def METHOD_NAME():
    target_scores = pd.Series([1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
    decoy_scores = pd.Series([0.8, 0.55, 0.2, 0.1])
    n_targets = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
    n_decoys = pd.Series([0, 0, 1, 1, 1, 2, 2, 2, 3, 4, 4])
    expected_fdr = n_decoys / n_targets
    expected_fdr_ros = (n_decoys + 1) / (n_targets + 1)
    expected_fdr_mono = pd.Series(
        [0 / 2, 0 / 2, 1 / 5, 1 / 5, 1 / 5, 2 / 8, 2 / 8, 2 / 8, 3 / 9, 4 / 11, 4 / 11]
    )
    fdr = run_fdr_ranking(target_scores, decoy_scores, 1, False, False)
    fdr_ros = run_fdr_ranking(target_scores, decoy_scores, 1, True, False)
    fdr_mono = run_fdr_ranking(target_scores, decoy_scores, 1, False, True)
    assert np.isclose(fdr, expected_fdr).all()
    assert np.isclose(fdr_ros, expected_fdr_ros).all()
    assert np.isclose(fdr_mono, expected_fdr_mono).all()