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    "# JUMP-Lite\n",
    "\n",
    "{func}`~mantispy.ds.jump_lite` reads JUMP-Lite Target-2 {cite:p}`Munoz_2026`: four plates of the JUMP Target-2\n",
    "plate map, one from each of four laboratories, with the same 1,536 wells measured six ways. Four are learned\n",
    "embeddings, one is the same architecture as one of them with untrained weights, and `cp_measure` is a\n",
    "CellProfiler-equivalent feature set. {func}`~mantispy.ds.jump_lite_targets` returns the RefChemDB gene\n",
    "annotations of its compounds, in the shape {func}`~mantispy.metrics.known_relationships` reads.\n",
    "\n",
    "## Source\n",
    "\n",
    "Three things about how the features were made bound every result computed from them.\n",
    "\n",
    "\n",
    "- **The images are lossy.** Every one of the six blocks was computed on JPEG-XL medium-quality images. Texture\n",
    "  measurements and convolutional features are the two things most sensitive to compression, so this is not a\n",
    "  neutral choice, and it is a property of the benchmark rather than of your screen.\n",
    "- **One field of view per well.** Full Target-2 acquires nine; JUMP-Lite keeps one. So every cell count below\n",
    "  is cells *in one field*, and [Bringing your own embedding](../tutorials/data/embeddings.ipynb) says how to\n",
    "  aggregate a model's per-field output when there is more than one.\n",
    "- **Only one of the six blocks needed segmentation.** `cp_measure` measures objects, so it needs a nucleus and\n",
    "  a cell body found for every cell. The five embeddings read the whole field and segment nothing. That is the\n",
    "  real difference between the embeddings and `cp_measure`, and it cuts both ways.\n",
    "\n",
    "## Used in\n",
    "\n",
    "- [Bringing your own embedding](../tutorials/data/embeddings.ipynb)\n",
    "- [Which measurements moved](../tutorials/phenotypes/which_features_moved.ipynb)\n",
    "- [Learned embeddings against CellProfiler](../case_studies/learned_embeddings.ipynb)"
   ]
  },
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     "data": {
      "text/plain": [
       "('openphenom', 'dinov2', 'dinov2_random', 'subcell', 'morphem', 'cp_measure')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mantispy as mt\n",
    "\n",
    "mt.ds.JUMP_LITE_MODELS"
   ]
  },
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   "id": "c002",
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      "text/plain": [
       "AnnData object with n_obs × n_vars = 1536 × 384\n",
       "    obs: 'Metadata_id', 'Metadata_Source', 'Metadata_Batch', 'Metadata_Plate', 'Metadata_Well', 'Metadata_Site', 'Metadata_model', 'Metadata_dataset', 'Metadata_compression', 'Metadata_Row', 'Metadata_Col', 'Metadata_CellCount', 'Metadata_JCP2022', 'Metadata_InChIKey', 'Metadata_Perturbation', 'Metadata_Perturbation_Type', 'Metadata_Control_Type', 'Metadata_Control'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = mt.ds.jump_lite(\"openphenom\")\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c003",
   "metadata": {},
   "source": [
    "The four batches are four different laboratories running one plate map, which makes this a batch-correction\n",
    "problem with a clean design: whatever separates `source_3` from `source_6` is not biology, because they\n",
    "received the same compounds in the same wells."
   ]
  },
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   "id": "c004",
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>plate</th>\n",
       "      <th>wells</th>\n",
       "      <th>controls</th>\n",
       "      <th>median_cells</th>\n",
       "      <th>median_cells, controls</th>\n",
       "      <th>compounds</th>\n",
       "      <th>wells under 50 cells</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metadata_Source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>source_3</th>\n",
       "      <td>JCPQC016</td>\n",
       "      <td>384</td>\n",
       "      <td>64</td>\n",
       "      <td>264.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>302</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_4</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>384</td>\n",
       "      <td>64</td>\n",
       "      <td>189.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>302</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_5</th>\n",
       "      <td>ACPJUM012</td>\n",
       "      <td>384</td>\n",
       "      <td>64</td>\n",
       "      <td>336.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>302</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_6</th>\n",
       "      <td>110000293081</td>\n",
       "      <td>384</td>\n",
       "      <td>64</td>\n",
       "      <td>254.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>302</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        plate  wells  controls  median_cells  \\\n",
       "Metadata_Source                                                \n",
       "source_3             JCPQC016    384        64         264.0   \n",
       "source_4           BR00121438    384        64         189.0   \n",
       "source_5            ACPJUM012    384        64         336.0   \n",
       "source_6         110000293081    384        64         254.0   \n",
       "\n",
       "                 median_cells, controls  compounds  wells under 50 cells  \n",
       "Metadata_Source                                                           \n",
       "source_3                          282.0        302                    28  \n",
       "source_4                          210.0        302                    61  \n",
       "source_5                          357.0        302                    13  \n",
       "source_6                          266.0        302                    37  "
      ]
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     "execution_count": 3,
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   "source": [
    "counts = adata.obs.groupby(\"Metadata_Source\", observed=True).agg(\n",
    "    plate=(\"Metadata_Plate\", \"first\"),\n",
    "    wells=(\"Metadata_Well\", \"size\"),\n",
    "    controls=(\"Metadata_Control\", \"sum\"),\n",
    "    median_cells=(\"Metadata_CellCount\", \"median\"),\n",
    ")\n",
    "# Controls got no compound: their count is the site's own density.\n",
    "counts[\"median_cells, controls\"] = (\n",
    "    adata.obs[adata.obs[\"Metadata_Control\"]].groupby(\"Metadata_Source\", observed=True)[\"Metadata_CellCount\"].median()\n",
    ")\n",
    "counts[\"compounds\"] = adata.obs.groupby(\"Metadata_Source\", observed=True)[\"Metadata_Perturbation\"].nunique()\n",
    "thin = adata.obs[\"Metadata_CellCount\"] < 50\n",
    "counts[\"wells under 50 cells\"] = thin.groupby(adata.obs[\"Metadata_Source\"], observed=True).sum()\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c005",
   "metadata": {},
   "source": [
    "## What it looks like\n",
    "\n",
    "One number per laboratory hides where the wells are. Four 384-well plates, coloured by how many cells were\n",
    "found in the field."
   ]
  },
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       "{'share of the plate on the edge': '20%',\n",
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",
      "text/plain": [
       "<Figure size 1600x320 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.plate(adata, color=\"Metadata_CellCount\", groupby=\"Metadata_Source\", ncols=4)\n",
    "\n",
    "mt.pl.cell_counts(adata, groupby=\"Metadata_Source\")\n",
    "\n",
    "row, column = adata.obs[\"Metadata_Well\"].str[0], adata.obs[\"Metadata_Well\"].str[1:].astype(int)\n",
    "edge = row.isin([\"A\", \"P\"]) | column.isin([1, 24])\n",
    "{\n",
    "    \"share of the plate on the edge\": f\"{edge.mean():.0%}\",\n",
    "    \"share of thin wells on the edge\": f\"{edge[thin].mean():.0%}\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c007",
   "metadata": {},
   "source": [
    "Two facts to know before using it. The sites run at different densities before any compound acts: the\n",
    "control wells, which received none, hold a median of 210 cells at `source_4` against 357 at `source_5`. And\n",
    "the thin wells are not banked against the edge, where evaporation would put them. The edge is a fifth of the\n",
    "plate but holds only a tenth of the thin wells."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}