docs: def _significant_pathways_dataframe(pvalue_information,
                                      side_information,
                                      alpha):
      """Create the significant pathways pandas.DataFrame.
      Given the p-values corresponding to each pathway in a feature,
      apply the FDR correction for multiple testing and remove those that
      do not have a q-value of less than `alpha`.
      """
      significant_pathways = pd.concat(
          [pvalue_information, side_information], axis=1)
      # fdr_bh: false discovery rate, Benjamini & Hochberg (1995, 2000)
      below_alpha, qvalues, _, _ = multipletests(
          significant_pathways["p-value"], alpha=alpha, method="fdr_bh")
      below_alpha = pd.Series(
          below_alpha, index=pvalue_information.index, name="pass")
      qvalues = pd.Series(
          qvalues, index=pvalue_information.index, name="q-value")
      significant_pathways = pd.concat(
          [significant_pathways, below_alpha, qvalues], axis=1)
      significant_pathways = significant_pathways[significant_pathways["pass"]]
      significant_pathways.drop("pass", axis=1, inplace=True)
      significant_pathways.loc[:, "pathway"] = significant_pathways.index
      return significant_pathways