Repository: monarch-initiative/gpsea (language: python)

Issue:
Add more sanity checks
I got an error yesterday because I entered the protein ID as "NP_000123.2 ", i.e., with a trailing space.
This led to an exception from the UniProt API saying it could not find the protein.
I would suggest let's throw an error if the transcript ID does not correspond to what we expect -- begings with "NP_" and ends with a version number, no leading or trailing spaces. The user will then know what to do -- I just guessed that there was a whitespace issue.


Discussion / hints:

Fix patch:
diff --git a/notebooks/MAPK8IP3/MAPK8IP3.ipynb b/notebooks/MAPK8IP3/MAPK8IP3.ipynb
index fbe9d921..a5ba9827 100644
--- a/notebooks/MAPK8IP3/MAPK8IP3.ipynb
+++ b/notebooks/MAPK8IP3/MAPK8IP3.ipynb
@@ -418,8 +418,7 @@
     "analysis_config = CohortAnalysisConfiguration()\n",
     "analysis_config.missing_implies_excluded = True\n",
     "analysis_config.pval_correction = 'fdr_bh'\n",
-    "analysis_config.min_perc_patients_w_hpo = 0.1\n",
-    "analysis_config.heuristic_strategy()\n",
+    "analysis_config.heuristic_strategy(threshold_HPO_observed_frequency=0.25)\n",
     "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)"
    ]
   },
diff --git a/notebooks/PPP2R1A/PPP2R1A.ipynb b/notebooks/PPP2R1A/PPP2R1A.ipynb
index 43432410..ca1f7929 100644
--- a/notebooks/PPP2R1A/PPP2R1A.ipynb
+++ b/notebooks/PPP2R1A/PPP2R1A.ipynb
@@ -380,7 +380,7 @@
     "\n",
     "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n",
     "tx_coordinates = txc_service.fetch(PPP2R1A_transcript_id)\n",
-    "protein_id = 'NP_037407.4' \n",
+    "protein_id = 'NP_037407.4'\n",
     "\n",
     "pms = UniprotProteinMetadataService()\n",
     "protein_meta = pms.annotate(PPP2R1A_protein_id)"
@@ -420,8 +420,7 @@
     "analysis_config = CohortAnalysisConfiguration()\n",
     "analysis_config.missing_implies_excluded = True\n",
     "analysis_config.pval_correction = 'fdr_bh'\n",
-    "analysis_config.min_perc_patients_w_hpo = 0.1\n",
-    "analysis_config.heuristic_strategy()\n",
+    "analysis_config.heuristic_strategy(threshold_HPO_observed_frequency=0.25)\n",
     "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)"
    ]
   },
diff --git a/notebooks/SUOX/SUOX.ipynb b/notebooks/SUOX/SUOX.ipynb
index c0f4f6b2..7a109325 100644
--- a/notebooks/SUOX/SUOX.ipynb
+++ b/notebooks/SUOX/SUOX.ipynb
@@ -19,11 +19,19 @@
    "id": "0f100a23",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/robin/PycharmProjects/genophenocorr/gpc_venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded HPO v2024-04-26\n",
+      "Loaded HPO v2023-10-09\n",
       "Using genophenocorr version 0.1.1dev\n"
      ]
     }
@@ -40,7 +48,7 @@
     "from IPython.display import HTML, display\n",
     "import hpotk\n",
     "store = hpotk.configure_ontology_store()\n",
-    "hpo = store.load_minimal_hpo()\n",
+    "hpo = store.load_minimal_hpo(release='v2023-10-09')\n",
     "print(f'Loaded HPO v{hpo.version}')\n",
     "print(f\"Using genophenocorr version {genophenocorr.__version__}\")"
    ]
@@ -83,7 +91,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Patients Created: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [01:29<00:00,  2.55s/it]\n",
+      "Patients Created: 100%|██████████| 35/35 [00:00<00:00, 1207.61it/s]\n",
       "Validated under none policy\n",
       "No errors or warnings were found\n"
      ]
@@ -160,161 +168,159 @@
        "  <p>No errors encountered.</p>\n",
        "  \n",
        "\n",
-       "    <h3>Top 10 HPO Terms</h3>\n",
-       "    <table>\n",
-       "      <caption>A total of 28 HPO terms were used to annotated the cohort. </caption>\n",
+       "    <table align=\"left\">\n",
+       "    <caption style=\"color: black;\">\n",
+       "        <h3>Top 10 HPO Terms</h3>\n",
+       "        A total of 28 HPO terms were used to annotated the cohort. \n",
+       "    </caption>\n",
        "        <tbody>\n",
        "            <tr class=\"strng\">\n",
        "                <th class=\"lft\">HPO Term</th>\n",
        "                <th>ID</th>\n",
        "                <th>Annotation Count</th>\n",
-       "              </tr>\n",
-       "              \n",
-       "              <tr>\n",
+       "            </tr>\n",
+       "            \n",
+       "            <tr>\n",
        "                <td class=\"lft\">Seizure</td>\n",
        "                <td>HP:0001250</td>\n",
        "                <td>28</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
+       "            <tr>\n",
        "                <td class=\"lft\">Hypotonia</td>\n",
        "                <td>HP:0001252</td>\n",
        "                <td>15</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
+       "            <tr>\n",
        "                <td class=\"lft\">Sulfocysteinuria</td>\n",
        "                <td>HP:0032350</td>\n",
        "                <td>13</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
+       "            <tr>\n",
        "                <td class=\"lft\">Hypertonia</td>\n",
        "                <td>HP:0001276</td>\n",
        "                <td>11</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">Microcephaly</td>\n",
-       "                <td>HP:0000252</td>\n",
-       "                <td>10</td>\n",
+       "            <tr>\n",
+       "                <td class=\"lft\">Abnormality of extrapyramidal motor function</td>\n",
+       "                <td>HP:0002071</td>\n",
+       "                <td>11</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">Hypohomocysteinemia</td>\n",
-       "                <td>HP:0020222</td>\n",
+       "            <tr>\n",
+       "                <td class=\"lft\">Microcephaly</td>\n",
+       "                <td>HP:0000252</td>\n",
        "                <td>10</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
+       "            <tr>\n",
        "                <td class=\"lft\">Hypohomocysteinemia</td>\n",
        "                <td>HP:0020222</td>\n",
        "                <td>10</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
+       "            <tr>\n",
        "                <td class=\"lft\">Neurodevelopmental delay</td>\n",
        "                <td>HP:0012758</td>\n",
        "                <td>8</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">Ectopia lentis</td>\n",
-       "                <td>HP:0001083</td>\n",
+       "            <tr>\n",
+       "                <td class=\"lft\">Hypocystinemia</td>\n",
+       "                <td>HP:0500152</td>\n",
        "                <td>7</td>\n",
        "                </tr>\n",
        "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">Hypocystinemia</td>\n",
-       "                <td>HP:0500152</td>\n",
+       "            <tr>\n",
+       "                <td class=\"lft\">Ectopia lentis</td>\n",
+       "                <td>HP:0001083</td>\n",
        "                <td>7</td>\n",
        "                </tr>\n",
        "            \n",
        "                </tbody>\n",
        "    </table>\n",
        "\n",
-       " <h3>Top 10 Variants</h3>\n",
-       " <table>\n",
-       "  <caption>\n",
-       "    Variants are shown according to NM_001032386.2. A total of 48 unique variants were identified in the cohort.\n",
-       "  </caption>\n",
-       "        <tbody>\n",
-       "            <tr class=\"strng\">\n",
-       "                <th class=\"lft\">Variant</th>\n",
-       "                <th>Variant name</th>\n",
-       "                <th>Variant Count</th>\n",
-       "              </tr>\n",
-       "              \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004589_56004589_C_G</td>\n",
-       "                <td>c.1200C>G</td>\n",
-       "                <td>7</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004039_56004039_G_A</td>\n",
-       "                <td>c.650G>A</td>\n",
-       "                <td>3</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004485_56004485_C_T</td>\n",
-       "                <td>c.1096C>T</td>\n",
-       "                <td>3</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004765_56004765_G_A</td>\n",
-       "                <td>c.1376G>A</td>\n",
-       "                <td>3</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004273_56004273_G_A</td>\n",
-       "                <td>c.884G>A</td>\n",
-       "                <td>2</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004771_56004771_A_T</td>\n",
-       "                <td>c.1382A>T</td>\n",
-       "                <td>2</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004905_56004909_ATTGT_A</td>\n",
-       "                <td>c.1521_1524del</td>\n",
-       "                <td>2</td>\n",
+       "    <table align=\"right\">\n",
+       "    <caption style=\"color: black;\">\n",
+       "        <h3>Top 10 Variants</h3>\n",
+       "        Variants are shown according to NM_001032386.2. A total of 48 unique variants were identified in the cohort.\n",
+       "    </caption>\n",
+       "            <tbody>\n",
+       "                <tr class=\"strng\">\n",
+       "                    <th class=\"lft\">Variant</th>\n",
+       "                    <th>Variant name</th>\n",
+       "                    <th>Variant Count</th>\n",
        "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004273_56004273_G_A</td>\n",
-       "                <td>c.884G>A</td>\n",
-       "                <td>2</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004933_56004933_A_ACAATGTGCAGCCAGACACCGTGGCCC</td>\n",
-       "                <td>c.1549_1574dup</td>\n",
-       "                <td>2</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56004120_56004124_CTCTT_C</td>\n",
-       "                <td>c.734_737del</td>\n",
-       "                <td>1</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">12_56003864_56003864_G_T</td>\n",
-       "                <td>c.475G>T</td>\n",
-       "                <td>1</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "         </tbody>\n",
-       "    </table>\n",
-       "    <h3>Diseases</h3>\n",
-       "    <table>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004589_56004589_C_G</td>\n",
+       "                    <td>c.1200C>G</td>\n",
+       "                    <td>7</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004039_56004039_G_A</td>\n",
+       "                    <td>c.650G>A</td>\n",
+       "                    <td>3</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004485_56004485_C_T</td>\n",
+       "                    <td>c.1096C>T</td>\n",
+       "                    <td>3</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004765_56004765_G_A</td>\n",
+       "                    <td>c.1376G>A</td>\n",
+       "                    <td>3</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004905_56004909_ATTGT_A</td>\n",
+       "                    <td>c.1521_1524del</td>\n",
+       "                    <td>2</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004933_56004933_A_ACAATGTGCAGCCAGACACCGTGGCCC</td>\n",
+       "                    <td>c.1549_1574dup</td>\n",
+       "                    <td>2</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004771_56004771_A_T</td>\n",
+       "                    <td>c.1382A>T</td>\n",
+       "                    <td>2</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004273_56004273_G_A</td>\n",
+       "                    <td>c.884G>A</td>\n",
+       "                    <td>2</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56004473_56004473_G_A</td>\n",
+       "                    <td>c.1084G>A</td>\n",
+       "                    <td>1</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td class=\"lft\">12_56003741_56003741_C_T</td>\n",
+       "                    <td>c.352C>T</td>\n",
+       "                    <td>1</td>\n",
+       "                    </tr>\n",
+       "                \n",
+       "            </tbody>\n",
+       "        </table>\n",
+       "    <table align=\"left\">\n",
+       "    <caption style=\"color: black;\">\n",
+       "        <h3>Diseases</h3>\n",
+       "    </caption>\n",
        "        <tbody>\n",
        "            <tr class=\"strng\">\n",
        "                <th class=\"lft\">Disease ID</th>\n",
@@ -331,31 +337,7 @@
        "           </tbody>\n",
        "    </table>\n",
        "    \n",
-       "    <h3>Variant categories for NM_001032386.2</h3>\n",
-       "    <table>\n",
-       "        <tbody>\n",
-       "            <tr class=\"strng\">\n",
-       "                <th class=\"lft\">Variant effect</th>\n",
-       "                <th>Annotation Count</th>\n",
-       "              </tr>\n",
-       "              \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">MISSENSE_VARIANT</td>\n",
-       "                <td>29</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">FRAMESHIFT_VARIANT</td>\n",
-       "                <td>9</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "              <tr>\n",
-       "                <td class=\"lft\">STOP_GAINED</td>\n",
-       "                <td>10</td>\n",
-       "                </tr>\n",
-       "            \n",
-       "           </tbody>\n",
-       "    </table>\n",
+       "  <p>Call this function with transcript to see table with variant effect counts.</p>\n",
        "  \n",
        "\n",
        "\n",
@@ -379,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "615010fa",
    "metadata": {},
    "outputs": [],
@@ -387,15 +369,14 @@
     "from genophenocorr.preprocessing import UniprotProteinMetadataService\n",
     "\n",
     "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n",
-    "tx_coordinates = txc_service.fetch(SUOX_transcript_id)\n",
-    "protein_id = 'NP_037407.4' \n",
+    "tx_coordinates = txc_service.fetch(SUOX_transcript_id) \n",
     "pms = UniprotProteinMetadataService()\n",
     "protein_meta = pms.annotate(SUOX_protein_id)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "3152c918",
    "metadata": {},
    "outputs": [
@@ -405,13 +386,13 @@
        "<Axes: title={'center': 'Sulfite oxidase, mitochondrial\\ntranscript: NM_001032386.2, protein: NP_001027558.1'}>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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nZyfDMPTCCy/IxcVFU6ZMkST169dPCxcu1JAhQ9SwYUP5+/un23+fPn302WefqUePHvrrr7+UL18+ffvtt+ab8GlKlCih4OBgvfHGGzp16pQ8PDy0cOFCqzUWDh48qAYNGqhTp04qWbKkHBwctGjRIp07d06dO3eWdKs4lNlzEh0draCgIPXs2VMzZsx40I/ngbi4uKhkyZKaN2+eihUrJh8fH5UuXVqlS5fW+PHj1axZM1WvXl0vvviiEhMTFRkZKU9PT4WHh5v7GD16tFauXKnQ0FD17dtXISEhOnPmjObPn6+NGzfKy8sr0/n4+flpxIgRioiIUNOmTdWqVSsdOHBAkydPVuXKlS0Wps6szH7+9+vZZ5/VDz/8oLZt26pFixY6duyYvvjiC5UsWVLXrl17qL4BAACAu6EQAQAAgCwzYcIEzZ8/X0uXLtXUqVOVlJSkQoUKacCAAXr33XctbvCOHDlSMTExWrBggb7//ns1a9ZMy5YtU+7cubMsn7x58+qLL77QmDFj9OKLLyolJUXr1q1T7ty51bVrV/n7++vDDz/U+PHjdePGDeXPn1+1a9e2KJikJ0+ePNq0aZPefPNNRUZG6vr16ypbtqx++ukn8y/gIyMjtX79ei1cuFB+fn7mfb/++muVLl1affr00S+//JJu/66urlqzZo0GDRqkyMhIubq6qlu3bmrWrJmaNm1qjnN0dNRPP/2kwYMHa8yYMXJ2dlbbtm31yiuvqFy5cua4ggULqkuXLlqzZo2+/fZbOTg4qESJEvr+++/Vvn17c1xmz0naTet8+fLdx6eRdaZNm6ZBgwbptddeU1JSksLCwlS6dGk1bNhQy5cvV1hYmEaOHClHR0eFhoZq7NixCgoKMu+fP39+bdmyRf/5z380Z84cXblyRfnz51ezZs0e6GZ/eHi4/Pz89Nlnn+m1116Tj4+P+vbtq9GjR8vR0fG++8vs53+/evXqpbNnz+rLL7/UihUrVLJkSc2ePVvz58/X+vXrH7hfAAAA4F5MxqO0EhwAAACAR97kyZM1fPhwHTlyRHny5MnudAAAAAA84lgjAgAAAMB9WbdunQYPHkwRAgAAAECm8EQEAAAAAAAAAACwGZ6IAAAAAAAAAAAANkMhAgAAAAAAAAAA2AyFCAAAAAAAAAAAYDMUIgAAAAAAAAAAgM1QiAAAAHiE9OrVS4GBgdmdBoAMzJgxQyaTSdHR0dmdCgAAAPDYoBABAAAeeZs2bVJ4eLji4uKyO5VH2tKlSxUeHv7Q/ZhMJplMJk2cONFqW9pN2D///NPcFh4eLpPJJDs7O/3zzz9W+1y5ckUuLi4ymUx65ZVX7jufqKgoNW3aVO7u7vLx8VH37t0VExNjFZeamqpx48YpKChIzs7OKlu2rL777juruK1bt2rAgAGqWLGiHB0dZTKZ7jr+119/rZCQEDk7O6to0aKKjIy0ilm0aJGaNGkif39/OTk5qUCBAurQoYP27NljEXfhwgWNHz9ederUkZ+fn7y8vFStWjXNmzfPqs+9e/eqY8eOKly4sFxdXeXr66s6derop59+SjfP77//XtWqVZOXl5dy5cql0NBQ/fLLL+nGHjlyRF27dlXu3Lnl4uKiokWL6p133rnreZCkNWvW6IUXXlCxYsXk6uqqwoUL66WXXtKZM2fuue+j4PTp0woPD9fOnTuzO5V7Wr9+vfm7+Ndff1lt79Wrl9zd3S3a6tata97HZDLJx8dHlStX1jfffKPU1NT7Gj+z3ycp89/RDz74QK1atVKePHlkMpnu+vfq1KlT6tSpk7y8vOTh4aHWrVvr6NGjFjH//POPIiIiVKVKFXl7e8vX11d169bV6tWrrfq789zc/nJ0dLSIDQwMTDfu5ZdftohL+3uY3uvs2bMWsdevX9eYMWNUsmRJubq6Kn/+/OrYsaP27t2b4Tl40HMHAADwKHLI7gQAAADuZdOmTYqIiFCvXr3k5eWV3enY1FdffXXfNwzTLF26VJ9//nmW3aAaP368+vfvL1dX10zFOzk56bvvvtPw4cMt2n/44YcHzuHkyZOqU6eOPD09NXr0aF27dk0TJkzQ7t27tXXrVuXIkcMc+8477+jDDz9Unz59VLlyZS1ZskRdu3aVyWRS586dzXFLly7VtGnTVLZsWRUuXFgHDx7McPwvv/xSL7/8stq3b6/XX39dGzZs0ODBg5WQkKA333zTHLd79255e3tryJAh8vX11dmzZ/XNN9+oSpUq2rx5s8qVKydJ2rx5s9555x01b95c7777rhwcHLRw4UJ17txZ+/btU0REhLnP48eP6+rVq+rZs6f8/f2VkJCghQsXqlWrVvryyy/Vt29fc2xkZKQGDx6sFi1a6MMPP9T169c1Y8YMPfvss1q4cKHatWtnjt25c6fq1q2r/Pnza+jQocqVK5dOnDiRbhHpTm+++aYuXryojh07qmjRojp69Kg+++wz/fzzz9q5c6fy5s17zz6y0+nTpxUREaHAwECVL1/+gfro3r27OnfuLCcnp6xN7i7Cw8MzLEDdqUCBAhozZowkKSYmRrNmzdKLL76ogwcP6sMPP8z0mJn9Pt3Pd/Tdd99V3rx59cwzz2jFihUZjn3t2jXVq1dPly9f1ttvvy1HR0dNmjRJoaGh2rlzp3LlyiVJWrJkicaOHas2bdqoZ8+eSk5O1qxZs9SoUSN988036t27t8XxvPTSSxbjxMfH6+WXX1bjxo2tcihfvryGDh1q0VasWLF08x01apSCgoIs2u78/6pu3brpxx9/VJ8+fVShQgWdPn1an3/+uapXr67du3crICAgw/MhZf7cAQAAPLIMAACAR9z48eMNScaxY8fuGZuSkmIkJibaPqksdu3atYfuY+DAgUZW/OudJKN8+fKGJGPixIkW26ZPn25IMrZt22ZuCwsLMyQZ7dq1M8qXL2/VX6NGjYz27dsbkoyBAwfeVy79+/c3XFxcjOPHj5vbVq1aZUgyvvzyS3PbyZMnDUdHR4v+U1NTjdq1axsFChQwkpOTze1nz541EhISDMO4+zlLSEgwcuXKZbRo0cKivVu3boabm5tx8eLFu+Z+9uxZw8HBwejXr5+57ejRo0Z0dLRFXGpqqlG/fn3DycnpntdBcnKyUa5cOaN48eIW7UWLFjUqV65spKammtsuX75suLu7G61atTK3paSkGKVLlzaqVq1qPgf349dffzVSUlKs2iQZ77zzzn3397BSU1Pv6zi2bdtmSDKmT59uu6SyyLp16yy+i3/99ZfF9p49expubm4WbaGhoUapUqUs2uLj440CBQoYbm5uRlJSUqbGvp/vU2a/o4ZhmP+Gx8TEGJKMsLCwdMcfO3asIcnYunWruS0qKsqwt7c3RowYYW7bs2ePERMTY7Hv9evXjRIlShgFChS453F+++23hiRjzpw5Fu0BAQFW3/v0pPf3MD0nT540JBlvvPGGRfvatWsNScZHH310z7Eye+4AAAAeVUzNBAAAHmnh4eEaNmyYJCkoKMg87UXa/Oxp0/3MmTNHpUqVkpOTk5YvXy5JmjBhgmrUqKFcuXLJxcVFFStW1IIFC6zGSOtj8eLFKl26tJycnFSqVClzP2muXr2qV199VYGBgXJyclLu3LnVqFEjbd++3SJuy5Ytat68uby9veXm5qayZcvqk08+MW9Pm1LlyJEjat68uXLmzKlu3bqZt92+RkR0dLRMJpMmTJigSZMmKSAgQC4uLgoNDbWY9qdXr176/PPPzceT9kpz5swZ7d+/Xzdv3szUea9Zs6bq16+vcePGKTExMVP7dO3aVTt37tT+/fvNbWfPntXatWvVtWvXTPVxp4ULF+rZZ59VoUKFzG0NGzZUsWLF9P3335vblixZops3b2rAgAHmNpPJpP79++vkyZPavHmzuT1PnjxycXG559jr1q3ThQsXLPqUpIEDByo+Pj7DaY/S5M6dW66urhZTigUFBVn98tlkMqlNmza6ceOG1dQzd7K3t1fBggWtpim7cuWKcufObfGZe3h4yN3d3eJYV65cqT179igsLEwuLi5KSEhQSkrKXce8XZ06dWRnZ2fV5uPjo6ioqEz3c7u06W1+++039evXT7ly5ZKHh4d69OihS5cuWcQGBgbq2Wef1YoVK1SpUiW5uLjoyy+/lCQdPXpUHTt2lI+Pj1xdXVWtWjWLz2j9+vWqXLmyJKl3797m78iMGTPMMVu2bFHTpk3l6ekpV1dXhYaG6vfff08339vXiEjLa+PGjapSpYqcnZ1VuHBhzZo1y+p4jxw5oiNHjmT6/AwaNEje3t4P/KRT2rmIj49Pd7qk9NzP9ymz31FJmV7/ZsGCBapcubL585KkEiVKqEGDBhZ9lipVSr6+vhb7Ojk5qXnz5jp58qSuXr1613Hmzp0rNzc3tW7dOt3tSUlJio+Pz1TOV69ezfC7lJZHnjx5LNrz5csnSZn6e8TaQQAA4HFHIQIAADzS2rVrpy5dukiSJk2apG+//Vbffvut/Pz8zDFr167Va6+9pueee06ffPKJ+YbNJ598omeeeUajRo3S6NGj5eDgoI4dO6Z7A3njxo0aMGCAOnfurHHjxun69etq3769Lly4YI55+eWXNWXKFLVv316TJ0/WG2+8IRcXF4sbsKtWrVKdOnW0b98+DRkyRBMnTlS9evX0888/W4yXnJysJk2aKHfu3JowYYLat29/1/Mwa9Ysffrppxo4cKBGjBihPXv2qH79+jp37pwkqV+/fmrUqJEkmc/Rt99+a95/xIgRCgkJ0alTpzJz2iXdKgKdO3dOU6ZMyVR8nTp1VKBAAc2dO9fcNm/ePLm7u6tFixaZHjfNqVOndP78eVWqVMlqW5UqVbRjxw7z+x07dsjNzU0hISFWcWnb71faPneOX7FiRdnZ2aXbZ1xcnGJiYrR792699NJLunLliho0aHDPsdLmk7/zpqp0a/qY2NhYHTlyRJMmTdKyZcus+qxbt66WL1+uyMhIRUdHa//+/Ro4cKAuX76sIUOGmOPS5s53cnJSpUqV5ObmJldXV3Xu3FkXL168Z57puXbtmq5du5Zu7vfjlVdeUVRUlMLDw9WjRw/NmTNHbdq0kWEYFnEHDhxQly5d1KhRI33yyScqX768zp07pxo1amjFihUaMGCAPvjgA12/fl2tWrXSokWLJEkhISEaNWqUJKlv377m70idOnUk3fo7UqdOHV25ckVhYWEaPXq04uLiVL9+fW3duvWe+R8+fFgdOnRQo0aNNHHiRHl7e6tXr15WawA0aNAgU9dEGg8PD7322mv66aefrIqemXX06FHZ29tnemq7zH6f7uc7mlmpqan6+++/M+zzyJEj9ywwnD17Vq6urnedVi4mJkarVq1SmzZt5ObmZrV97dq1cnV1lbu7uwIDAy2KyXeqV6+ePDw85OrqqlatWunQoUMW24ODg1WgQAFNnDhRP/30k06ePKmtW7fq5ZdfVlBQkMVUVwAAAE+s7H4kAwAA4F7uNjWTJMPOzs7Yu3ev1bY7p2xJSkoySpcubdSvX9+qjxw5chiHDx82t+3atcuQZERGRprbPD097zq1UHJyshEUFGQEBAQYly5dsth2+5Q5PXv2NCQZb731llUfPXv2NAICAszvjx07ZkgyXFxcjJMnT5rbt2zZYkgyXnvtNXPb3aYZShszM9Nb6bYplOrVq2fkzZvXfC7vNjVTTEyM8cYbbxhFihQxb6tcubLRu3dvq34zI20anVmzZlltGzZsmCHJuH79umEYhtGiRQujcOHCVnHx8fEZnmvDuPs5GzhwoGFvb5/uNj8/P6Nz585W7cWLFzckGZIMd3d3491337WayuhOFy5cMHLnzm3Url073e39+vUz92lnZ2d06NDBalqoc+fOGQ0aNDDHSTJ8fX2NTZs2WcS1atXKkGTkypXL6Natm7FgwQLjP//5j+Hg4GDUqFHD4jrNrPfee8+QZKxZs+a+9zWM/11TFStWtJg6aNy4cYYkY8mSJea2gIAAQ5KxfPlyiz5effVVQ5KxYcMGc9vVq1eNoKAgIzAw0PwZZDQ1U2pqqlG0aFGjSZMmFucgISHBCAoKMho1amSV7+3fpbS8fvvtN3Pb+fPnDScnJ2Po0KEWYwUEBFh8xzOSNjXT/Pnzjbi4OMPb29timq2MpmYqUaKEERMTY8TExBhRUVHG4MGDDUlGy5Yt7zlmmsx+n+7nO3q7u00vlLZt1KhRVts+//xzQ5Kxf//+DHM/dOiQ4ezsbHTv3v1uh2hERkYakoylS5dabWvZsqUxduxYY/HixcbXX39t1K5d25BkDB8+3CJu3rx5Rq9evYyZM2caixYtMt59913D1dXV8PX1NU6cOGERu2XLFiM4ONjiO1qxYkXjzJkzd83zTkzNBAAAHlc8EQEAAB57oaGhKlmypFX77dNdXLp0SZcvX1bt2rXT/VVxw4YNFRwcbH5ftmxZeXh4WEyV4+XlpS1btuj06dPp5rFjxw4dO3ZMr776qtUvj2+fMidN//7973lsadq0aaP8+fOb31epUkVVq1bV0qVLM7X/jBkzZBjGfU/vER4errNnz+qLL77IVHzXrl11+PBhbdu2zfy/DzotU9qUUOktCuzs7GwRk5iYmKm4+x3/9oV27+w3vT6nT5+u5cuXa/LkyQoJCVFiYuJdpz5KTU1Vt27dFBcXp8jIyHRjXn31Va1atUozZ85Us2bNlJKSoqSkJIsYV1dXFS9eXD179tT8+fP1zTffKF++fGrXrp0OHz5sjrt27ZokqXLlypo9e7bat2+vUaNG6b333tOmTZu0Zs2ae56X2/3222+KiIhQp06dVL9+/fva9059+/aVo6Oj+X3//v3l4OBgdY0HBQWpSZMmFm1Lly5VlSpVVKtWLXObu7u7+vbtq+joaO3bt++uY+/cuVOHDh1S165ddeHCBcXGxio2Nlbx8fFq0KCBfvvtt3suIl+yZEnVrl3b/N7Pz0/Fixe3mm4rOjraYlqnzPD09NSrr76qH3/88Z5PGezfv19+fn7y8/NTSEiIIiMj1aJFC33zzTeZHi+z36f7+Y7ez9gP2mdCQoI6duwoFxeXey7MPXfuXPn5+ZmfJLvdjz/+qOHDh6t169Z64YUX9Ouvv6pJkyb66KOPdPLkSXNcp06dNH36dPXo0UNt2rTRe++9pxUrVujChQv64IMPLPr09vZW+fLl9dZbb2nx4sWaMGGCoqOj1bFjR12/fv3uJwUAAOAJQCECAAA89oKCgtJt//nnn1WtWjU5OzvLx8dHfn5+mjJlii5fvmwVe/v85mm8vb0t5qgfN26c9uzZo4IFC6pKlSoKDw+3uMmYNu976dKl75mzg4ODChQocM+4NEWLFrVqK1as2H3f0LxfderUUb169TK9VsQzzzyjEiVKaO7cuZozZ47y5s37wDeo0wpJN27csNqWduMuLcbFxSVTcfc7/p03/G/vN70+q1evriZNmqh///5asWKFZs+erREjRmQ4xqBBg7R8+XJNmzZN5cqVSzemRIkSatiwoXr06KGff/5Z165dU8uWLS2mLOrYsaNOnDihGTNmqEOHDurdu7fWr1+vpKQkvfPOOxbHJMk83VmatGLRpk2bMsz1Tvv371fbtm1VunRpTZs2LdP7ZeTOa9zd3V358uWzusbT+74fP35cxYsXt2pPm1ro+PHjdx07bSqdnj17mm/ip72mTZumGzdupPt343aZ+RvyMIYMGSIvL697rhURGBioVatWafXq1dq4caPOnj2rn3/++b6mzsrs9+l+vqP3M/aD9JmSkqLOnTtr3759WrBggfz9/TMc4+jRo9q8ebOee+45OTg43DMnk8mk1157TcnJyVq/fv1dY2vVqqWqVauap0GTZC6CV69eXWPGjFHr1q01dOhQLVy4UBs3btT06dPvmQMAAMDjjkIEAAB47KV3U2rDhg1q1aqVnJ2dNXnyZC1dulSrVq1S165dreacl24tApye22M7deqko0ePKjIyUv7+/ho/frxKlSqlZcuW3XfOTk5OVov+PqrCwsJ09uxZ86LA99K1a1fNmzdPc+fO1XPPPffAx5m2kOuZM2estp05c0Y+Pj7mX03ny5dPZ8+etfps0/a9203Ju42fkpKi8+fPW7QnJSXpwoUL9+zT29tb9evX15w5c9LdHhERocmTJ+vDDz9U9+7dM51Xhw4dtG3bNh08eFDSrZuqy5cvV6tWrSzifHx8VKtWLYvFltNyvnPR3Ny5c0tSpm+a//PPP2rcuLE8PT21dOlS5cyZM9P5P6wHKSrdS9rTDuPHj9eqVavSfbm7u9+1j8z8DXkYmX0qws3NTQ0bNlSDBg1Us2ZN82d7PzL7fbqf72hmpe2TUZ+3j3+7Pn366Oeff9aMGTPuWfxMW8emW7dumc6rYMGCkpSptVQKFixoEbdw4UKdO3fO6jsaGhoqDw8PqwXRAQAAnkSPx3/9AgCAp1p60xrdy8KFC+Xs7KwVK1bohRdeULNmzdSwYcOHziVfvnwaMGCAFi9erGPHjilXrlzmKTjSpnbas2fPQ49zpzsXP5WkgwcPWky19CDnKTNCQ0NVt25djR07NlNPRXTt2lVnzpzRwYMHH3haJknKnz+//Pz89Oeff1pt27p1q8qXL29+X758eSUkJFgsHC5JW7ZsMW+/X2n73Dn+n3/+qdTU1Ez1mZiYmO4v6T///HOFh4fr1Vdf1ZtvvnlfeaV9Bmn9pi1Ynt4UUDdv3lRycrL5fcWKFSXJatHytOnGbl8EPiMXLlxQ48aNdePGDa1YscJ8M/ph3XmNX7t2TWfOnMnUdGIBAQE6cOCAVfv+/fvN26WMvyNp310PDw81bNgw3dft00Zll7Rp3yIiImw6Tma/T/fzHc0sOzs7lSlTJt0+t2zZosKFC1sVvoYNG6bp06dr0qRJVk/7pGfu3LkKDg5WtWrVMp1X2tNvmfmOHD161CIuo++oYRhKSUmx+I4CAAA8qShEAACAR56bm5skKS4uLtP72Nvby2QyWdz4iY6O1uLFix8oh5SUFKsbyrlz55a/v795CpEKFSooKChIH3/8sVWuD/ur6MWLF1vcPN66dau2bNmiZs2amdvudp7OnDmj/fv36+bNmw80ftpaEVOnTr1nbHBwsD7++GONGTNGVapUeaDx0rRv314///yz/vnnH3PbmjVrdPDgQXXs2NHc1rp1azk6Omry5MnmNsMw9MUXXyh//vyqUaPGfY9dv359+fj4aMqUKRbtU6ZMkaurq1q0aGFuu/OpCenW9bZmzRpVqlTJon3evHkaPHiwunXrpo8++ijD8dPr8+bNm5o1a5ZcXFzM66IUKVJEdnZ2mjdvnsV1dvLkSW3YsEHPPPOMua1169ZycnLS9OnTLdY8SJta6fb58tO7ZuLj49W8eXOdOnVKS5cuTXfKsAc1depUi7GmTJmi5ORki2s8I82bN9fWrVu1efNmi1ynTp2qwMBA87nK6DtSsWJFBQcHa8KECeZ1NG4XExPzIIeUriNHjpincbtfaU9FLFmyRDt37syynO50P9+nzH5H70faUz+3FyMOHDigtWvXWvU5fvx4TZgwQW+//baGDBlyz7537NihqKioDIukFy9etCoY3Lx5Ux9++KFy5MihevXqmdvTuy6WLl2qv/76S02bNjW3FStWTJL03//+1yL2xx9/VHx8vMV39PLly9q/f/89pwIDAAB43Nx7QkwAAIBslvYr7nfeeUedO3eWo6OjWrZsab6pmJ4WLVroo48+UtOmTdW1a1edP39en3/+uYoUKaK///77vnO4evWqChQooA4dOqhcuXJyd3fX6tWrtW3bNk2cOFHSrV/yTpkyRS1btlT58uXVu3dv5cuXT/v379fevXu1YsWKBzsBunWzuVatWurfv79u3Lihjz/+WLly5dLw4cPNMWnnafDgwWrSpIns7e3VuXNnSdKIESM0c+ZMHTt27L4XrJZuPRURGhqqX3/9NVPxmbkhmBlvv/225s+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",
       "text/plain": [
        "<Figure size 2000x2000 with 1 Axes>"
       ]
@@ -436,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "531e077d",
    "metadata": {},
    "outputs": [],
@@ -445,7 +426,6 @@
     "analysis_config = CohortAnalysisConfiguration()\n",
     "analysis_config.missing_implies_excluded = True\n",
     "analysis_config.pval_correction = 'fdr_bh'\n",
-    "analysis_config.min_perc_patients_w_hpo = 0.1\n",
     "#analysis_config.heuristic_strategy()\n",
     "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)"
    ]
@@ -468,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "f4fb138e",
    "metadata": {},
    "outputs": [
@@ -578,32 +558,32 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormality of urine homeostasis [HP:0003110]</th>\n",
+       "      <th>Abnormal cerebral morphology [HP:0002060]</th>\n",
        "      <td>1/1</td>\n",
        "      <td>100%</td>\n",
-       "      <td>11/11</td>\n",
+       "      <td>5/5</td>\n",
        "      <td>100%</td>\n",
-       "      <td>2/2</td>\n",
+       "      <td>4/4</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal circulating homocysteine concentration [HP:0010919]</th>\n",
-       "      <td>4/4</td>\n",
+       "      <th>Abnormal brain morphology [HP:0012443]</th>\n",
+       "      <td>1/1</td>\n",
        "      <td>100%</td>\n",
        "      <td>5/5</td>\n",
        "      <td>100%</td>\n",
-       "      <td>1/1</td>\n",
+       "      <td>4/4</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Decreased head circumference [HP:0040195]</th>\n",
+       "      <th>Abnormal circulating cysteine concentration [HP:0010918]</th>\n",
        "      <td>1/1</td>\n",
        "      <td>100%</td>\n",
-       "      <td>5/5</td>\n",
+       "      <td>2/2</td>\n",
        "      <td>100%</td>\n",
        "      <td>4/4</td>\n",
        "      <td>100%</td>\n",
@@ -611,13 +591,13 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Aplasia/Hypoplasia of the cerebrum [HP:0007364]</th>\n",
-       "      <td>1/1</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>5/5</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>4/4</td>\n",
+       "      <th>Abnormal circulating non-proteinogenic amino acid concentration [HP:0033109]</th>\n",
+       "      <td>0/0</td>\n",
+       "      <td>0%</td>\n",
+       "      <td>2/2</td>\n",
        "      <td>100%</td>\n",
+       "      <td>0/0</td>\n",
+       "      <td>0%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -634,7 +614,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>71 rows × 8 columns</p>\n",
+       "<p>64 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -646,10 +626,10 @@
        "Abnormality of extrapyramidal motor function [H...   3/4     75%   6/13   \n",
        "Neurodevelopmental delay [HP:0012758]                0/4      0%   4/13   \n",
        "...                                                  ...     ...    ...   \n",
-       "Abnormality of urine homeostasis [HP:0003110]        1/1    100%  11/11   \n",
-       "Abnormal circulating homocysteine concentration...   4/4    100%    5/5   \n",
-       "Decreased head circumference [HP:0040195]            1/1    100%    5/5   \n",
-       "Aplasia/Hypoplasia of the cerebrum [HP:0007364]      1/1    100%    5/5   \n",
+       "Abnormal cerebral morphology [HP:0002060]            1/1    100%    5/5   \n",
+       "Abnormal brain morphology [HP:0012443]               1/1    100%    5/5   \n",
+       "Abnormal circulating cysteine concentration [HP...   1/1    100%    2/2   \n",
+       "Abnormal circulating non-proteinogenic amino ac...   0/0      0%    2/2   \n",
        "Abnormality of skull size [HP:0000240]               1/1    100%    5/5   \n",
        "\n",
        "MISSENSE_VARIANT on NM_001032386.2                         Neither          \\\n",
@@ -660,10 +640,10 @@
        "Abnormality of extrapyramidal motor function [H...     46%     2/8     25%   \n",
        "Neurodevelopmental delay [HP:0012758]                  31%     4/8     50%   \n",
        "...                                                    ...     ...     ...   \n",
-       "Abnormality of urine homeostasis [HP:0003110]         100%     2/2    100%   \n",
-       "Abnormal circulating homocysteine concentration...    100%     1/1    100%   \n",
-       "Decreased head circumference [HP:0040195]             100%     4/4    100%   \n",
-       "Aplasia/Hypoplasia of the cerebrum [HP:0007364]       100%     4/4    100%   \n",
+       "Abnormal cerebral morphology [HP:0002060]             100%     4/4    100%   \n",
+       "Abnormal brain morphology [HP:0012443]                100%     4/4    100%   \n",
+       "Abnormal circulating cysteine concentration [HP...    100%     4/4    100%   \n",
+       "Abnormal circulating non-proteinogenic amino ac...    100%     0/0      0%   \n",
        "Abnormality of skull size [HP:0000240]                100%     4/4    100%   \n",
        "\n",
        "MISSENSE_VARIANT on NM_001032386.2                                              \n",
@@ -674,16 +654,16 @@
        "Abnormality of extrapyramidal motor function [H...  0.262511               1.0  \n",
        "Neurodevelopmental delay [HP:0012758]               0.270175               1.0  \n",
        "...                                                      ...               ...  \n",
-       "Abnormality of urine homeostasis [HP:0003110]       1.000000               1.0  \n",
-       "Abnormal circulating homocysteine concentration...  1.000000               1.0  \n",
-       "Decreased head circumference [HP:0040195]           1.000000               1.0  \n",
-       "Aplasia/Hypoplasia of the cerebrum [HP:0007364]     1.000000               1.0  \n",
+       "Abnormal cerebral morphology [HP:0002060]           1.000000               1.0  \n",
+       "Abnormal brain morphology [HP:0012443]              1.000000               1.0  \n",
+       "Abnormal circulating cysteine concentration [HP...  1.000000               1.0  \n",
+       "Abnormal circulating non-proteinogenic amino ac...  1.000000               1.0  \n",
        "Abnormality of skull size [HP:0000240]              1.000000               1.0  \n",
        "\n",
-       "[71 rows x 8 columns]"
+       "[64 rows x 8 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -703,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "743954bd",
    "metadata": {},
    "outputs": [
@@ -813,18 +793,18 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormality of urine homeostasis [HP:0003110]</th>\n",
+       "      <th>Abnormal cerebral morphology [HP:0002060]</th>\n",
        "      <td>0/0</td>\n",
        "      <td>0%</td>\n",
        "      <td>2/2</td>\n",
        "      <td>100%</td>\n",
-       "      <td>12/12</td>\n",
+       "      <td>8/8</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal circulating homocysteine concentration [HP:0010919]</th>\n",
+       "      <th>Abnormal brain morphology [HP:0012443]</th>\n",
        "      <td>0/0</td>\n",
        "      <td>0%</td>\n",
        "      <td>2/2</td>\n",
@@ -835,24 +815,24 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Decreased head circumference [HP:0040195]</th>\n",
+       "      <th>Abnormal circulating cysteine concentration [HP:0010918]</th>\n",
        "      <td>0/0</td>\n",
        "      <td>0%</td>\n",
-       "      <td>2/2</td>\n",
+       "      <td>1/1</td>\n",
        "      <td>100%</td>\n",
-       "      <td>8/8</td>\n",
+       "      <td>6/6</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Aplasia/Hypoplasia of the cerebrum [HP:0007364]</th>\n",
+       "      <th>Abnormal circulating non-proteinogenic amino acid concentration [HP:0033109]</th>\n",
+       "      <td>0/0</td>\n",
+       "      <td>0%</td>\n",
        "      <td>0/0</td>\n",
        "      <td>0%</td>\n",
        "      <td>2/2</td>\n",
        "      <td>100%</td>\n",
-       "      <td>8/8</td>\n",
-       "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -869,7 +849,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>71 rows × 8 columns</p>\n",
+       "<p>64 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -881,10 +861,10 @@
        "Cognitive regression [HP:0034332]                    0/0      0%   0/6   \n",
        "Ectopia lentis [HP:0001083]                          0/0      0%   2/3   \n",
        "...                                                  ...     ...   ...   \n",
-       "Abnormality of urine homeostasis [HP:0003110]        0/0      0%   2/2   \n",
-       "Abnormal circulating homocysteine concentration...   0/0      0%   2/2   \n",
-       "Decreased head circumference [HP:0040195]            0/0      0%   2/2   \n",
-       "Aplasia/Hypoplasia of the cerebrum [HP:0007364]      0/0      0%   2/2   \n",
+       "Abnormal cerebral morphology [HP:0002060]            0/0      0%   2/2   \n",
+       "Abnormal brain morphology [HP:0012443]               0/0      0%   2/2   \n",
+       "Abnormal circulating cysteine concentration [HP...   0/0      0%   1/1   \n",
+       "Abnormal circulating non-proteinogenic amino ac...   0/0      0%   0/0   \n",
        "Abnormality of skull size [HP:0000240]               0/0      0%   2/2   \n",
        "\n",
        ">=1 allele of the variant 12_56004589_56004589_C_G         Neither          \\\n",
@@ -895,10 +875,10 @@
        "Cognitive regression [HP:0034332]                       0%    6/19     32%   \n",
        "Ectopia lentis [HP:0001083]                            67%    5/15     33%   \n",
        "...                                                    ...     ...     ...   \n",
-       "Abnormality of urine homeostasis [HP:0003110]         100%   12/12    100%   \n",
-       "Abnormal circulating homocysteine concentration...    100%     8/8    100%   \n",
-       "Decreased head circumference [HP:0040195]             100%     8/8    100%   \n",
-       "Aplasia/Hypoplasia of the cerebrum [HP:0007364]       100%     8/8    100%   \n",
+       "Abnormal cerebral morphology [HP:0002060]             100%     8/8    100%   \n",
+       "Abnormal brain morphology [HP:0012443]                100%     8/8    100%   \n",
+       "Abnormal circulating cysteine concentration [HP...    100%     6/6    100%   \n",
+       "Abnormal circulating non-proteinogenic amino ac...      0%     2/2    100%   \n",
        "Abnormality of skull size [HP:0000240]                100%     8/8    100%   \n",
        "\n",
        ">=1 allele of the variant 12_56004589_56004589_C_G                              \n",
@@ -909,16 +889,16 @@
        "Cognitive regression [HP:0034332]                   0.277764               1.0  \n",
        "Ectopia lentis [HP:0001083]                         0.528186               1.0  \n",
        "...                                                      ...               ...  \n",
-       "Abnormality of urine homeostasis [HP:0003110]       1.000000               1.0  \n",
-       "Abnormal circulating homocysteine concentration...  1.000000               1.0  \n",
-       "Decreased head circumference [HP:0040195]           1.000000               1.0  \n",
-       "Aplasia/Hypoplasia of the cerebrum [HP:0007364]     1.000000               1.0  \n",
+       "Abnormal cerebral morphology [HP:0002060]           1.000000               1.0  \n",
+       "Abnormal brain morphology [HP:0012443]              1.000000               1.0  \n",
+       "Abnormal circulating cysteine concentration [HP...  1.000000               1.0  \n",
+       "Abnormal circulating non-proteinogenic amino ac...  1.000000               1.0  \n",
        "Abnormality of skull size [HP:0000240]              1.000000               1.0  \n",
        "\n",
-       "[71 rows x 8 columns]"
+       "[64 rows x 8 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -953,7 +933,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
diff --git a/src/genophenocorr/analysis/__init__.py b/src/genophenocorr/analysis/__init__.py
index cf30e38c..dc0eb799 100644
--- a/src/genophenocorr/analysis/__init__.py
+++ b/src/genophenocorr/analysis/__init__.py
@@ -2,14 +2,14 @@
 
 from ._api import CohortAnalysis, GenotypePhenotypeAnalysisResult, HpoMtcReport
 from ._config import CohortAnalysisConfiguration, configure_cohort_analysis
-from ._gp_analysis import HeuristicSamplerMtcFilter, SpecifiedTermsMtcFilter, apply_predicates_on_patients
+from ._gp_analysis import HeuristicMtcFilter, SpecifiedTermsMtcFilter, apply_predicates_on_patients
 
 __all__ = [
     'configure_cohort_analysis',
     'CohortAnalysis', 'GenotypePhenotypeAnalysisResult',
     'CohortAnalysisConfiguration',
     'HpoMtcReport',
-    'HeuristicSamplerMtcFilter',
+    'HeuristicMtcFilter',
     'SpecifiedTermsMtcFilter',
     'apply_predicates_on_patients',
 ]
diff --git a/src/genophenocorr/analysis/_api.py b/src/genophenocorr/analysis/_api.py
index 42716a97..29b13147 100644
--- a/src/genophenocorr/analysis/_api.py
+++ b/src/genophenocorr/analysis/_api.py
@@ -28,8 +28,7 @@ def __init__(
         Args:
             filter_name: name of the MTC filter strategy (e.g. `heuristic sampler`)
             mtc_name:  name of the MTC function (e.g. `bonferroni`)
-            filter_results_map: mapping with reasons for filtering out a term as keys,
-             and counts of filtered terms as values
+            filter_results_map: mapping with reasons for filtering out a term as keys, and counts of filtered terms as values
             term_count: the number of HPO terms before filtering
         """
         self._filter_name = filter_name
@@ -40,28 +39,32 @@ def __init__(
     @property
     def filter_method(self) -> str:
         """
-        Get the name of the HpoMtcFilter method used.
+        Returns:
+            the name of the HpoMtcFilter method used.
         """
         return self._filter_name
 
     @property
     def skipped_terms_dict(self) -> typing.Mapping[str, int]:
         """
-        Get a mapping with reasons why an HPO term was skipped as keys and counts of the skipped terms as values.
+        Returns:
+            a mapping with reasons why an HPO term was skipped as keys and counts of the skipped terms as values.
         """
         return self._results_map
 
     @property
     def mtc_method(self) -> str:
         """
-        Get the name of the multiple testing correction method used (e.g. `bonferroni`).
+         Returns:
+             the name of the multiple testing correction method used (e.g. `bonferroni`).
         """
         return self._mtc_name
 
     @property
     def n_terms_tested(self) -> int:
         """
-        Get the number of HPO terms before filtering.
+        Returns:
+            the number of HPO terms before filtering.
         """
         return self._term_count
 
diff --git a/src/genophenocorr/analysis/_config.py b/src/genophenocorr/analysis/_config.py
index 4c2be0d0..280a4f0a 100644
--- a/src/genophenocorr/analysis/_config.py
+++ b/src/genophenocorr/analysis/_config.py
@@ -9,7 +9,7 @@
     ProtCachingMetadataService
 from ._api import CohortAnalysis, HpoMtcFilter
 from ._filter import SimplePhenotypeFilter
-from ._gp_analysis import FisherExactAnalyzer, IdentityTermMtcFilter, HeuristicSamplerMtcFilter, SpecifiedTermsMtcFilter
+from ._gp_analysis import FisherExactAnalyzer, IdentityTermMtcFilter, HeuristicMtcFilter, SpecifiedTermsMtcFilter
 from ._gp_impl import GpCohortAnalysis
 
 P_VAL_OPTIONS = (
@@ -64,7 +64,7 @@ def __init__(self):
         self._missing_implies_excluded = False
         self._pval_correction = 'bonferroni'
         self._mtc_alpha = .05
-        self._min_perc_patients_w_hpo = .1
+        self._min_perc_patients_w_hpo = .2
         self._include_sv = False
         self._mtc_strategy = MTC_Strategy.ALL_HPO_TERMS
         self._terms_to_test = set() ## only relevant for SPECIFIED_TERMS strategy
@@ -112,28 +112,10 @@ def pval_correction(self, pval_correction: typing.Optional[str]):
     def min_perc_patients_w_hpo(self) -> float:
         """
         A threshold for removing rare HPO terms, only the terms that are observed in at least this fraction of patients
-        will be retained for the analysis.
+        will be retained for the analysis (default 0.2).
         """
         return self._min_perc_patients_w_hpo
 
-    @min_perc_patients_w_hpo.setter
-    def min_perc_patients_w_hpo(self, min_perc_patients_w_hpo: float):
-        """
-        :param min_perc_patients_w_hpo: as a `float` with the lower acceptable bound of patients
-          for the phenotype feature to be included in the analysis
-          (e.g. `0.2` for needing 20% or more patients).
-        """
-        if not isinstance(min_perc_patients_w_hpo, float):
-            try:
-                min_perc_patients_w_hpo = float(min_perc_patients_w_hpo)
-            except ValueError:
-                self._logger.warning("min_perc_patients_w_hpo must be a number, but was %s. Using %f",
-                                     min_perc_patients_w_hpo, self._min_perc_patients_w_hpo)
-        if min_perc_patients_w_hpo > 1 or min_perc_patients_w_hpo <= 0:
-            self._logger.warning("min_perc_patients_w_hpo must be greater than 0 and at most 1, but was %f. Using %f",
-                                 min_perc_patients_w_hpo, self._min_perc_patients_w_hpo)
-        else:
-            self._min_perc_patients_w_hpo = min_perc_patients_w_hpo
 
     @property
     def mtc_alpha(self) -> float:
@@ -198,8 +180,21 @@ def mtc_strategy(self, strategy: MTC_Strategy):
         else:
             self._logger.warning('TODO DOCUMENT `include_sv` value %s. Using %s', strategy, self._mtc_strategy)
 
-    def heuristic_strategy(self):
+    def heuristic_strategy(self, threshold_HPO_observed_frequency: float=0.2):
         self.mtc_strategy = MTC_Strategy.HEURISTIC_SAMPLER
+        if not isinstance(threshold_HPO_observed_frequency, float):
+            try:
+                threshold_HPO_observed_frequency = float(threshold_HPO_observed_frequency)
+            except ValueError:
+                self._logger.warning("min_perc_patients_w_hpo must be a number, but was %s. Using %f",
+                                     threshold_HPO_observed_frequency, self._min_perc_patients_w_hpo)
+        if threshold_HPO_observed_frequency > 1 or threshold_HPO_observed_frequency <= 0:
+            errmsg = f"min_perc_patients_w_hpo must be greater than 0 and at most 1, but was {threshold_HPO_observed_frequency}. Fix this before continuingf",
+            self._logger.warning(errmsg)
+            raise ValueError(errmsg)
+        else:
+            self._min_perc_patients_w_hpo = threshold_HPO_observed_frequency
+        self._min_perc_patients_w_hpo = threshold_HPO_observed_frequency
 
     def specify_terms_strategy(self,  specified_term_set: typing.Iterable[typing.Union[str, hpotk.TermId]]):
         self.mtc_strategy = MTC_Strategy.SPECIFY_TERMS
@@ -240,7 +235,7 @@ def configure_cohort_analysis(cohort: Cohort,
 
     mtc_filter: HpoMtcFilter
     if config.mtc_strategy == MTC_Strategy.HEURISTIC_SAMPLER:
-        mtc_filter = HeuristicSamplerMtcFilter(hpo=hpo)
+        mtc_filter = HeuristicMtcFilter(hpo=hpo, hpo_term_frequency_filter = config._min_perc_patients_w_hpo)
     elif config.mtc_strategy == MTC_Strategy.SPECIFY_TERMS:
         validated_terms_to_test = _validate_terms_to_test(hpo, config.get_terms_to_test())
         mtc_filter = SpecifiedTermsMtcFilter(hpo=hpo, terms_to_test=validated_terms_to_test)
@@ -267,17 +262,17 @@ def configure_cohort_analysis(cohort: Cohort,
     )
 
 def _validate_terms_to_test(
-        hpo: hpotk.MinimalOntology, 
+        hpo: hpotk.MinimalOntology,
         terms_to_test: typing.Iterable[typing.Union[hpotk.TermId, str]],
     ) -> typing.Iterable[hpotk.TermId]:
     """
     Check that:
-     * all terms to test are valid TermIds/CURIES, 
-     * the term IDs are in used HPO, and 
+     * all terms to test are valid TermIds/CURIES,
+     * the term IDs are in used HPO, and
      * there is at least one term to test
     """
     validated_terms_to_test = set()
-    
+
     for term in terms_to_test:
         if isinstance(term, hpotk.TermId):
             pass
@@ -285,13 +280,13 @@ def _validate_terms_to_test(
             term = hpotk.TermId.from_curie(term)
         else:
             raise ValueError(f'{term} is neither a TermId nor a CURIE `str`!')
-        
+
         if term not in hpo:
             raise ValueError(f"HPO ID {term} not in HPO ontology {hpo.version}")
         validated_terms_to_test.add(term)
     if len(validated_terms_to_test) == 0:
         raise ValueError('Cannot run use {MTC_Strategy.SPECIFY_TERMS} with no HPO terms!')
-    
+
     return validated_terms_to_test
 
 
diff --git a/src/genophenocorr/analysis/_gp_analysis.py b/src/genophenocorr/analysis/_gp_analysis.py
index 4c940b4d..3aff1d80 100644
--- a/src/genophenocorr/analysis/_gp_analysis.py
+++ b/src/genophenocorr/analysis/_gp_analysis.py
@@ -160,9 +160,9 @@ def filter_terms_to_test(
     def filter_method_name(self) -> str:
         return "specified terms filter"
 
-class HeuristicSamplerMtcFilter(HpoMtcFilter):
+class HeuristicMtcFilter(HpoMtcFilter):
     """
-    `HeuristicSamplerMtcFilter` decides which phenotypes should be tested and which phenotypes are not worth testing.
+    `HeuristicMtcFilter` decides which phenotypes should be tested and which phenotypes are not worth testing.
 
     The class uses a number of heuristics and rules of thumb.
     1. Do not perform a test if there are less than min_annots annotations (default 2). That is, do not perform a
@@ -183,10 +183,16 @@ class HeuristicSamplerMtcFilter(HpoMtcFilter):
     def __init__(
             self,
             hpo: hpotk.MinimalOntology,
-            second_level_pval_threshold: float = 1e-5,
+            hpo_term_frequency_filter: float,
     ):
+        """
+
+        Args:
+            hpo: reference to HPO ontology object
+            hpo_term_frequency_filter: Minimum frequency for an HPO term to have in at least one of the genotype groups (e.g., 22% in missense and 3% in nonsense genotypes would be ok, but not 13% missense and 10% nonsense genotypes if the threshold is 0.2)
+        """
         self._hpo = hpo
-        self._second_level_pval_threshold = second_level_pval_threshold
+        self._hpo_term_frequency_filter = hpo_term_frequency_filter
         # The following numbers of total observations in the genotype groups can never be significant,
         # so we just skip them see above explanation
         self._powerless_set = {(2, 4), (4, 2), (2, 3), (3, 3), (2, 2), (3, 2)}
@@ -229,20 +235,25 @@ def filter_terms_to_test(
 
             counts_frame = all_counts[term_id]
             total = counts_frame.sum().sum()
+            max_freq = HeuristicMtcFilter.get_maximum_group_observed_HPO_frequency(counts_frame)
+            if max_freq < self._hpo_term_frequency_filter:
+                reason = f"Skipping term with maximum frequency that was less than threshold {self._hpo_term_frequency_filter}"
+                reason_for_filtering_out[reason] += 1
+
             if counts_frame.shape == (2, 2) and total < 7:
                 reason = f"Skipping term with only {total} observations (not powered for 2x2)"
                 reason_for_filtering_out[reason] += 1
                 continue
             # todo -- similar for (3,2)
-            if not HeuristicSamplerMtcFilter.some_cell_has_greater_than_one_count(counts_frame):
+            if not HeuristicMtcFilter.some_cell_has_greater_than_one_count(counts_frame):
                 reason = "Skipping term because no genotype has more than one observed HPO count"
                 reason_for_filtering_out[reason] += 1
                 continue
-            elif HeuristicSamplerMtcFilter.genotypes_have_same_hpo_proportions(counts_frame):
+            elif HeuristicMtcFilter.genotypes_have_same_hpo_proportions(counts_frame):
                 reason = "Skipping term because all genotypes have same HPO observed proportions"
                 reason_for_filtering_out[reason] += 1
                 continue
-            elif HeuristicSamplerMtcFilter.one_genotype_has_zero_hpo_observations(counts_frame):
+            elif HeuristicMtcFilter.one_genotype_has_zero_hpo_observations(counts_frame):
                 reason = "Skipping term because one genotype had zero observations"
                 reason_for_filtering_out[reason] += 1
                 continue
@@ -255,7 +266,7 @@ def filter_terms_to_test(
                         reason_for_filtering_out[reason] += 1
                         continue
 
-            total_HPO = HeuristicSamplerMtcFilter.get_number_of_observed_hpo_observations(counts_frame)
+            total_HPO = HeuristicMtcFilter.get_number_of_observed_hpo_observations(counts_frame)
             tested_counts_pf[term_id] = counts_frame
             ## Heuristic -- if a child of a second level term has at least 75% of the counts of its ancestor
             # second level term, then do not test because it is unlikely to add much insight
@@ -270,7 +281,7 @@ def filter_terms_to_test(
                 do_test = True
                 for descendant in self._hpo.graph.get_descendants(term_id):
                     if descendant in tested_counts_pf:
-                        total_d_hpo = HeuristicSamplerMtcFilter.get_number_of_observed_hpo_observations(
+                        total_d_hpo = HeuristicMtcFilter.get_number_of_observed_hpo_observations(
                             tested_counts_pf[descendant]
                         )
                         if total_d_hpo / total_HPO >= SECOND_LEVEL_TERM_THRESHOLD:
@@ -292,6 +303,15 @@ def filter_method_name(self) -> str:
     def get_number_of_observed_hpo_observations(counts_frame: pd.DataFrame) -> int:
         return counts_frame.loc[PatientCategories.YES].sum()
 
+    @staticmethod
+    def get_maximum_group_observed_HPO_frequency(counts_frame: pd.DataFrame) -> float:
+        """
+        Returns:
+            The maximum frequency of observed HPO annotations across all genotypes.
+        """
+        df = counts_frame.loc[PatientCategories.YES] / (counts_frame.loc[PatientCategories.YES] + counts_frame.loc[PatientCategories.NO])
+        return df.max()
+
     @staticmethod
     def one_genotype_has_zero_hpo_observations(counts: pd.DataFrame):
         if not isinstance(counts, pd.DataFrame):
diff --git a/src/genophenocorr/preprocessing/_uniprot.py b/src/genophenocorr/preprocessing/_uniprot.py
index adb9f0ff..28e3dd8e 100644
--- a/src/genophenocorr/preprocessing/_uniprot.py
+++ b/src/genophenocorr/preprocessing/_uniprot.py
@@ -85,6 +85,8 @@ def annotate(self, protein_id: str) -> ProteinMetadata:
         """
         if not isinstance(protein_id, str):
             raise ValueError(f'Protein ID must be a str but it was {type(protein_id)}')
+        if protein_id.startswith(" ") or protein_id.endswith(" "):
+            raise ValueError(f"Please remove whitespace from protein id: \"{protein_id}\" and try again!")
         if not protein_id.startswith("NP_"):
             raise ValueError(f"only works with a RefSeq database ID (e.g. NP_037407.4), but we got {protein_id}")
         api_url = self._url % protein_id


Test patch:
diff --git a/tests/analysis/test_examples.py b/tests/analysis/test_examples.py
index 6aa22040..0eef340e 100644
--- a/tests/analysis/test_examples.py
+++ b/tests/analysis/test_examples.py
@@ -65,8 +65,8 @@ def test_get_count(
         all_counts = results.all_counts
         assert isinstance(all_counts, typing.Mapping)
 
-        # We tested 26 HPO terms
-        assert len(all_counts) == 71
+        # We tested 69 HPO terms
+        assert len(all_counts) == 69
 
         # The index of all_counts is a Tuple with (HPO TermId, BooleanPredicate
         # Let's test Seizure - we should have one row for each Patient Predicate
diff --git a/tests/analysis/test_mtc_filter.py b/tests/analysis/test_mtc_filter.py
index 9559aeb1..3f90ea61 100644
--- a/tests/analysis/test_mtc_filter.py
+++ b/tests/analysis/test_mtc_filter.py
@@ -5,7 +5,7 @@
 import pandas as pd
 import pytest
 
-from genophenocorr.analysis import HeuristicSamplerMtcFilter, SpecifiedTermsMtcFilter, apply_predicates_on_patients
+from genophenocorr.analysis import HeuristicMtcFilter, SpecifiedTermsMtcFilter, apply_predicates_on_patients
 from genophenocorr.analysis.predicate import PatientCategories, GenotypePolyPredicate
 from genophenocorr.analysis.predicate.phenotype import PhenotypePolyPredicate
 from genophenocorr.model import Cohort
@@ -14,8 +14,9 @@
 class TestHeuristicSamplerMtcFilter:
 
     @pytest.fixture
-    def mtc_filter(self, hpo: hpotk.MinimalOntology) -> HeuristicSamplerMtcFilter:
-        return HeuristicSamplerMtcFilter(hpo=hpo)
+    def mtc_filter(self, hpo: hpotk.MinimalOntology) -> HeuristicMtcFilter:
+        default_freq_threshold=0.2
+        return HeuristicMtcFilter(hpo=hpo, hpo_term_frequency_filter=default_freq_threshold)
 
     @pytest.fixture(scope='class')
     def patient_counts(
@@ -50,7 +51,7 @@ def test_one_genotype_has_zero_hpo_observations(
     ):
         counts_df = self.prepare_counts_df(counts)
 
-        actual = HeuristicSamplerMtcFilter.one_genotype_has_zero_hpo_observations(counts=counts_df)
+        actual = HeuristicMtcFilter.one_genotype_has_zero_hpo_observations(counts=counts_df)
 
         assert actual == expected
 
@@ -74,7 +75,7 @@ def test_some_cell_has_greater_than_one_count(
     ):
         counts_df = self.prepare_counts_df(counts)
 
-        actual = HeuristicSamplerMtcFilter.some_cell_has_greater_than_one_count(counts=counts_df)
+        actual = HeuristicMtcFilter.some_cell_has_greater_than_one_count(counts=counts_df)
 
         assert actual == expected
 
@@ -93,13 +94,13 @@ def test_genotypes_have_same_hpo_proportions(
     ):
         counts_df = self.prepare_counts_df(counts)
 
-        actual = HeuristicSamplerMtcFilter.genotypes_have_same_hpo_proportions(counts=counts_df)
+        actual = HeuristicMtcFilter.genotypes_have_same_hpo_proportions(counts=counts_df)
 
         assert actual == expected
 
     def test_filter_terms_to_test(
             self,
-            mtc_filter: HeuristicSamplerMtcFilter,
+            mtc_filter: HeuristicMtcFilter,
             patient_counts: typing.Tuple[
                 typing.Mapping[hpotk.TermId, int],
                 typing.Mapping[hpotk.TermId, pd.DataFrame],
@@ -153,3 +154,52 @@ def prepare_counts_df(counts):
         values = np.array(counts).reshape((2, 2))
 
         return pd.DataFrame(data=values, index=index, columns=columns)
+
+
+    def test_min_observed_HPO_threshold(
+        self,
+        patient_counts: typing.Tuple[
+            typing.Mapping[hpotk.TermId, int],
+            typing.Mapping[hpotk.TermId, pd.DataFrame],
+            ],
+    ):
+        """
+        In our heuristic filter, we only test terms that have at least a threshold
+        frequency in at least one of the groups. We use the "all counts" datastructure, that
+        is a dictionary whose keys are hpotoolkit TermIds and whose values are pandas DataFrames
+        with 2x2 contingenicy tables of counts. For instance, each column will have one row for
+        PatientCategories.YES and one for PatientCategories.NO, indicating counts of measured observed/excluded
+        HPO phenotypes. Each column is a certain genotype, e.g., MISSENSE or NON-MISSENSE. We want the
+        function to return the maximum frequency. In each column, the frequency is calculate by
+        PatientCategories.YES / (PatientCategories.YES+PatientCategories.NO). This function tests that this works
+        for all of the HPO terms in the dictionary.
+        """
+        EPSILON = 0.001
+        _, all_counts = patient_counts
+        # Ectopia lentis HP:0001083  (6 9  1 2), freqs are 6/15=0.4 and 1/3=0.33
+        ectopia = all_counts[hpotk.TermId.from_curie("HP:0001083")]
+        max_f = HeuristicMtcFilter.get_maximum_group_observed_HPO_frequency(ectopia)
+        assert max_f == pytest.approx(0.4, abs=EPSILON)
+        
+        # Seizure HP:0001250 (17 7 11 0), freqs are 17/24=0.7083 and 11/11=1
+        seizure = all_counts[hpotk.TermId.from_curie("HP:0001250")]
+        max_f = HeuristicMtcFilter.get_maximum_group_observed_HPO_frequency(seizure)
+        assert max_f == pytest.approx(1.0, abs=EPSILON)
+        
+        # Sulfocysteinuria HP:0032350 (11 0 2 0), freqs are both 1
+        sulfocysteinuria = all_counts[hpotk.TermId.from_curie("HP:0032350")]
+        max_f = HeuristicMtcFilter.get_maximum_group_observed_HPO_frequency(sulfocysteinuria)
+        assert max_f == pytest.approx(1.0, abs=EPSILON)
+        
+        # Neurodevelopmental delay HP:0012758 (4 13 4 4), freqs are 4/17 = 0.235 and 4/8=0.5
+        ndelay = all_counts[hpotk.TermId.from_curie("HP:0012758")]
+        max_f = HeuristicMtcFilter.get_maximum_group_observed_HPO_frequency(ndelay)
+        assert max_f == pytest.approx(0.5, abs=EPSILON)
+        
+        # Hypertonia HP:0001276 (7 9 4 3) fresa are 7/16=0.4375 and 4/7=0.5714
+        hypertonia  = all_counts[hpotk.TermId.from_curie("HP:0001276")]
+        max_f = HeuristicMtcFilter.get_maximum_group_observed_HPO_frequency(hypertonia)
+        assert max_f == pytest.approx(0.5714, abs=EPSILON)
+
+
+