def run_correlation_worker(run_parameters, spreadsheet_df, phenotype_df,
job_id):
""" core function for parallel run_correlation
Args:
run_parameters: dict of parameters
spreadsheet_df: spreadsheet data frame
phenotype_df: phenotype data frame
job_id: parallel iteration number
"""
# selects the ith row in phenotype_df
np.random.seed(job_id)
phenotype_df = phenotype_df.iloc[[job_id], :]
spreadsheet_df, phenotype_df, msg = datacln.check_input_value_for_gene_prioritazion(
spreadsheet_df, phenotype_df)
pc_array = get_correlation(spreadsheet_df.values, phenotype_df.values[0],
run_parameters)
feature_name_list = spreadsheet_df.index
phenotype_name = phenotype_df.index.values[0]
generate_correlation_output(pc_array, phenotype_name, feature_name_list,
run_parameters)
def run_bootstrap_net_correlation_worker(run_parameters, spreadsheet_df, phenotype_df, network_mat,
spreadsheet_genes_as_input, baseline_array, job_id):
""" worker for bootstrap network parallelization.
Args:
run_parameters: dict of parameters
spreadsheet_df: spreadsheet data frame
phenotype_df: phenotype data frame
network_mat: adjacency matrix
spreadsheet_genes_as_input: list of genes
baseline_array: network smooted baseline array
job_id: parallel iteration number
"""
np.random.seed(job_id)
n_bootstraps = run_parameters["number_of_bootstraps" ]
cols_sampling_fraction = run_parameters["cols_sampling_fraction"]
top_beta_of_sort = run_parameters["top_beta_of_sort" ]
restart_accumulator = np.zeros(network_mat.shape[0])
gm_accumulator = np.ones(network_mat.shape[0])
borda_count = np.zeros(network_mat.shape[0])
phenotype_df = phenotype_df.iloc[[job_id], :]
spreadsheet_df ,\
phenotype_df ,\
msg = datacln.check_input_value_for_gene_prioritazion(spreadsheet_df, phenotype_df)
sample_smooth = spreadsheet_df.as_matrix()
pearson_array = get_correlation(sample_smooth, phenotype_df.values[0], run_parameters)
for bootstrap_number in range(0, n_bootstraps):
sample_random , sample_permutation = sample_a_matrix_pearson(sample_smooth, 1.0, cols_sampling_fraction)
phenotype_response = phenotype_df.values[0, None]
phenotype_response = phenotype_response[0, sample_permutation]
pc_array = get_correlation(sample_random, phenotype_response, run_parameters)
mask = np.in1d(spreadsheet_df.index, spreadsheet_genes_as_input)
pc_array[~mask] = 0.0
pc_array = np.abs(trim_to_top_beta(pc_array, top_beta_of_sort))
restart_accumulator[pc_array != 0] += 1.0
pc_array = pc_array / max( sum(pc_array), EPSILON_0 )
pc_array = kn.smooth_matrix_with_rwr(pc_array, network_mat, run_parameters)[0]
pc_array = pc_array - baseline_array
borda_count = sum_array_ranking_to_borda_count(borda_count, pc_array)
gm_accumulator = (np.abs(pc_array) + EPSILON_0) * gm_accumulator
restart_accumulator = restart_accumulator / n_bootstraps
borda_count = borda_count / n_bootstraps
viz_score = (borda_count - min(borda_count)) / (max(borda_count) - min(borda_count))
phenotype_name = phenotype_df.index.values[0]
gene_name_list = spreadsheet_df.index
gene_orig_list = spreadsheet_genes_as_input
quantitative_score = borda_count
generate_net_correlation_output(pearson_array, quantitative_score, viz_score, restart_accumulator,
phenotype_name, gene_name_list, gene_orig_list, run_parameters)
def run_net_correlation_worker(run_parameters, spreadsheet_df, phenotype_df,
network_mat, spreadsheet_genes_as_input,
baseline_array, job_id):
""" core function for parallel run_net_correlation
Args:
run_parameters: dict of parameters
spreadsheet_df: spreadsheet data frame
phenotype_df: phenotype data frame
network_mat: adjacency matrix
spreadsheet_genes_as_input: list of genes
baseline_array: network smooted baseline array
job_id: parallel iteration number
"""
np.random.seed(job_id)
phenotype_df = phenotype_df.iloc[[job_id], :]
spreadsheet_df ,\
phenotype_df ,\
msg = datacln.check_input_value_for_gene_prioritazion(spreadsheet_df, phenotype_df)
sample_smooth = spreadsheet_df.values
pc_array = get_correlation(sample_smooth, phenotype_df.values[0],
run_parameters)
pearson_array = pc_array.copy()
mask = np.in1d(spreadsheet_df.index, spreadsheet_genes_as_input)
pc_array[~mask] = 0.0
pc_array = np.abs(
trim_to_top_beta(pc_array, run_parameters["top_beta_of_sort"]))
restart_accumulator = pc_array.copy()
restart_accumulator[restart_accumulator != 0] = 1
pc_array = pc_array / max(sum(pc_array), EPSILON_0)
pc_array = kn.smooth_matrix_with_rwr(pc_array, network_mat,
run_parameters)[0]
pc_array = pc_array - baseline_array
quantitative_score = pc_array
viz_score = (pc_array - min(pc_array)) / (max(pc_array) - min(pc_array))
phenotype_name = phenotype_df.index.values[0]
gene_name_list = spreadsheet_df.index
gene_orig_list = spreadsheet_genes_as_input
generate_net_correlation_output(pearson_array, quantitative_score,
viz_score, restart_accumulator,
phenotype_name, gene_name_list,
gene_orig_list, run_parameters)
def run_bootstrap_correlation_worker(run_parameters, spreadsheet_df,
phenotype_df, n_bootstraps, job_id):
""" core function for parallel run_bootstrap_correlation
Args:
run_parameters: dict of parameters
spreadsheet_df: spreadsheet data frame
phenotype_df: phenotype data frame
n_bootstraps: number of bootstrap samples to use
job_id: parallel iteration number
"""
np.random.seed(job_id)
phenotype_df = phenotype_df.iloc[[job_id], :]
spreadsheet_df ,\
phenotype_df ,\
msg = datacln.check_input_value_for_gene_prioritazion(spreadsheet_df, phenotype_df)
pearson_array = get_correlation(spreadsheet_df.values,
phenotype_df.values[0], run_parameters)
borda_count = np.zeros(spreadsheet_df.shape[0])
gm_accumulator = np.ones(spreadsheet_df.shape[0])
for bootstrap_number in range(0, n_bootstraps):
sample_random, sample_permutation = sample_a_matrix_pearson(
spreadsheet_df.values, 1.0,
run_parameters["cols_sampling_fraction"])
phenotype_response = phenotype_df.values[0, None]
phenotype_response = phenotype_response[0, sample_permutation]
pc_array = get_correlation(sample_random, phenotype_response,
run_parameters)
borda_count = sum_array_ranking_to_borda_count(borda_count,
np.abs(pc_array))
gm_accumulator = (np.abs(pc_array) + EPSILON_0) * gm_accumulator
pcc_gm_array = gm_accumulator**(1 / n_bootstraps)
borda_count = borda_count / n_bootstraps
phenotype_name = phenotype_df.index.values[0]
gene_name_list = spreadsheet_df.index
viz_score = (borda_count - min(borda_count)) / (max(borda_count) -
min(borda_count))
generate_bootstrap_correlation_output(borda_count, viz_score,
pearson_array, phenotype_name,
gene_name_list, run_parameters)
