(distance_matrix, sample_names, labels,
run_parameters)
save_final_samples_clustering(sample_names, labels, run_parameters)
save_spreadsheet_and_variance_heatmap(spreadsheet_df, labels,
run_parameters)
return labels
def run_nmf(run_parameters):
""" wrapper: call sequence to perform non-negative matrix factorization and write results.
Args:
run_parameters: parameter set dictionary.
"""
number_of_clusters = run_parameters['number_of_clusters']
spreadsheet_name_full_path = run_parameters['spreadsheet_name_full_path']
spreadsheet_df = kn.get_spreadsheet_df(spreadsheet_name_full_path)
spreadsheet_mat = spreadsheet_df.as_matrix()
spreadsheet_mat = kn.get_quantile_norm_matrix(spreadsheet_mat)
h_mat = kn.perform_nmf(spreadsheet_mat, run_parameters)
linkage_matrix = np.zeros(
(spreadsheet_mat.shape[1], spreadsheet_mat.shape[1]))
sample_perm = np.arange(0, spreadsheet_mat.shape[1])
linkage_matrix = kn.update_linkage_matrix(h_mat, sample_perm,
linkage_matrix)
labels = kn.perform_kmeans(linkage_matrix, number_of_clusters)
sample_names = spreadsheet_df.columns
save_consensus_clustering(linkage_matrix, sample_names, labels,
run_parameters)
save_final_samples_clustering(sample_names, labels, run_parameters)
save_spreadsheet_and_variance_heatmap(spreadsheet_df, labels,
run_parameters)
def reset_phenotype_cols_list(change):
""" Reset the three parameters dropdown listboxes to a new file selection.
Args:
change: IPywidgets widget control change event
"""
if get_km_file_button.file_selector.value == LIST_BOX_UPDATE_MESSAGE:
if get_km_file_button.description == 'Clear':
get_km_file_button.view_box.value = ''
get_km_file_button.view_box.description = ''
get_km_file_button.description = 'View'
refresh_files_list(get_km_file_button)
return
options_df = kn.get_spreadsheet_df(
os.path.join(input_data_dir, get_km_file_button.file_selector.value))
sorted_options_list = sorted(list(options_df.columns.values))
if len(sorted_options_list) > 0:
def_val = sorted_options_list[0]
else:
def_val = ''
cluster_id_listbox.options = sorted_options_list
cluster_id_listbox.value = def_val
event_id_listbox.options = sorted_options_list
event_id_listbox.value = def_val
time_id_listbox.options = sorted_options_list
time_id_listbox.value = def_val
def combine_phenotype_data_and_clustering(run_parameters):
"""This is to insert the sample clusters column into the phenotype dataframe.
Returns:
phenotype_df: phenotype dataframe with the first column as sample clusters.
"""
phenotype_df = kn.get_spreadsheet_df(
run_parameters['phenotype_name_full_path'])
phenotype_df.insert(0, 'Cluster_ID', np.nan) # pylint: disable=no-member
cluster_labels_df = pd.read_csv(
run_parameters['cluster_mapping_full_path'],
index_col=0,
header=None,
sep='\t')
cluster_labels_df.columns = ['Cluster_ID']
common_samples = kn.find_common_node_names(phenotype_df.index,
cluster_labels_df.index)
phenotype_df.loc[common_samples,
'Cluster_ID'] = cluster_labels_df.loc[common_samples,
'Cluster_ID'] # pylint: disable=no-member
return phenotype_df
def run_GRN_lasso(run_parameters):
"""
"""
spreadsheet = kn.get_spreadsheet_df(
run_parameters['spreadsheet_name_full_path'])
gene_list = spreadsheet.index
tf_idx = range(int(spreadsheet.shape[0] * 0.2))
tf_spreadsheet = spreadsheet.iloc[tf_idx, :]
result_df = pd.DataFrame(index=gene_list, columns=tf_spreadsheet.index)
param_dict = {
'n_alphas': 1000,
'fit_intercept': run_parameters['fit_intercept'],
'normalize': run_parameters['normalize'],
'max_iter': 2000,
'cv': 5
}
for i in range(spreadsheet.shape[0]):
# curr_response = spreadsheet.values[i, :].reshape(-1,1)
curr_response = spreadsheet.values[i, :].ravel()
curr_model = algo_lasso(tf_spreadsheet.values.T, curr_response,
param_dict)
coef = curr_model.coef_.ravel()
# (x-min(x))/(max(x)-min(x))
result_df.loc[gene_list[i], :] = (coef - min(coef)) / (max(coef) -
min(coef))
file_path = os.path.join(run_parameters['results_directory'],
'GRN_coefficient_result.tsv')
result_df.to_csv(file_path, header=True, index=True, sep='\t')
def run_net_similarity(run_parameters):
""" Run random walk first to smooth expression and signature
then perform similarity analysis and save the similarity matrix.
Args:
run_parameters: parameter set dictionary.
"""
expression_name = run_parameters["spreadsheet_name_full_path"]
signature_name = run_parameters["signature_name_full_path"]
gg_network_name = run_parameters['gg_network_name_full_path']
similarity_measure = run_parameters["similarity_measure"]
expression_df = kn.get_spreadsheet_df(expression_name)
signature_df = kn.get_spreadsheet_df(signature_name)
samples_names = expression_df.columns
signatures_names = signature_df.columns
signatures_names = [i.split('.')[0] for i in signatures_names]
signature_df.columns = signatures_names
network_mat, unique_gene_names = kn.get_sparse_network_matrix(
gg_network_name)
# network_mat = kn.normalize_sparse_mat_by_diagonal(network_mat)
expression_df = kn.update_spreadsheet_df(expression_df, unique_gene_names)
signature_df = kn.update_spreadsheet_df(signature_df, unique_gene_names)
expression_mat = expression_df.as_matrix()
signature_mat = signature_df.as_matrix()
expression_mat, iterations = kn.smooth_matrix_with_rwr(
expression_mat, network_mat, run_parameters)
signature_mat, iterations = kn.smooth_matrix_with_rwr(
signature_mat, network_mat, run_parameters)
expression_df.iloc[:] = expression_mat
signature_df.iloc[:] = signature_mat
similarity_mat = generate_similarity_mat(expression_df, signature_df,
similarity_measure)
# similarity_mat = map_similarity_range(similarity_mat, 0)
similarity_df = pd.DataFrame(similarity_mat,
index=samples_names,
columns=signatures_names)
save_final_samples_signature(similarity_df, run_parameters)
save_best_match_signature(similarity_df, run_parameters)
def run_fisher(run_parameters):
''' wrapper: call sequence to perform fisher gene-set characterization
Args:
run_parameters: dictionary of run parameters
'''
# -----------------------------------
# - Data read and extraction Section -
# -----------------------------------
spreadsheet_df = kn.get_spreadsheet_df(
run_parameters['spreadsheet_name_full_path'])
prop_gene_network_df = kn.get_network_df(
run_parameters['pg_network_name_full_path'])
spreadsheet_gene_names = kn.extract_spreadsheet_gene_names(spreadsheet_df)
prop_gene_network_n1_names, \
prop_gene_network_n2_names = kn.extract_network_node_names(prop_gene_network_df)
# -----------------------------------------------------------------------
# - limit the gene set to the intersection of network and user gene set -
# -----------------------------------------------------------------------
common_gene_names = kn.find_common_node_names(prop_gene_network_n2_names,
spreadsheet_gene_names)
common_gene_names_dict = kn.create_node_names_dict(common_gene_names)
prop_gene_network_n1_names_dict = kn.create_node_names_dict(
prop_gene_network_n1_names)
reverse_prop_dict = kn.create_reverse_node_names_dict(
prop_gene_network_n1_names_dict)
# ----------------------------------------------------------------------------
# - restrict spreadsheet and network to common genes and drop everthing else -
# ----------------------------------------------------------------------------
new_spreadsheet_df = kn.update_spreadsheet_df(spreadsheet_df,
common_gene_names)
prop_gene_network_df = kn.update_network_df(prop_gene_network_df,
common_gene_names, "node_2")
prop_gene_network_df['wt'] = 1
# ----------------------------------------------------------------------------
# - map every gene name to an integer index in sequential order startng at 0 -
# ----------------------------------------------------------------------------
prop_gene_network_df = kn.map_node_names_to_index(
prop_gene_network_df, prop_gene_network_n1_names_dict, "node_1")
prop_gene_network_df = kn.map_node_names_to_index(prop_gene_network_df,
common_gene_names_dict,
"node_2")
# --------------------------------------------
# - store the network in a csr sparse format -
# --------------------------------------------
universe_count = len(common_gene_names)
prop_gene_network_sparse = kn.convert_network_df_to_sparse(
prop_gene_network_df, universe_count, len(prop_gene_network_n1_names))
fisher_contingency_pval = get_fisher_exact_test(prop_gene_network_sparse,
reverse_prop_dict,
new_spreadsheet_df)
fisher_final_result = save_fisher_test_result(
fisher_contingency_pval, run_parameters['results_directory'],
spreadsheet_df.columns.values, 2)
map_and_save_droplist(spreadsheet_df, common_gene_names, 'fisher_droplist',
run_parameters)
return fisher_final_result
def run_lasso_predict(run_parameters):
gene_samples_train_df = kn.get_spreadsheet_df(
run_parameters['spreadsheet_name_full_path'])
response_train_df = kn.get_spreadsheet_df(
run_parameters['response_name_full_path'])
gene_samples_test_df = kn.get_spreadsheet_df(
run_parameters['test_spreadsheet_name_full_path'])
response_test_sample_names = list(gene_samples_test_df.columns)
reg_moE = linear_model.Lasso()
response_predict = reg_moE.fit(gene_samples_train_df.transpose().values,
response_train_df.values[0]).predict(
gene_samples_test_df.transpose().values)
predict_df = pd.DataFrame(response_predict.T,
index=response_test_sample_names,
columns=['predict'])
write_predict_data(predict_df, run_parameters)
def run_net_similarity(run_parameters):
""" Run random walk first to smooth expression and signature
then perform similarity analysis and save the similarity matrix.
Args:
run_parameters: parameter set dictionary.
"""
expression_name = run_parameters["spreadsheet_name_full_path"]
signature_name = run_parameters["signature_name_full_path" ]
gg_network_name = run_parameters['gg_network_name_full_path' ]
similarity_measure = run_parameters["similarity_measure" ]
expression_df = kn.get_spreadsheet_df(expression_name)
signature_df = kn.get_spreadsheet_df( signature_name)
expression_col_names = expression_df.columns
signature_col_names = signature_df.columns
#---------------------
network_mat, \
unique_gene_names = kn.get_sparse_network_matrix(gg_network_name)
expression_df = kn.update_spreadsheet_df(expression_df, unique_gene_names)
signature_df = kn.update_spreadsheet_df( signature_df, unique_gene_names)
#---------------------
expression_mat = expression_df.values
signature_mat = signature_df.values
expression_mat, \
iterations = kn.smooth_matrix_with_rwr(expression_mat, network_mat, run_parameters)
signature_mat, \
iterations = kn.smooth_matrix_with_rwr( signature_mat, network_mat, run_parameters)
expression_df.iloc[:] = expression_mat
signature_df.iloc [:] = signature_mat
# ---------------------------------------------
similarity_mat = generate_similarity_mat(expression_df, signature_df,similarity_measure)
# ---------------------------------------------
similarity_df = pd.DataFrame( similarity_mat, index = expression_col_names, columns = signature_col_names )
save_final_expression_signature( similarity_df, run_parameters )
save_best_match_signature ( similarity_df, run_parameters )
def view_spreadsheet_file_head(full_file_name):
""" notebook convenience """
if os.path.isfile(full_file_name):
sp_df = kn.get_spreadsheet_df(full_file_name)
deNada, f_name = os.path.split(full_file_name)
print(f_name, ' size:', sp_df.shape)
display(sp_df.head(10))
else:
print('file not found on local path')
def run_svr_predict(run_parameters):
''' Using SVR model to predict response data against feature data
Args:
run_parameters: dictionary of run parameters
'''
gene_file = run_parameters['spreadsheet_name_full_path']
sign_file = run_parameters['response_name_full_path']
test_file = run_parameters['test_spreadsheet_name_full_path']
gene_df = kn.get_spreadsheet_df(gene_file)
sign_df = kn.get_spreadsheet_df(sign_file)
test_df = kn.get_spreadsheet_df(test_file)
row_names = test_df.columns
gene_mat = gene_df.values
sign_mat = sign_df.values[0]
test_mat = test_df.values
svr_kernel = run_parameters['svr_kernel']
p_grid = {'svr_degree': 3, 'svr_gamma': 'auto', 'svr_coef0': 0.0, \
'svr_tol': 0.001, 'svr_C': 1.0, 'svr_epsilon': 0.1, \
'svr_shrinking': True, 'svr_cache_size':200, \
'svr_verbose': False, 'svr_max_iter': -1}
for k, v in p_grid.items():
if k in run_parameters:
p_grid[k] = v
reg_model = SVR(kernel=svr_kernel, degree=p_grid['svr_degree'], gamma=p_grid['svr_gamma'], \
coef0=p_grid['svr_coef0'], tol=p_grid['svr_tol'], C=p_grid['svr_C'], epsilon=p_grid['svr_epsilon'], \
shrinking=p_grid['svr_shrinking'], cache_size=p_grid['svr_cache_size'], verbose=p_grid['svr_verbose'], \
max_iter=p_grid['svr_max_iter'])
reg_model.fit(gene_mat.T, sign_mat)
filename = os.path.join(run_parameters['results_directory'],
'svr_model.pkl')
pickle.dump(reg_model, open(filename, 'wb'))
response_predict = reg_model.predict(test_mat.T)
predict_df = pd.DataFrame(response_predict.T,
index=row_names,
columns=['predict'])
write_predict_data(predict_df, run_parameters)
def run_DRaWR(run_parameters):
''' wrapper: call sequence to perform random walk with restart
Args:
run_parameters: dictionary of run parameters
'''
network_sparse, unique_gene_names, \
pg_network_n1_names = build_hybrid_sparse_matrix(run_parameters, True, True)
unique_all_node_names = unique_gene_names + pg_network_n1_names
spreadsheet_df = kn.get_spreadsheet_df(
run_parameters['spreadsheet_name_full_path'])
new_spreadsheet_df = kn.update_spreadsheet_df(spreadsheet_df,
unique_all_node_names)
unique_genes_length = len(unique_gene_names)
property_length = len(set(pg_network_n1_names))
base_col = np.append(np.ones(unique_genes_length, dtype=np.int),
np.zeros(property_length, dtype=np.int))
new_spreadsheet_df = kn.append_column_to_spreadsheet(
new_spreadsheet_df, base_col, 'base')
hetero_network = normalize(network_sparse, norm='l1', axis=0)
final_spreadsheet_matrix, step = kn.smooth_matrix_with_rwr(
normalize(new_spreadsheet_df, norm='l1', axis=0), hetero_network,
run_parameters)
final_spreadsheet_df = pd.DataFrame(final_spreadsheet_matrix)
final_spreadsheet_df.index = new_spreadsheet_df.index.values
final_spreadsheet_df.columns = new_spreadsheet_df.columns.values
prop_spreadsheet_df = rank_drawr_property(final_spreadsheet_df,
pg_network_n1_names)
spreadsheet_df_mask = final_spreadsheet_df.loc[
final_spreadsheet_df.index.isin(spreadsheet_df.index)]
gene_result_df = construct_drawr_result_df(spreadsheet_df_mask, 0,
spreadsheet_df_mask.shape[0],
True, run_parameters)
prop_result_df = construct_drawr_result_df(final_spreadsheet_df,
unique_genes_length,
final_spreadsheet_df.shape[0],
False, run_parameters)
save_timestamped_df(prop_spreadsheet_df,
run_parameters['results_directory'],
'DRaWR_ranked_by_property')
save_timestamped_df(gene_result_df, run_parameters['results_directory'],
'DRaWR_sorted_by_gene_score')
save_timestamped_df(prop_result_df, run_parameters['results_directory'],
'DRaWR_sorted_by_property_score')
map_and_save_droplist(spreadsheet_df, unique_gene_names, 'DRaWR_droplist',
run_parameters)
return prop_spreadsheet_df
def run_bootstrap_correlation(run_parameters):
""" perform feature prioritization using bootstrap sampling
Args:
run_parameters: parameter set dictionary.
"""
run_parameters["results_tmp_directory"] = kn.create_dir(run_parameters["results_directory"], 'tmp')
phenotype_df = kn.get_spreadsheet_df(run_parameters["phenotype_name_full_path"])
spreadsheet_df = kn.get_spreadsheet_df(run_parameters["spreadsheet_name_full_path"])
phenotype_df = phenotype_df.T
n_bootstraps = run_parameters["number_of_bootstraps"]
number_of_jobs = len(phenotype_df.index)
jobs_id = range(0, number_of_jobs)
zipped_arguments = dstutil.zip_parameters(run_parameters, spreadsheet_df, phenotype_df, n_bootstraps, jobs_id)
dstutil.parallelize_processes_locally(run_bootstrap_correlation_worker, zipped_arguments, number_of_jobs)
write_phenotype_data_all(run_parameters)
kn.remove_dir(run_parameters["results_tmp_directory"])
def run_spreadsheet_numerical_transform(run_parameters):
""" numerical transformation of dataframe
Args: run_parameters with keys:
"results_directory", "spreadsheet_file_name", "numeric_function", (with corresponding options):
(z_transform_axis, z_transform_ddof)
(log_transform_log_base, log_transform_log_offset_
(threshold_cut_off, threshold_substitution_value, threshold_scope)
"""
results_directory = run_parameters['results_directory']
spreadsheet_name_full_path = run_parameters['spreadsheet_name_full_path']
numeric_function = run_parameters['numeric_function']
spreadsheet_df = kn.get_spreadsheet_df(spreadsheet_name_full_path)
if numeric_function == 'abs':
spreadsheet_df = abs_df(spreadsheet_df)
transform_name = "absolute_value"
elif numeric_function == 'z_transform':
z_transform_axis = run_parameters['z_transform_axis']
z_transform_ddof = run_parameters['z_transform_ddof']
spreadsheet_df = z_transform_df(spreadsheet_df,
axis=z_transform_axis,
ddof=z_transform_ddof)
transform_name = 'z_transform'
elif numeric_function == 'log_transform':
log_transform_log_base = run_parameters['log_transform_log_base']
if log_transform_log_base == "e":
log_transform_log_base = np.exp(1)
log_transform_log_offset = run_parameters['log_transform_log_offset']
spreadsheet_df = log_transform_df(spreadsheet_df,
log_base=log_transform_log_base,
log_offset=log_transform_log_offset)
transform_name = 'log_transform'
elif numeric_function == 'threshold':
threshold_cut_off = run_parameters['threshold_cut_off']
threshold_substitution_value = run_parameters[
'threshold_substitution_value']
threshold_scope = run_parameters['threshold_scope']
spreadsheet_df = threshold_df(spreadsheet_df,
cut_off=threshold_cut_off,
sub_val=threshold_substitution_value,
scope=threshold_scope)
transform_name = 'threshold'
else:
return
write_transform_df(spreadsheet_df, spreadsheet_name_full_path,
transform_name, results_directory)
def run_common_samples_df(run_parameters):
""" Make two spreadsheets consistent by samples: two new spreadsheets created
with samples being the intersection of sample sets of given spreadsheets.
Args: run_parameters with keys:
"results_directory", "spreadsheet_1_file_name", "spreadsheet_2_file_name"
"""
results_directory = run_parameters['results_directory']
spreadsheet_1_file_name = run_parameters['spreadsheet_1_file_name']
spreadsheet_2_file_name = run_parameters['spreadsheet_2_file_name']
spreadsheet_1_df = kn.get_spreadsheet_df(spreadsheet_1_file_name)
spreadsheet_2_df = kn.get_spreadsheet_df(spreadsheet_2_file_name)
spreadsheet_1_df, spreadsheet_2_df = common_samples_df(
spreadsheet_1_df, spreadsheet_2_df)
transform_name = "common_samples"
write_transform_df(spreadsheet_1_df, spreadsheet_1_file_name,
transform_name, results_directory)
write_transform_df(spreadsheet_2_df, spreadsheet_2_file_name,
transform_name, results_directory)
def run_cc_nmf(run_parameters):
""" wrapper: call sequence to perform non-negative matrix factorization with
consensus clustering and write results.
Args:
run_parameters: parameter set dictionary.
"""
tmp_dir = 'tmp_cc_nmf'
run_parameters = update_tmp_directory(run_parameters, tmp_dir)
processing_method = run_parameters['processing_method']
number_of_bootstraps = run_parameters['number_of_bootstraps']
number_of_clusters = run_parameters['number_of_clusters']
spreadsheet_name_full_path = run_parameters['spreadsheet_name_full_path']
spreadsheet_df = kn.get_spreadsheet_df(spreadsheet_name_full_path)
spreadsheet_mat = spreadsheet_df.as_matrix()
spreadsheet_mat = kn.get_quantile_norm_matrix(spreadsheet_mat)
number_of_samples = spreadsheet_mat.shape[1]
if processing_method == 'serial':
for sample in range(0, number_of_bootstraps):
run_cc_nmf_clusters_worker(spreadsheet_mat, run_parameters, sample)
elif processing_method == 'parallel':
find_and_save_cc_nmf_clusters_parallel(spreadsheet_mat, run_parameters,
number_of_bootstraps)
elif processing_method == 'distribute':
func_args = [spreadsheet_mat, run_parameters]
dependency_list = [
run_cc_nmf_clusters_worker, save_a_clustering_to_tmp,
dstutil.determine_parallelism_locally
]
dstutil.execute_distribute_computing_job(
run_parameters['cluster_ip_address'], number_of_bootstraps,
func_args, find_and_save_cc_nmf_clusters_parallel, dependency_list)
else:
raise ValueError('processing_method contains bad value.')
consensus_matrix = form_consensus_matrix(run_parameters, number_of_samples)
labels = kn.perform_kmeans(consensus_matrix, number_of_clusters)
sample_names = spreadsheet_df.columns
save_consensus_clustering(consensus_matrix, sample_names, labels,
run_parameters)
save_final_samples_clustering(sample_names, labels, run_parameters)
save_spreadsheet_and_variance_heatmap(spreadsheet_df, labels,
run_parameters)
kn.remove_dir(run_parameters["tmp_directory"])
