def score1C(sim, data_df):
    sim_w = np.unique(sim.U, return_counts=True)[1] / sim.N

    est_w = data_df.groupby('cluster_id').vaf_purity.count().values / len(
        data_df)
    est_phi = data_df.groupby('cluster_id').vaf_purity.mean().values
    return score1C_base(sim.phi, est_phi, sim_w, est_w)
Exemple #2
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def score1C(sim, cluster_table):
    sim_w = np.unique(sim.U, return_counts=True)[1] / sim.N

    est_w = cluster_table['Cluster_Assignment'] / cluster_table[
        'Cluster_Assignment'].sum()
    est_phi = cluster_table['postDP_ccf_mean']
    return score1C_base(sim.phi, est_phi, sim_w, est_w)
def score1C(sim, est):
    sim_w = np.unique(sim.U, return_counts=True)[1] / sim.N
    pre_est_w = np.zeros(est.J)
    pre_counts = np.unique(np.argmax(est.qun, axis=1), return_counts=True)
    pre_est_w[pre_counts[0]] = pre_counts[1]
    est_w = pre_est_w / len(sim.U)
    return score1C_base(sim.phi, est.phi, sim_w, est_w)
def score1C(sim, loci_table):
    sim_w = np.unique(sim.U, return_counts=True)[1] / sim.N
    est_w = loci_table[loci_table.cluster > 0].groupby(
        'cluster')['tumor.vaf'].count() / len(loci_table)
    est_phi = loci_table[loci_table.cluster > 0].groupby(
        'cluster')['tumor.vaf'].mean() / 100
    return score1C_base(sim.phi, est_phi, sim_w, est_w)
Exemple #5
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def score1C(sim, cluster_table):
    sim_w = np.unique(sim.U, return_counts=True)[1] / sim.N

    est_w = cluster_table['no.of.mutations'] / cluster_table[
        'no.of.mutations'].sum()
    est_phi = cluster_table['location']
    return score1C_base(sim.phi, est_phi, sim_w, est_w)
def score1C(sim, loci_table):
    sim_w = np.unique(sim.U, return_counts=True)[1]/sim.N
    pre_cluster_table = loci_table.groupby('ccube_ccf_mean').rough_mult.count()
    est_w = pre_cluster_table.values/len(loci_table)
    est_phi = pre_cluster_table.index.values
    return score1C_base(sim.phi, est_phi, sim_w, est_w)
 row_list.append(folder_path.split('_')[-1])
 row_list.append(info_df.median_depth.mean())
 (ll_ratio, pval, J_pred, phi_pred_values, weights_pred,
  pred_cluster_assign, pred_subclonal, pred_profile, pred_signatures,
  pred_signatures_mut, est_dist, runtime) = \
     method_function_dict[method](folder_path)
 row_list.append(J_pred)
 row_list.append(ll_ratio)
 row_list.append(pval)
 if J_pred is not None:
     row_list.append(score1B_base(J_true, J_pred))
 else:
     row_list.append(np.nan)
 if phi_pred_values is not None:
     row_list.append(
         score1C_base(phi_true_values, phi_pred_values, weights_true,
                      weights_pred))
 else:
     row_list.append(np.nan)
 if pred_cluster_assign is not None:
     ordered_table = pd.merge(pred_cluster_assign,
                              true_cluster_assign,
                              on='mutation_id',
                              how='inner')
     if len(true_cluster_assign) < 20000:
         row_list.append(
             score2A_base(ordered_table.true_cluster_id,
                          ordered_table.pred_cluster_id))
     else:
         row_list.append(np.nan)
     ordered_table = pd.merge(pred_subclonal,
                              true_subclonal,
Exemple #8
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def score1C(sim, cluster_table):
    sim_w = np.unique(sim.U, return_counts=True)[1] / sim.N

    est_w = cluster_table['size'] / cluster_table['size'].sum()
    est_phi = cluster_table['mean']
    return score1C_base(sim.phi, est_phi, sim_w, est_w)
Exemple #9
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def score1C(sim, data_df):
    sim_w = np.unique(sim.U, return_counts=True)[1]/sim.N

    est_w = data_df.groupby('Clonality').CCF.count().values/len(data_df)
    est_phi = data_df.groupby('Clonality').CCF.mean().values
    return score1C_base(sim.phi, est_phi, sim_w, est_w)
 row_list.append(info_df.perc_dip.mean())
 row_list.append(folder_path.split('_')[-1])
 row_list.append(info_df.median_depth.mean())
 (ll_ratio, pval, J_pred, phi_pred_values, weights_pred,
  pred_cluster_assign, pred_subclonal, pred_profile, pred_signatures,
  pred_signatures_mut, est_dist, runtime) = \
     method_function_dict[method](folder_path)
 row_list.append(J_pred)
 row_list.append(ll_ratio)
 row_list.append(pval)
 if J_pred is not None:
     row_list.append(score1B_base(J_true, J_pred))
 else:
     row_list.append(np.nan)
 if phi_pred_values is not None:
     row_list.append(score1C_base(phi_true_values, phi_pred_values,
                                  weights_true, weights_pred))
 else:
     row_list.append(np.nan)
 if pred_cluster_assign is not None:
     ordered_table = pd.merge(pred_cluster_assign, true_cluster_assign,
                              on='mutation_id', how='inner')
     if len(true_cluster_assign)<20000:
         row_list.append(score2A_base(ordered_table.true_cluster_id,
                                      ordered_table.pred_cluster_id))
     else:
         row_list.append(np.nan)
     ordered_table = pd.merge(pred_subclonal, true_subclonal,
                              on='mutation_id', how='inner')
     auc, accuracy, sensitivity, specificity, precision = \
         score2C_base(ordered_table.true_subclonal,
                      ordered_table.pred_subclonal)