From 684a3e84866bfdf75065823379e24030d2cb4bd0 Mon Sep 17 00:00:00 2001
From: daveringelberg <141670222+daveringelberg@users.noreply.github.com>
Date: Wed, 9 Sep 2026 12:50:22 +0200
Subject: [PATCH 1/2] Add executed Hill dose-response tutorial

---
 perturbation_space.ipynb | 416 ++++++++++++++++++++++++++-------------
 1 file changed, 278 insertions(+), 138 deletions(-)

diff --git a/perturbation_space.ipynb b/perturbation_space.ipynb
index 72f90ee..fcbd895 100644
--- a/perturbation_space.ipynb
+++ b/perturbation_space.ipynb
@@ -2389,7 +2389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-07-06T12:08:42.714265Z",
@@ -2399,30 +2399,6 @@
     }
    },
    "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d43b1d8c9cc044738b673481f7a5d3b6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
@@ -2453,194 +2429,170 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>BMS</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.458705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>BMS</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>16.440267</td>\n",
+       "      <td>0.455309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>1</th>\n",
        "      <td>BMS</td>\n",
        "      <td>0.5</td>\n",
-       "      <td>19.021279</td>\n",
+       "      <td>2.837909</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>2</th>\n",
        "      <td>BMS</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>15.553012</td>\n",
+       "      <td>7.724844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>3</th>\n",
        "      <td>BMS</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>17.927020</td>\n",
+       "      <td>9.949092</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>4</th>\n",
        "      <td>BMS</td>\n",
        "      <td>10.0</td>\n",
-       "      <td>18.802652</td>\n",
+       "      <td>9.459776</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>5</th>\n",
        "      <td>BMS</td>\n",
        "      <td>50.0</td>\n",
-       "      <td>1.760868</td>\n",
+       "      <td>6.352766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>6</th>\n",
        "      <td>BMS</td>\n",
        "      <td>100.0</td>\n",
-       "      <td>0.252982</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Dex</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.462214</td>\n",
+       "      <td>10.776962</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>7</th>\n",
        "      <td>Dex</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>13.983747</td>\n",
+       "      <td>2.784808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>8</th>\n",
        "      <td>Dex</td>\n",
        "      <td>0.5</td>\n",
-       "      <td>14.442097</td>\n",
+       "      <td>3.937478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>9</th>\n",
        "      <td>Dex</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>15.757915</td>\n",
+       "      <td>3.862224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>10</th>\n",
        "      <td>Dex</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>14.573900</td>\n",
+       "      <td>3.799150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>11</th>\n",
        "      <td>Dex</td>\n",
        "      <td>10.0</td>\n",
-       "      <td>15.343917</td>\n",
+       "      <td>4.054644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>12</th>\n",
        "      <td>Dex</td>\n",
        "      <td>50.0</td>\n",
-       "      <td>14.910395</td>\n",
+       "      <td>3.695452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>13</th>\n",
        "      <td>Dex</td>\n",
        "      <td>100.0</td>\n",
-       "      <td>14.220436</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Nutlin</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.569830</td>\n",
+       "      <td>3.588994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>14</th>\n",
        "      <td>Nutlin</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>14.341032</td>\n",
+       "      <td>0.207008</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>15</th>\n",
        "      <td>Nutlin</td>\n",
        "      <td>0.5</td>\n",
-       "      <td>14.133695</td>\n",
+       "      <td>0.505313</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>16</th>\n",
        "      <td>Nutlin</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>13.840254</td>\n",
+       "      <td>0.801843</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>17</th>\n",
        "      <td>Nutlin</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>17.403937</td>\n",
+       "      <td>4.162573</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>18</th>\n",
        "      <td>Nutlin</td>\n",
        "      <td>10.0</td>\n",
-       "      <td>18.150556</td>\n",
+       "      <td>5.416453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>19</th>\n",
        "      <td>Nutlin</td>\n",
        "      <td>50.0</td>\n",
-       "      <td>12.122952</td>\n",
+       "      <td>5.025462</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>20</th>\n",
        "      <td>Nutlin</td>\n",
        "      <td>100.0</td>\n",
-       "      <td>0.012348</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>SAHA</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.493444</td>\n",
+       "      <td>13.032257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>21</th>\n",
        "      <td>SAHA</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>16.731112</td>\n",
+       "      <td>1.995622</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>22</th>\n",
        "      <td>SAHA</td>\n",
        "      <td>0.5</td>\n",
-       "      <td>19.180071</td>\n",
+       "      <td>8.138505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>23</th>\n",
        "      <td>SAHA</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>25.357111</td>\n",
+       "      <td>13.982469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>24</th>\n",
        "      <td>SAHA</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>23.126392</td>\n",
+       "      <td>15.642829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>25</th>\n",
        "      <td>SAHA</td>\n",
        "      <td>10.0</td>\n",
-       "      <td>23.113911</td>\n",
+       "      <td>15.953979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>26</th>\n",
        "      <td>SAHA</td>\n",
        "      <td>50.0</td>\n",
-       "      <td>24.790767</td>\n",
+       "      <td>15.948679</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>31</th>\n",
+       "      <th>27</th>\n",
        "      <td>SAHA</td>\n",
        "      <td>100.0</td>\n",
-       "      <td>20.902018</td>\n",
+       "      <td>14.004499</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2648,53 +2600,241 @@
       ],
       "text/plain": [
        "   perturbation   dose   distance\n",
-       "0           BMS    0.0  14.458705\n",
-       "1           BMS    0.1  16.440267\n",
-       "2           BMS    0.5  19.021279\n",
-       "3           BMS    1.0  15.553012\n",
-       "4           BMS    5.0  17.927020\n",
-       "5           BMS   10.0  18.802652\n",
-       "6           BMS   50.0   1.760868\n",
-       "7           BMS  100.0   0.252982\n",
-       "8           Dex    0.0  13.462214\n",
-       "9           Dex    0.1  13.983747\n",
-       "10          Dex    0.5  14.442097\n",
-       "11          Dex    1.0  15.757915\n",
-       "12          Dex    5.0  14.573900\n",
-       "13          Dex   10.0  15.343917\n",
-       "14          Dex   50.0  14.910395\n",
-       "15          Dex  100.0  14.220436\n",
-       "16       Nutlin    0.0  14.569830\n",
-       "17       Nutlin    0.1  14.341032\n",
-       "18       Nutlin    0.5  14.133695\n",
-       "19       Nutlin    1.0  13.840254\n",
-       "20       Nutlin    5.0  17.403937\n",
-       "21       Nutlin   10.0  18.150556\n",
-       "22       Nutlin   50.0  12.122952\n",
-       "23       Nutlin  100.0   0.012348\n",
-       "24         SAHA    0.0  15.493444\n",
-       "25         SAHA    0.1  16.731112\n",
-       "26         SAHA    0.5  19.180071\n",
-       "27         SAHA    1.0  25.357111\n",
-       "28         SAHA    5.0  23.126392\n",
-       "29         SAHA   10.0  23.113911\n",
-       "30         SAHA   50.0  24.790767\n",
-       "31         SAHA  100.0  20.902018"
+       "0           BMS    0.1   0.455309\n",
+       "1           BMS    0.5   2.837909\n",
+       "2           BMS    1.0   7.724844\n",
+       "3           BMS    5.0   9.949092\n",
+       "4           BMS   10.0   9.459776\n",
+       "5           BMS   50.0   6.352766\n",
+       "6           BMS  100.0  10.776962\n",
+       "7           Dex    0.1   2.784808\n",
+       "8           Dex    0.5   3.937478\n",
+       "9           Dex    1.0   3.862224\n",
+       "10          Dex    5.0   3.799150\n",
+       "11          Dex   10.0   4.054644\n",
+       "12          Dex   50.0   3.695452\n",
+       "13          Dex  100.0   3.588994\n",
+       "14       Nutlin    0.1   0.207008\n",
+       "15       Nutlin    0.5   0.505313\n",
+       "16       Nutlin    1.0   0.801843\n",
+       "17       Nutlin    5.0   4.162573\n",
+       "18       Nutlin   10.0   5.416453\n",
+       "19       Nutlin   50.0   5.025462\n",
+       "20       Nutlin  100.0  13.032257\n",
+       "21         SAHA    0.1   1.995622\n",
+       "22         SAHA    0.5   8.138505\n",
+       "23         SAHA    1.0  13.982469\n",
+       "24         SAHA    5.0  15.642829\n",
+       "25         SAHA   10.0  15.953979\n",
+       "26         SAHA   50.0  15.948679\n",
+       "27         SAHA  100.0  14.004499"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "sciplex = pt.dt.srivatsan_2020_sciplex2()\n",
+    "# Use assigned zero-dose samples as the reference, not unassigned cells labelled control.\n",
+    "sciplex = sciplex[sciplex.obs[\"dose_value\"].notna()].copy()\n",
+    "sciplex.obs[\"dose_value\"] = sciplex.obs[\"dose_value\"].astype(float)\n",
+    "sciplex.obs[\"perturbation\"] = sciplex.obs[\"perturbation\"].astype(str)\n",
+    "sciplex.obs.loc[sciplex.obs[\"dose_value\"] == 0, \"perturbation\"] = \"zero_dose\"\n",
     "sc.pp.normalize_total(sciplex, target_sum=1e4)\n",
     "sc.pp.log1p(sciplex)\n",
     "sc.pp.pca(sciplex)\n",
     "\n",
     "ps = pt.tl.PseudobulkSpace()\n",
-    "ps.dose_response(sciplex, target_col=\"perturbation\", dose_col=\"dose_value\", embedding_key=\"X_pca\")"
+    "responses = ps.dose_response(sciplex, dose_col=\"dose_value\", reference_key=\"zero_dose\", embedding_key=\"X_pca\")\n",
+    "responses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Hill-curve fitting\n",
+    "\n",
+    "`fit_dose_response` fits a four-parameter Hill curve to each perturbation's dose-response measurements. Here, EC50 is the concentration halfway between the fitted low- and high-dose transcriptomic-distance responses; it is not a viability IC50.\n",
+    "\n",
+    "The result contains the curve parameters, an approximate EC50 standard error, R-squared and whether EC50 lies within the tested positive-dose range. We enable user warnings for this fit, including warnings about uncertainty that cannot be estimated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UserWarning: Cannot estimate the EC50 standard error for perturbation 'Dex'. Inspect the dose range and fitted curve before interpreting the estimate.\n",
+      "UserWarning: Cannot estimate the EC50 standard error for perturbation 'Nutlin'. Inspect the dose range and fitted curve before interpreting the estimate.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perturbation</th>\n",
+       "      <th>e0</th>\n",
+       "      <th>emax</th>\n",
+       "      <th>hill_coefficient</th>\n",
+       "      <th>ec50</th>\n",
+       "      <th>ec50_standard_error</th>\n",
+       "      <th>r_squared</th>\n",
+       "      <th>midpoint_in_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMS</td>\n",
+       "      <td>0.449911</td>\n",
+       "      <td>9.133957</td>\n",
+       "      <td>3.781633</td>\n",
+       "      <td>6.465416e-01</td>\n",
+       "      <td>0.200065</td>\n",
+       "      <td>0.875171</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Dex</td>\n",
+       "      <td>-1.774093</td>\n",
+       "      <td>3.822990</td>\n",
+       "      <td>13.239854</td>\n",
+       "      <td>8.942640e-02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.868485</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Nutlin</td>\n",
+       "      <td>-0.031537</td>\n",
+       "      <td>782497.161279</td>\n",
+       "      <td>0.462775</td>\n",
+       "      <td>2.801340e+12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.853778</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SAHA</td>\n",
+       "      <td>1.942545</td>\n",
+       "      <td>15.388201</td>\n",
+       "      <td>3.333287</td>\n",
+       "      <td>5.242065e-01</td>\n",
+       "      <td>0.050140</td>\n",
+       "      <td>0.984326</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  perturbation        e0           emax  hill_coefficient          ec50  \\\n",
+       "0          BMS  0.449911       9.133957          3.781633  6.465416e-01   \n",
+       "1          Dex -1.774093       3.822990         13.239854  8.942640e-02   \n",
+       "2       Nutlin -0.031537  782497.161279          0.462775  2.801340e+12   \n",
+       "3         SAHA  1.942545      15.388201          3.333287  5.242065e-01   \n",
+       "\n",
+       "   ec50_standard_error  r_squared  midpoint_in_range  \n",
+       "0             0.200065   0.875171               True  \n",
+       "1                  NaN   0.868485              False  \n",
+       "2                  NaN   0.853778              False  \n",
+       "3             0.050140   0.984326               True  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with warnings.catch_warnings(record=True) as fit_warnings:\n",
+    "    warnings.simplefilter(\"always\", UserWarning)\n",
+    "    fits = ps.fit_dose_response(responses)\n",
+    "for warning in fit_warnings:\n",
+    "    print(f\"{warning.category.__name__}: {warning.message}\")\n",
+    "fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the measured responses alongside the fitted curves with Matplotlib. Each point is the E-distance from the pooled zero-dose reference for one perturbation and dose. Reference samples are not returned as separate response measurements. The curve is evaluated over the measured positive-dose range on a logarithmic x-axis, and a dotted line marks EC50 when it lies within that range. Doses retain the dataset's recorded units; verify their mapping to physical concentrations before comparing potency between compounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from scipy.special import expit\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(9, 6), layout=\"constrained\")\n",
+    "for ax, fit in zip(axes.flat, fits.itertuples(index=False), strict=True):\n",
+    "    measured = responses.loc[responses[\"perturbation\"] == fit.perturbation]\n",
+    "    dose_grid = np.geomspace(measured[\"dose\"].min(), measured[\"dose\"].max(), 200)\n",
+    "    fraction = expit(fit.hill_coefficient * (np.log(dose_grid) - np.log(fit.ec50)))\n",
+    "    predicted = fit.e0 + (fit.emax - fit.e0) * fraction\n",
+    "\n",
+    "    ax.scatter(measured[\"dose\"], measured[\"distance\"], label=\"Measured response\", zorder=3)\n",
+    "    ax.plot(dose_grid, predicted, color=\"tab:orange\", label=\"Hill fit\")\n",
+    "    if fit.midpoint_in_range:\n",
+    "        ax.axvline(fit.ec50, color=\"0.5\", linestyle=\":\", label=\"EC50\")\n",
+    "    ax.set_xscale(\"log\")\n",
+    "    ax.set(title=fit.perturbation, xlabel=\"Dose (dataset units)\", ylabel=\"E-distance from control\")\n",
+    "    ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Dex and Nutlin fits have `NaN` standard errors and midpoints outside the tested range. Dex is already near its fitted plateau at the lowest positive doses, while Nutlin does not show a clear high-dose plateau. Their EC50 estimates are not reliable. BMS and SAHA have in-range midpoints and finite standard errors, but these indicators alone do not establish reliable potency estimates.\n",
+    "\n",
+    "Inspect whether the measurements constrain both plateaus and whether a Hill curve follows the dose-response pattern. A high R-squared, small standard error or an in-range EC50 alone does not establish a reliable fit. If EC50 is outside the measured range, it is an extrapolation."
    ]
   }
  ],

From 2134842066e7054ba6f466972933fd7481dea06e Mon Sep 17 00:00:00 2001
From: daveringelberg <141670222+daveringelberg@users.noreply.github.com>
Date: Sun, 13 Sep 2026 08:46:46 +0200
Subject: [PATCH 2/2] Update Hill tutorial for AnnData results and assay inputs

---
 perturbation_space.ipynb | 105 +++++++++++++++++++++++++++++++++++++--
 1 file changed, 100 insertions(+), 5 deletions(-)

diff --git a/perturbation_space.ipynb b/perturbation_space.ipynb
index fcbd895..5d49030 100644
--- a/perturbation_space.ipynb
+++ b/perturbation_space.ipynb
@@ -2639,9 +2639,8 @@
     "sciplex = pt.dt.srivatsan_2020_sciplex2()\n",
     "# Use assigned zero-dose samples as the reference, not unassigned cells labelled control.\n",
     "sciplex = sciplex[sciplex.obs[\"dose_value\"].notna()].copy()\n",
-    "sciplex.obs[\"dose_value\"] = sciplex.obs[\"dose_value\"].astype(float)\n",
-    "sciplex.obs[\"perturbation\"] = sciplex.obs[\"perturbation\"].astype(str)\n",
-    "sciplex.obs.loc[sciplex.obs[\"dose_value\"] == 0, \"perturbation\"] = \"zero_dose\"\n",
+    "sciplex.obs[\"perturbation\"] = sciplex.obs[\"perturbation\"].cat.add_categories([\"zero_dose\"])\n",
+    "sciplex.obs.loc[sciplex.obs[\"dose_value\"] == \"0\", \"perturbation\"] = \"zero_dose\"\n",
     "sc.pp.normalize_total(sciplex, target_sum=1e4)\n",
     "sc.pp.log1p(sciplex)\n",
     "sc.pp.pca(sciplex)\n",
@@ -2659,7 +2658,7 @@
     "\n",
     "`fit_dose_response` fits a four-parameter Hill curve to each perturbation's dose-response measurements. Here, EC50 is the concentration halfway between the fitted low- and high-dose transcriptomic-distance responses; it is not a viability IC50.\n",
     "\n",
-    "The result contains the curve parameters, an approximate EC50 standard error, R-squared and whether EC50 lies within the tested positive-dose range. We enable user warnings for this fit, including warnings about uncertainty that cannot be estimated."
+    "The fit table is stored in `sciplex.uns[\"dose_response\"][\"fits\"]`, with one row per perturbation. It contains the curve parameters, an approximate EC50 standard error, R-squared and whether EC50 lies within the tested positive-dose range. We enable user warnings for this fit, including warnings about uncertainty that cannot be estimated."
    ]
   },
   {
@@ -2777,9 +2776,10 @@
    "source": [
     "with warnings.catch_warnings(record=True) as fit_warnings:\n",
     "    warnings.simplefilter(\"always\", UserWarning)\n",
-    "    fits = ps.fit_dose_response(responses)\n",
+    "    ps.fit_dose_response(sciplex, responses)\n",
     "for warning in fit_warnings:\n",
     "    print(f\"{warning.category.__name__}: {warning.message}\")\n",
+    "fits = sciplex.uns[\"dose_response\"][\"fits\"]\n",
     "fits"
    ]
   },
@@ -2836,6 +2836,101 @@
     "\n",
     "Inspect whether the measurements constrain both plateaus and whether a Hill curve follows the dose-response pattern. A high R-squared, small standard error or an in-range EC50 alone does not establish a reliable fit. If EC50 is outside the measured range, it is an extrapolation."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Fitting an assay response\n",
+    "\n",
+    "An existing assay-response table can go directly into `fit_dose_response()`, without `dose_response()`.\n",
+    "This example uses simulated fractional inhibition (0 = none, 1 = complete) at concentrations in micromolar; replace `assay` with your own control-normalized measurements, for example from `pd.read_csv(...)`.\n",
+    "`response_type=\"inhibition\"` selects relative IC50 terminology, not normalization. AnnData stores the results; no single-cell matrix is needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perturbation</th>\n",
+       "      <th>e0</th>\n",
+       "      <th>emax</th>\n",
+       "      <th>hill_coefficient</th>\n",
+       "      <th>ic50</th>\n",
+       "      <th>ic50_standard_error</th>\n",
+       "      <th>r_squared</th>\n",
+       "      <th>midpoint_in_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>drug</td>\n",
+       "      <td>-2.267090e-11</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.025902e-08</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  perturbation            e0  emax  hill_coefficient  ic50  \\\n",
+       "0         drug -2.267090e-11   1.0               1.0  10.0   \n",
+       "\n",
+       "   ic50_standard_error  r_squared  midpoint_in_range  \n",
+       "0         1.025902e-08        1.0               True  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import pertpy as pt\n",
+    "from anndata import AnnData\n",
+    "\n",
+    "doses = pd.Series([0, 1, 3, 10, 30, 100])\n",
+    "assay = pd.DataFrame({\"perturbation\": \"drug\", \"dose\": doses, \"inhibition\": doses / (10 + doses)})\n",
+    "assay_adata = AnnData()\n",
+    "\n",
+    "ps = pt.tl.PseudobulkSpace()\n",
+    "ps.fit_dose_response(\n",
+    "    assay_adata,\n",
+    "    assay,\n",
+    "    response_col=\"inhibition\",\n",
+    "    response_type=\"inhibition\",\n",
+    ")\n",
+    "assay_adata.uns[\"dose_response\"][\"fits\"]"
+   ]
   }
  ],
  "metadata": {