.. _`mining`:
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Gradient correlation
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Find genes that could be PCGs by calculating and evaluating correlation coefficient values between known PCGs (from published studies) and target genes.

Calculating Spearman's rank correlation coefficient
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.. code-block:: python3

  import scipy.stats
  import scanpy as sc
  import pandas as pd

.. code-block:: python3

  adata = sc.read_h5ad(file_name)
  df = adata.to_df()
  target_genes = list(df.columns)  # or assigned by users
  with open('known_pcgs.txt', 'r') as f:
    known_pcgs = f.read().splitlines()

Finding out more polarity-related genes
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.. code-block:: python3

  df_data = {'gene':[], 'pcg':[], 'scc':[]}
  for gene in target_genes:
    for pcg in known_pcgs:
      if pcg in df.columns and pcg != gene:
        df_data['gene'].append(gene)
        df_data['pcg'].append(pcg)
        scc = scipy.stats.spearmanr(df[gene], df[pcg]).correlation
        df_data['scc'].append(scc)

.. code-block:: python3

  results = pd.DataFrame(df_data)
  results = results.sort_values(by='scc', ascending=False)
  results.to_csv('spearman.csv', index=False)