{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "05b7b43f",
   "metadata": {},
   "source": [
    "# Predicting Gene Expression with Decima"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42034677",
   "metadata": {},
   "source": [
    "Decima allows prediction of gene expression at the cell type level, and this tutorial demonstrates how to leverage the prediction API for both genes in the training data and custom genes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1898aff7",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "### Precomputed Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4634c23",
   "metadata": {},
   "source": [
    "Scores for all genes in the training data are precomputed and saved to metadata h5ad object for each model replicate and are available under the `DecimaResult` class. `predicted_expression_matrix` class returns predicted average gene expression across the replicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b76abce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:24:54.170009Z",
     "iopub.status.busy": "2025-11-21T06:24:54.169485Z",
     "iopub.status.idle": "2025-11-21T06:25:32.182146Z",
     "shell.execute_reply": "2025-11-21T06:25:32.181240Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
      "  warnings.warn(\n",
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:08.3 (375.3MB/s)\n"
     ]
    },
    {
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       "      <th>STRADA</th>\n",
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       "<p>8856 rows × 18457 columns</p>\n",
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       "            STRADA      ETV4     USP25    ZSWIM5  C21orf58  MIR497HG  \\\n",
       "agg_0     2.973438  1.845565  4.592531  5.099802  1.774879  0.356812   \n",
       "agg_1     2.954213  1.896726  4.688557  5.510440  1.666929  0.352725   \n",
       "agg_2     2.938851  2.197247  4.861410  5.617520  1.773381  0.380867   \n",
       "agg_3     3.045972  2.138573  4.863791  5.273604  1.760097  0.463555   \n",
       "agg_4     3.025518  2.019096  4.602948  5.257001  1.755338  0.382190   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "agg_9533  2.333562  0.633322  4.675825  2.793023  0.752030  0.692083   \n",
       "agg_9535  0.835037  0.358773  1.964896  0.307449  0.337240  0.834196   \n",
       "agg_9536  3.008039  1.209324  4.798392  3.931870  1.401328  1.638555   \n",
       "agg_9537  1.241936  0.455059  2.919995  0.571672  0.486448  1.175586   \n",
       "agg_9538  1.715507  0.700955  3.044732  0.858696  0.903406  1.763168   \n",
       "\n",
       "            CFAP74      GSE1       LPP      CLK1  ...    STRIP2  TNFRSF1A  \\\n",
       "agg_0     2.590836  4.629774  4.897171  3.326940  ...  2.836060  0.297015   \n",
       "agg_1     2.292625  4.459535  4.915286  3.192858  ...  3.125704  0.242543   \n",
       "agg_2     2.394917  4.415038  4.836399  3.390717  ...  3.082098  0.263285   \n",
       "agg_3     2.391702  3.940975  4.857763  3.410926  ...  2.882890  0.290327   \n",
       "agg_4     2.432810  4.392480  4.959488  3.250500  ...  3.082296  0.258540   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "agg_9533  0.503531  4.327948  6.903193  3.695593  ...  0.549795  2.270181   \n",
       "agg_9535  0.093885  1.853794  3.700790  4.467302  ...  0.176885  1.370898   \n",
       "agg_9536  0.969720  4.779201  6.631931  4.127797  ...  1.174298  1.870530   \n",
       "agg_9537  0.145397  2.412148  4.759118  4.913945  ...  0.371035  1.361073   \n",
       "agg_9538  0.215304  2.604478  4.549708  4.839124  ...  0.594310  1.801298   \n",
       "\n",
       "          RBM14-RBM4   C1orf21  LINC00639     NPDC1    ZNF425    COL5A1  \\\n",
       "agg_0       1.883849  4.293593   1.463565  3.183534  2.340202  2.374942   \n",
       "agg_1       1.908177  4.439424   1.236739  3.494824  2.425672  2.054568   \n",
       "agg_2       2.006456  4.383455   1.208590  4.013819  2.408381  2.297343   \n",
       "agg_3       1.922963  4.550189   1.430520  3.693118  2.297103  2.121887   \n",
       "agg_4       2.038277  4.464807   1.249043  3.665800  2.400820  2.255862   \n",
       "...              ...       ...        ...       ...       ...       ...   \n",
       "agg_9533    1.563218  4.395422   0.550088  1.330252  1.044471  3.759369   \n",
       "agg_9535    1.022708  3.400267   0.052162  1.908870  0.253417  1.448111   \n",
       "agg_9536    2.506874  5.151776   0.967644  1.809947  2.205356  4.244005   \n",
       "agg_9537    1.668085  4.005738   0.078611  1.571750  0.508187  2.067150   \n",
       "agg_9538    2.075996  3.933860   0.165590  1.970268  0.993521  2.232347   \n",
       "\n",
       "              BRD3     EVI5L  \n",
       "agg_0     2.911916  3.230072  \n",
       "agg_1     2.713408  3.491463  \n",
       "agg_2     2.892222  3.695785  \n",
       "agg_3     2.626117  3.223912  \n",
       "agg_4     2.925619  3.471005  \n",
       "...            ...       ...  \n",
       "agg_9533  2.491346  1.872717  \n",
       "agg_9535  1.622033  1.064292  \n",
       "agg_9536  2.974467  2.659873  \n",
       "agg_9537  2.323764  1.429850  \n",
       "agg_9538  2.473388  1.902884  \n",
       "\n",
       "[8856 rows x 18457 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from decima import DecimaResult\n",
    "\n",
    "result = DecimaResult.load()\n",
    "result.predicted_expression_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802943e2",
   "metadata": {},
   "source": [
    "To access the predicted expression matrix for a specific model, you can use the `model_name` parameter. In this example, we obtain the predicted gene expression for first model replicate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acbb140a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:25:32.184513Z",
     "iopub.status.busy": "2025-11-21T06:25:32.183699Z",
     "iopub.status.idle": "2025-11-21T06:25:32.199071Z",
     "shell.execute_reply": "2025-11-21T06:25:32.198367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STRADA</th>\n",
       "      <th>ETV4</th>\n",
       "      <th>USP25</th>\n",
       "      <th>ZSWIM5</th>\n",
       "      <th>C21orf58</th>\n",
       "      <th>MIR497HG</th>\n",
       "      <th>CFAP74</th>\n",
       "      <th>GSE1</th>\n",
       "      <th>LPP</th>\n",
       "      <th>CLK1</th>\n",
       "      <th>...</th>\n",
       "      <th>STRIP2</th>\n",
       "      <th>TNFRSF1A</th>\n",
       "      <th>RBM14-RBM4</th>\n",
       "      <th>C1orf21</th>\n",
       "      <th>LINC00639</th>\n",
       "      <th>NPDC1</th>\n",
       "      <th>ZNF425</th>\n",
       "      <th>COL5A1</th>\n",
       "      <th>BRD3</th>\n",
       "      <th>EVI5L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>agg_0</th>\n",
       "      <td>2.973438</td>\n",
       "      <td>1.845565</td>\n",
       "      <td>4.592531</td>\n",
       "      <td>5.099802</td>\n",
       "      <td>1.774879</td>\n",
       "      <td>0.356812</td>\n",
       "      <td>2.590836</td>\n",
       "      <td>4.629774</td>\n",
       "      <td>4.897171</td>\n",
       "      <td>3.326940</td>\n",
       "      <td>...</td>\n",
       "      <td>2.836060</td>\n",
       "      <td>0.297015</td>\n",
       "      <td>1.883849</td>\n",
       "      <td>4.293593</td>\n",
       "      <td>1.463565</td>\n",
       "      <td>3.183534</td>\n",
       "      <td>2.340202</td>\n",
       "      <td>2.374942</td>\n",
       "      <td>2.911916</td>\n",
       "      <td>3.230072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_1</th>\n",
       "      <td>2.954213</td>\n",
       "      <td>1.896726</td>\n",
       "      <td>4.688557</td>\n",
       "      <td>5.510440</td>\n",
       "      <td>1.666929</td>\n",
       "      <td>0.352725</td>\n",
       "      <td>2.292625</td>\n",
       "      <td>4.459535</td>\n",
       "      <td>4.915286</td>\n",
       "      <td>3.192858</td>\n",
       "      <td>...</td>\n",
       "      <td>3.125704</td>\n",
       "      <td>0.242543</td>\n",
       "      <td>1.908177</td>\n",
       "      <td>4.439424</td>\n",
       "      <td>1.236739</td>\n",
       "      <td>3.494824</td>\n",
       "      <td>2.425672</td>\n",
       "      <td>2.054568</td>\n",
       "      <td>2.713408</td>\n",
       "      <td>3.491463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_2</th>\n",
       "      <td>2.938851</td>\n",
       "      <td>2.197247</td>\n",
       "      <td>4.861410</td>\n",
       "      <td>5.617520</td>\n",
       "      <td>1.773381</td>\n",
       "      <td>0.380867</td>\n",
       "      <td>2.394917</td>\n",
       "      <td>4.415038</td>\n",
       "      <td>4.836399</td>\n",
       "      <td>3.390717</td>\n",
       "      <td>...</td>\n",
       "      <td>3.082098</td>\n",
       "      <td>0.263285</td>\n",
       "      <td>2.006456</td>\n",
       "      <td>4.383455</td>\n",
       "      <td>1.208590</td>\n",
       "      <td>4.013819</td>\n",
       "      <td>2.408381</td>\n",
       "      <td>2.297343</td>\n",
       "      <td>2.892222</td>\n",
       "      <td>3.695785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_3</th>\n",
       "      <td>3.045972</td>\n",
       "      <td>2.138573</td>\n",
       "      <td>4.863791</td>\n",
       "      <td>5.273604</td>\n",
       "      <td>1.760097</td>\n",
       "      <td>0.463555</td>\n",
       "      <td>2.391702</td>\n",
       "      <td>3.940975</td>\n",
       "      <td>4.857763</td>\n",
       "      <td>3.410926</td>\n",
       "      <td>...</td>\n",
       "      <td>2.882890</td>\n",
       "      <td>0.290327</td>\n",
       "      <td>1.922963</td>\n",
       "      <td>4.550189</td>\n",
       "      <td>1.430520</td>\n",
       "      <td>3.693118</td>\n",
       "      <td>2.297103</td>\n",
       "      <td>2.121887</td>\n",
       "      <td>2.626117</td>\n",
       "      <td>3.223912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_4</th>\n",
       "      <td>3.025518</td>\n",
       "      <td>2.019096</td>\n",
       "      <td>4.602948</td>\n",
       "      <td>5.257001</td>\n",
       "      <td>1.755338</td>\n",
       "      <td>0.382190</td>\n",
       "      <td>2.432810</td>\n",
       "      <td>4.392480</td>\n",
       "      <td>4.959488</td>\n",
       "      <td>3.250500</td>\n",
       "      <td>...</td>\n",
       "      <td>3.082296</td>\n",
       "      <td>0.258540</td>\n",
       "      <td>2.038277</td>\n",
       "      <td>4.464807</td>\n",
       "      <td>1.249043</td>\n",
       "      <td>3.665800</td>\n",
       "      <td>2.400820</td>\n",
       "      <td>2.255862</td>\n",
       "      <td>2.925619</td>\n",
       "      <td>3.471005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9533</th>\n",
       "      <td>2.333562</td>\n",
       "      <td>0.633322</td>\n",
       "      <td>4.675825</td>\n",
       "      <td>2.793023</td>\n",
       "      <td>0.752030</td>\n",
       "      <td>0.692083</td>\n",
       "      <td>0.503531</td>\n",
       "      <td>4.327948</td>\n",
       "      <td>6.903193</td>\n",
       "      <td>3.695593</td>\n",
       "      <td>...</td>\n",
       "      <td>0.549795</td>\n",
       "      <td>2.270181</td>\n",
       "      <td>1.563218</td>\n",
       "      <td>4.395422</td>\n",
       "      <td>0.550088</td>\n",
       "      <td>1.330252</td>\n",
       "      <td>1.044471</td>\n",
       "      <td>3.759369</td>\n",
       "      <td>2.491346</td>\n",
       "      <td>1.872717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9535</th>\n",
       "      <td>0.835037</td>\n",
       "      <td>0.358773</td>\n",
       "      <td>1.964896</td>\n",
       "      <td>0.307449</td>\n",
       "      <td>0.337240</td>\n",
       "      <td>0.834196</td>\n",
       "      <td>0.093885</td>\n",
       "      <td>1.853794</td>\n",
       "      <td>3.700790</td>\n",
       "      <td>4.467302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176885</td>\n",
       "      <td>1.370898</td>\n",
       "      <td>1.022708</td>\n",
       "      <td>3.400267</td>\n",
       "      <td>0.052162</td>\n",
       "      <td>1.908870</td>\n",
       "      <td>0.253417</td>\n",
       "      <td>1.448111</td>\n",
       "      <td>1.622033</td>\n",
       "      <td>1.064292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9536</th>\n",
       "      <td>3.008039</td>\n",
       "      <td>1.209324</td>\n",
       "      <td>4.798392</td>\n",
       "      <td>3.931870</td>\n",
       "      <td>1.401328</td>\n",
       "      <td>1.638555</td>\n",
       "      <td>0.969720</td>\n",
       "      <td>4.779201</td>\n",
       "      <td>6.631931</td>\n",
       "      <td>4.127797</td>\n",
       "      <td>...</td>\n",
       "      <td>1.174298</td>\n",
       "      <td>1.870530</td>\n",
       "      <td>2.506874</td>\n",
       "      <td>5.151776</td>\n",
       "      <td>0.967644</td>\n",
       "      <td>1.809947</td>\n",
       "      <td>2.205356</td>\n",
       "      <td>4.244005</td>\n",
       "      <td>2.974467</td>\n",
       "      <td>2.659873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9537</th>\n",
       "      <td>1.241936</td>\n",
       "      <td>0.455059</td>\n",
       "      <td>2.919995</td>\n",
       "      <td>0.571672</td>\n",
       "      <td>0.486448</td>\n",
       "      <td>1.175586</td>\n",
       "      <td>0.145397</td>\n",
       "      <td>2.412148</td>\n",
       "      <td>4.759118</td>\n",
       "      <td>4.913945</td>\n",
       "      <td>...</td>\n",
       "      <td>0.371035</td>\n",
       "      <td>1.361073</td>\n",
       "      <td>1.668085</td>\n",
       "      <td>4.005738</td>\n",
       "      <td>0.078611</td>\n",
       "      <td>1.571750</td>\n",
       "      <td>0.508187</td>\n",
       "      <td>2.067150</td>\n",
       "      <td>2.323764</td>\n",
       "      <td>1.429850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9538</th>\n",
       "      <td>1.715507</td>\n",
       "      <td>0.700955</td>\n",
       "      <td>3.044732</td>\n",
       "      <td>0.858696</td>\n",
       "      <td>0.903406</td>\n",
       "      <td>1.763168</td>\n",
       "      <td>0.215304</td>\n",
       "      <td>2.604478</td>\n",
       "      <td>4.549708</td>\n",
       "      <td>4.839124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.594310</td>\n",
       "      <td>1.801298</td>\n",
       "      <td>2.075996</td>\n",
       "      <td>3.933860</td>\n",
       "      <td>0.165590</td>\n",
       "      <td>1.970268</td>\n",
       "      <td>0.993521</td>\n",
       "      <td>2.232347</td>\n",
       "      <td>2.473388</td>\n",
       "      <td>1.902884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8856 rows × 18457 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            STRADA      ETV4     USP25    ZSWIM5  C21orf58  MIR497HG  \\\n",
       "agg_0     2.973438  1.845565  4.592531  5.099802  1.774879  0.356812   \n",
       "agg_1     2.954213  1.896726  4.688557  5.510440  1.666929  0.352725   \n",
       "agg_2     2.938851  2.197247  4.861410  5.617520  1.773381  0.380867   \n",
       "agg_3     3.045972  2.138573  4.863791  5.273604  1.760097  0.463555   \n",
       "agg_4     3.025518  2.019096  4.602948  5.257001  1.755338  0.382190   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "agg_9533  2.333562  0.633322  4.675825  2.793023  0.752030  0.692083   \n",
       "agg_9535  0.835037  0.358773  1.964896  0.307449  0.337240  0.834196   \n",
       "agg_9536  3.008039  1.209324  4.798392  3.931870  1.401328  1.638555   \n",
       "agg_9537  1.241936  0.455059  2.919995  0.571672  0.486448  1.175586   \n",
       "agg_9538  1.715507  0.700955  3.044732  0.858696  0.903406  1.763168   \n",
       "\n",
       "            CFAP74      GSE1       LPP      CLK1  ...    STRIP2  TNFRSF1A  \\\n",
       "agg_0     2.590836  4.629774  4.897171  3.326940  ...  2.836060  0.297015   \n",
       "agg_1     2.292625  4.459535  4.915286  3.192858  ...  3.125704  0.242543   \n",
       "agg_2     2.394917  4.415038  4.836399  3.390717  ...  3.082098  0.263285   \n",
       "agg_3     2.391702  3.940975  4.857763  3.410926  ...  2.882890  0.290327   \n",
       "agg_4     2.432810  4.392480  4.959488  3.250500  ...  3.082296  0.258540   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "agg_9533  0.503531  4.327948  6.903193  3.695593  ...  0.549795  2.270181   \n",
       "agg_9535  0.093885  1.853794  3.700790  4.467302  ...  0.176885  1.370898   \n",
       "agg_9536  0.969720  4.779201  6.631931  4.127797  ...  1.174298  1.870530   \n",
       "agg_9537  0.145397  2.412148  4.759118  4.913945  ...  0.371035  1.361073   \n",
       "agg_9538  0.215304  2.604478  4.549708  4.839124  ...  0.594310  1.801298   \n",
       "\n",
       "          RBM14-RBM4   C1orf21  LINC00639     NPDC1    ZNF425    COL5A1  \\\n",
       "agg_0       1.883849  4.293593   1.463565  3.183534  2.340202  2.374942   \n",
       "agg_1       1.908177  4.439424   1.236739  3.494824  2.425672  2.054568   \n",
       "agg_2       2.006456  4.383455   1.208590  4.013819  2.408381  2.297343   \n",
       "agg_3       1.922963  4.550189   1.430520  3.693118  2.297103  2.121887   \n",
       "agg_4       2.038277  4.464807   1.249043  3.665800  2.400820  2.255862   \n",
       "...              ...       ...        ...       ...       ...       ...   \n",
       "agg_9533    1.563218  4.395422   0.550088  1.330252  1.044471  3.759369   \n",
       "agg_9535    1.022708  3.400267   0.052162  1.908870  0.253417  1.448111   \n",
       "agg_9536    2.506874  5.151776   0.967644  1.809947  2.205356  4.244005   \n",
       "agg_9537    1.668085  4.005738   0.078611  1.571750  0.508187  2.067150   \n",
       "agg_9538    2.075996  3.933860   0.165590  1.970268  0.993521  2.232347   \n",
       "\n",
       "              BRD3     EVI5L  \n",
       "agg_0     2.911916  3.230072  \n",
       "agg_1     2.713408  3.491463  \n",
       "agg_2     2.892222  3.695785  \n",
       "agg_3     2.626117  3.223912  \n",
       "agg_4     2.925619  3.471005  \n",
       "...            ...       ...  \n",
       "agg_9533  2.491346  1.872717  \n",
       "agg_9535  1.622033  1.064292  \n",
       "agg_9536  2.974467  2.659873  \n",
       "agg_9537  2.323764  1.429850  \n",
       "agg_9538  2.473388  1.902884  \n",
       "\n",
       "[8856 rows x 18457 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.predicted_expression_matrix(model_name=\"v1_rep0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b7cd00",
   "metadata": {},
   "source": [
    "and for the second model replicate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95ebbd2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:25:32.200797Z",
     "iopub.status.busy": "2025-11-21T06:25:32.200269Z",
     "iopub.status.idle": "2025-11-21T06:25:32.214579Z",
     "shell.execute_reply": "2025-11-21T06:25:32.213871Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STRADA</th>\n",
       "      <th>ETV4</th>\n",
       "      <th>USP25</th>\n",
       "      <th>ZSWIM5</th>\n",
       "      <th>C21orf58</th>\n",
       "      <th>MIR497HG</th>\n",
       "      <th>CFAP74</th>\n",
       "      <th>GSE1</th>\n",
       "      <th>LPP</th>\n",
       "      <th>CLK1</th>\n",
       "      <th>...</th>\n",
       "      <th>STRIP2</th>\n",
       "      <th>TNFRSF1A</th>\n",
       "      <th>RBM14-RBM4</th>\n",
       "      <th>C1orf21</th>\n",
       "      <th>LINC00639</th>\n",
       "      <th>NPDC1</th>\n",
       "      <th>ZNF425</th>\n",
       "      <th>COL5A1</th>\n",
       "      <th>BRD3</th>\n",
       "      <th>EVI5L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>agg_0</th>\n",
       "      <td>2.973438</td>\n",
       "      <td>1.845565</td>\n",
       "      <td>4.592531</td>\n",
       "      <td>5.099802</td>\n",
       "      <td>1.774879</td>\n",
       "      <td>0.356812</td>\n",
       "      <td>2.590836</td>\n",
       "      <td>4.629774</td>\n",
       "      <td>4.897171</td>\n",
       "      <td>3.326940</td>\n",
       "      <td>...</td>\n",
       "      <td>2.836060</td>\n",
       "      <td>0.297015</td>\n",
       "      <td>1.883849</td>\n",
       "      <td>4.293593</td>\n",
       "      <td>1.463565</td>\n",
       "      <td>3.183534</td>\n",
       "      <td>2.340202</td>\n",
       "      <td>2.374942</td>\n",
       "      <td>2.911916</td>\n",
       "      <td>3.230072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_1</th>\n",
       "      <td>2.954213</td>\n",
       "      <td>1.896726</td>\n",
       "      <td>4.688557</td>\n",
       "      <td>5.510440</td>\n",
       "      <td>1.666929</td>\n",
       "      <td>0.352725</td>\n",
       "      <td>2.292625</td>\n",
       "      <td>4.459535</td>\n",
       "      <td>4.915286</td>\n",
       "      <td>3.192858</td>\n",
       "      <td>...</td>\n",
       "      <td>3.125704</td>\n",
       "      <td>0.242543</td>\n",
       "      <td>1.908177</td>\n",
       "      <td>4.439424</td>\n",
       "      <td>1.236739</td>\n",
       "      <td>3.494824</td>\n",
       "      <td>2.425672</td>\n",
       "      <td>2.054568</td>\n",
       "      <td>2.713408</td>\n",
       "      <td>3.491463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_2</th>\n",
       "      <td>2.938851</td>\n",
       "      <td>2.197247</td>\n",
       "      <td>4.861410</td>\n",
       "      <td>5.617520</td>\n",
       "      <td>1.773381</td>\n",
       "      <td>0.380867</td>\n",
       "      <td>2.394917</td>\n",
       "      <td>4.415038</td>\n",
       "      <td>4.836399</td>\n",
       "      <td>3.390717</td>\n",
       "      <td>...</td>\n",
       "      <td>3.082098</td>\n",
       "      <td>0.263285</td>\n",
       "      <td>2.006456</td>\n",
       "      <td>4.383455</td>\n",
       "      <td>1.208590</td>\n",
       "      <td>4.013819</td>\n",
       "      <td>2.408381</td>\n",
       "      <td>2.297343</td>\n",
       "      <td>2.892222</td>\n",
       "      <td>3.695785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_3</th>\n",
       "      <td>3.045972</td>\n",
       "      <td>2.138573</td>\n",
       "      <td>4.863791</td>\n",
       "      <td>5.273604</td>\n",
       "      <td>1.760097</td>\n",
       "      <td>0.463555</td>\n",
       "      <td>2.391702</td>\n",
       "      <td>3.940975</td>\n",
       "      <td>4.857763</td>\n",
       "      <td>3.410926</td>\n",
       "      <td>...</td>\n",
       "      <td>2.882890</td>\n",
       "      <td>0.290327</td>\n",
       "      <td>1.922963</td>\n",
       "      <td>4.550189</td>\n",
       "      <td>1.430520</td>\n",
       "      <td>3.693118</td>\n",
       "      <td>2.297103</td>\n",
       "      <td>2.121887</td>\n",
       "      <td>2.626117</td>\n",
       "      <td>3.223912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_4</th>\n",
       "      <td>3.025518</td>\n",
       "      <td>2.019096</td>\n",
       "      <td>4.602948</td>\n",
       "      <td>5.257001</td>\n",
       "      <td>1.755338</td>\n",
       "      <td>0.382190</td>\n",
       "      <td>2.432810</td>\n",
       "      <td>4.392480</td>\n",
       "      <td>4.959488</td>\n",
       "      <td>3.250500</td>\n",
       "      <td>...</td>\n",
       "      <td>3.082296</td>\n",
       "      <td>0.258540</td>\n",
       "      <td>2.038277</td>\n",
       "      <td>4.464807</td>\n",
       "      <td>1.249043</td>\n",
       "      <td>3.665800</td>\n",
       "      <td>2.400820</td>\n",
       "      <td>2.255862</td>\n",
       "      <td>2.925619</td>\n",
       "      <td>3.471005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9533</th>\n",
       "      <td>2.333562</td>\n",
       "      <td>0.633322</td>\n",
       "      <td>4.675825</td>\n",
       "      <td>2.793023</td>\n",
       "      <td>0.752030</td>\n",
       "      <td>0.692083</td>\n",
       "      <td>0.503531</td>\n",
       "      <td>4.327948</td>\n",
       "      <td>6.903193</td>\n",
       "      <td>3.695593</td>\n",
       "      <td>...</td>\n",
       "      <td>0.549795</td>\n",
       "      <td>2.270181</td>\n",
       "      <td>1.563218</td>\n",
       "      <td>4.395422</td>\n",
       "      <td>0.550088</td>\n",
       "      <td>1.330252</td>\n",
       "      <td>1.044471</td>\n",
       "      <td>3.759369</td>\n",
       "      <td>2.491346</td>\n",
       "      <td>1.872717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9535</th>\n",
       "      <td>0.835037</td>\n",
       "      <td>0.358773</td>\n",
       "      <td>1.964896</td>\n",
       "      <td>0.307449</td>\n",
       "      <td>0.337240</td>\n",
       "      <td>0.834196</td>\n",
       "      <td>0.093885</td>\n",
       "      <td>1.853794</td>\n",
       "      <td>3.700790</td>\n",
       "      <td>4.467302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176885</td>\n",
       "      <td>1.370898</td>\n",
       "      <td>1.022708</td>\n",
       "      <td>3.400267</td>\n",
       "      <td>0.052162</td>\n",
       "      <td>1.908870</td>\n",
       "      <td>0.253417</td>\n",
       "      <td>1.448111</td>\n",
       "      <td>1.622033</td>\n",
       "      <td>1.064292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9536</th>\n",
       "      <td>3.008039</td>\n",
       "      <td>1.209324</td>\n",
       "      <td>4.798392</td>\n",
       "      <td>3.931870</td>\n",
       "      <td>1.401328</td>\n",
       "      <td>1.638555</td>\n",
       "      <td>0.969720</td>\n",
       "      <td>4.779201</td>\n",
       "      <td>6.631931</td>\n",
       "      <td>4.127797</td>\n",
       "      <td>...</td>\n",
       "      <td>1.174298</td>\n",
       "      <td>1.870530</td>\n",
       "      <td>2.506874</td>\n",
       "      <td>5.151776</td>\n",
       "      <td>0.967644</td>\n",
       "      <td>1.809947</td>\n",
       "      <td>2.205356</td>\n",
       "      <td>4.244005</td>\n",
       "      <td>2.974467</td>\n",
       "      <td>2.659873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9537</th>\n",
       "      <td>1.241936</td>\n",
       "      <td>0.455059</td>\n",
       "      <td>2.919995</td>\n",
       "      <td>0.571672</td>\n",
       "      <td>0.486448</td>\n",
       "      <td>1.175586</td>\n",
       "      <td>0.145397</td>\n",
       "      <td>2.412148</td>\n",
       "      <td>4.759118</td>\n",
       "      <td>4.913945</td>\n",
       "      <td>...</td>\n",
       "      <td>0.371035</td>\n",
       "      <td>1.361073</td>\n",
       "      <td>1.668085</td>\n",
       "      <td>4.005738</td>\n",
       "      <td>0.078611</td>\n",
       "      <td>1.571750</td>\n",
       "      <td>0.508187</td>\n",
       "      <td>2.067150</td>\n",
       "      <td>2.323764</td>\n",
       "      <td>1.429850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg_9538</th>\n",
       "      <td>1.715507</td>\n",
       "      <td>0.700955</td>\n",
       "      <td>3.044732</td>\n",
       "      <td>0.858696</td>\n",
       "      <td>0.903406</td>\n",
       "      <td>1.763168</td>\n",
       "      <td>0.215304</td>\n",
       "      <td>2.604478</td>\n",
       "      <td>4.549708</td>\n",
       "      <td>4.839124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.594310</td>\n",
       "      <td>1.801298</td>\n",
       "      <td>2.075996</td>\n",
       "      <td>3.933860</td>\n",
       "      <td>0.165590</td>\n",
       "      <td>1.970268</td>\n",
       "      <td>0.993521</td>\n",
       "      <td>2.232347</td>\n",
       "      <td>2.473388</td>\n",
       "      <td>1.902884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8856 rows × 18457 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            STRADA      ETV4     USP25    ZSWIM5  C21orf58  MIR497HG  \\\n",
       "agg_0     2.973438  1.845565  4.592531  5.099802  1.774879  0.356812   \n",
       "agg_1     2.954213  1.896726  4.688557  5.510440  1.666929  0.352725   \n",
       "agg_2     2.938851  2.197247  4.861410  5.617520  1.773381  0.380867   \n",
       "agg_3     3.045972  2.138573  4.863791  5.273604  1.760097  0.463555   \n",
       "agg_4     3.025518  2.019096  4.602948  5.257001  1.755338  0.382190   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "agg_9533  2.333562  0.633322  4.675825  2.793023  0.752030  0.692083   \n",
       "agg_9535  0.835037  0.358773  1.964896  0.307449  0.337240  0.834196   \n",
       "agg_9536  3.008039  1.209324  4.798392  3.931870  1.401328  1.638555   \n",
       "agg_9537  1.241936  0.455059  2.919995  0.571672  0.486448  1.175586   \n",
       "agg_9538  1.715507  0.700955  3.044732  0.858696  0.903406  1.763168   \n",
       "\n",
       "            CFAP74      GSE1       LPP      CLK1  ...    STRIP2  TNFRSF1A  \\\n",
       "agg_0     2.590836  4.629774  4.897171  3.326940  ...  2.836060  0.297015   \n",
       "agg_1     2.292625  4.459535  4.915286  3.192858  ...  3.125704  0.242543   \n",
       "agg_2     2.394917  4.415038  4.836399  3.390717  ...  3.082098  0.263285   \n",
       "agg_3     2.391702  3.940975  4.857763  3.410926  ...  2.882890  0.290327   \n",
       "agg_4     2.432810  4.392480  4.959488  3.250500  ...  3.082296  0.258540   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "agg_9533  0.503531  4.327948  6.903193  3.695593  ...  0.549795  2.270181   \n",
       "agg_9535  0.093885  1.853794  3.700790  4.467302  ...  0.176885  1.370898   \n",
       "agg_9536  0.969720  4.779201  6.631931  4.127797  ...  1.174298  1.870530   \n",
       "agg_9537  0.145397  2.412148  4.759118  4.913945  ...  0.371035  1.361073   \n",
       "agg_9538  0.215304  2.604478  4.549708  4.839124  ...  0.594310  1.801298   \n",
       "\n",
       "          RBM14-RBM4   C1orf21  LINC00639     NPDC1    ZNF425    COL5A1  \\\n",
       "agg_0       1.883849  4.293593   1.463565  3.183534  2.340202  2.374942   \n",
       "agg_1       1.908177  4.439424   1.236739  3.494824  2.425672  2.054568   \n",
       "agg_2       2.006456  4.383455   1.208590  4.013819  2.408381  2.297343   \n",
       "agg_3       1.922963  4.550189   1.430520  3.693118  2.297103  2.121887   \n",
       "agg_4       2.038277  4.464807   1.249043  3.665800  2.400820  2.255862   \n",
       "...              ...       ...        ...       ...       ...       ...   \n",
       "agg_9533    1.563218  4.395422   0.550088  1.330252  1.044471  3.759369   \n",
       "agg_9535    1.022708  3.400267   0.052162  1.908870  0.253417  1.448111   \n",
       "agg_9536    2.506874  5.151776   0.967644  1.809947  2.205356  4.244005   \n",
       "agg_9537    1.668085  4.005738   0.078611  1.571750  0.508187  2.067150   \n",
       "agg_9538    2.075996  3.933860   0.165590  1.970268  0.993521  2.232347   \n",
       "\n",
       "              BRD3     EVI5L  \n",
       "agg_0     2.911916  3.230072  \n",
       "agg_1     2.713408  3.491463  \n",
       "agg_2     2.892222  3.695785  \n",
       "agg_3     2.626117  3.223912  \n",
       "agg_4     2.925619  3.471005  \n",
       "...            ...       ...  \n",
       "agg_9533  2.491346  1.872717  \n",
       "agg_9535  1.622033  1.064292  \n",
       "agg_9536  2.974467  2.659873  \n",
       "agg_9537  2.323764  1.429850  \n",
       "agg_9538  2.473388  1.902884  \n",
       "\n",
       "[8856 rows x 18457 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.predicted_expression_matrix(model_name=\"v1_rep1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3435f5fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:25:32.216226Z",
     "iopub.status.busy": "2025-11-21T06:25:32.215688Z",
     "iopub.status.idle": "2025-11-21T06:25:32.219914Z",
     "shell.execute_reply": "2025-11-21T06:25:32.219213Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Layers with keys: preds, v1_rep0, v1_rep1, v1_rep2, v1_rep3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.anndata.layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "345ff486",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "### CLI API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e9f055",
   "metadata": {},
   "source": [
    "If you want to perform gene expression prediction again, rather than using the precomputed scores, you can use the Decima command-line interface (CLI) to generate new predictions for any set of genes you specify. For example, you can run the `decima predict-genes` command with the `--genes` argument to provide a comma-separated list of gene names (such as \"STRADA,ETV4,USP25\") if no gene provided it will perform expression predictions for all genes, select the prediction model with the `--model` option (for instance, \"ensemble\" or a specific replicate like \"0\"), and use `--save-replicates` to save predictions for each replicate. The `-o` flag lets you specify the output file path for the predictions in `.h5ad` format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6715257c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:25:32.221652Z",
     "iopub.status.busy": "2025-11-21T06:25:32.221116Z",
     "iopub.status.idle": "2025-11-21T06:26:16.929651Z",
     "shell.execute_reply": "2025-11-21T06:26:16.928993Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
      "  warnings.warn(\r\n",
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
      "  warnings.warn(\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decima - INFO - Using device: 0 and genome: hg38 for prediction.\r\n",
      "decima - INFO - Loading model ensemble...\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
      "Done. 00:00:02.6 (272.9MB/s)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep1:latest', 720.03MB. 1 files...\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
      "Done. 00:00:01.8 (405.6MB/s)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep2:latest', 720.03MB. 1 files...\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
      "Done. 00:00:02.0 (359.5MB/s)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep3:latest', 720.03MB. 1 files...\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
      "Done. 00:00:01.8 (400.1MB/s)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decima - INFO - Making predictions\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done. 00:00:01.9 (1645.5MB/s)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)\r\n",
      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\r\n",
      "TPU available: False, using: 0 TPU cores\r\n",
      "HPU available: False, using: 0 HPUs\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLURM auto-requeueing enabled. Setting signal handlers.\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Predicting: |                                             | 0/? [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Predicting: |                                             | 0/? [00:00<?, ?it/s]\r",
      "Predicting DataLoader 0:   0%|                            | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Predicting DataLoader 0:  33%|██████▋             | 1/3 [00:00<00:01,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Predicting DataLoader 0:  67%|█████████████▎      | 2/3 [00:01<00:00,  1.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Predicting DataLoader 0: 100%|████████████████████| 3/3 [00:01<00:00,  1.82it/s]\r",
      "Predicting DataLoader 0: 100%|████████████████████| 3/3 [00:01<00:00,  1.81it/s]\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\r\n",
      "decima - INFO - Creating anndata\r\n",
      "decima - INFO - Evaluating performance\r\n",
      "decima - WARNING - No ground truth expression matrix found in the metadata. Skipping evaluation.\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! decima predict-genes --genes \"STRADA,ETV4,USP25\" --model ensemble --save-replicates -o example/predict_genes/predictions.h5ad "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2c0356",
   "metadata": {},
   "source": [
    "After running this command, you can load the resulting predictions in Python using the `DecimaResult` class and access the predicted expression matrix as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e598b85c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:26:16.931282Z",
     "iopub.status.busy": "2025-11-21T06:26:16.931107Z",
     "iopub.status.idle": "2025-11-21T06:26:16.979662Z",
     "shell.execute_reply": "2025-11-21T06:26:16.979173Z"
    }
   },
   "outputs": [
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       "      <td>0.952112</td>\n",
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       "      <td>2.779494</td>\n",
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       "            STRADA      ETV4     USP25\n",
       "agg_0     3.060561  2.882100  3.469085\n",
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       "agg_2     3.156642  3.122381  3.718749\n",
       "agg_3     3.214047  3.204670  3.629874\n",
       "agg_4     3.103570  3.032028  3.512117\n",
       "...            ...       ...       ...\n",
       "agg_9533  2.313220  2.173597  3.156157\n",
       "agg_9535  0.952112  0.956720  1.250005\n",
       "agg_9536  2.779494  2.705945  3.530610\n",
       "agg_9537  1.342694  1.407407  1.867070\n",
       "agg_9538  1.745080  1.633314  2.082985\n",
       "\n",
       "[8856 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = DecimaResult.load(\"example/predict_genes/predictions.h5ad\")\n",
    "result.predicted_expression_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da2ab16",
   "metadata": {},
   "source": [
    "or for a specific replicate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce3884f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:26:16.981009Z",
     "iopub.status.busy": "2025-11-21T06:26:16.980750Z",
     "iopub.status.idle": "2025-11-21T06:26:16.986183Z",
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    {
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       "            STRADA      ETV4     USP25\n",
       "agg_0     2.933017  2.892934  2.858879\n",
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       "agg_2     2.743120  2.971366  2.950323\n",
       "agg_3     2.804692  3.346689  2.837382\n",
       "agg_4     2.816030  3.088620  2.973081\n",
       "...            ...       ...       ...\n",
       "agg_9533  2.510605  2.760446  1.965776\n",
       "agg_9535  1.246022  1.192524  0.407104\n",
       "agg_9536  2.809229  3.369957  2.927355\n",
       "agg_9537  1.580334  1.533467  0.879179\n",
       "agg_9538  2.014224  2.078125  1.374325\n",
       "\n",
       "[8856 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.predicted_expression_matrix(model_name=\"preds_v1_rep0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316629fe",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "### Python API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552b7245",
   "metadata": {},
   "source": [
    "The same functionality is available through the Python API, allowing you to perform gene expression prediction programmatically. You can specify the genes, model, and other options directly in your Python code using the provided classes and functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a0dba6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-21T06:26:16.987569Z",
     "iopub.status.busy": "2025-11-21T06:26:16.987310Z",
     "iopub.status.idle": "2025-11-21T06:26:50.087684Z",
     "shell.execute_reply": "2025-11-21T06:26:50.087076Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:01.3 (539.8MB/s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep1:latest', 720.03MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:01.4 (529.6MB/s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep2:latest', 720.03MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:00.6 (1166.2MB/s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep3:latest', 720.03MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:00.6 (1242.4MB/s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:05.1 (613.2MB/s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)\n",
      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TPU available: False, using: 0 TPU cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLURM auto-requeueing enabled. Setting signal handlers.\n"
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       "Predicting: |          | 0/? [00:00<?, ?it/s]"
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     },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n",
      "No ground truth expression matrix found in the metadata. Skipping evaluation.\n"
     ]
    }
   ],
   "source": [
    "from decima.tools.inference import predict_gene_expression\n",
    "\n",
    "ad = predict_gene_expression(\n",
    "    genes=[\"STRADA\", \"ETV4\", \"USP25\"],\n",
    "    model=\"ensemble\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346e4199",
   "metadata": {},
   "source": [
    "### Developer API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3eac4fb",
   "metadata": {},
   "source": [
    "Under the hood, the Decima prediction API uses the `GeneDataset` and `SeqDataset` pytorch Dataset classes to prepare the data for prediction. These classes provide a flexible way to handle different types of input data, including custom genes and DNA sequences. Internally, these datasets represent sequences using one-hot encoding and apply a gene mask to indicate which positions correspond to gene regions.\n",
    "\n",
    "For example, you can create a `GeneDataset` object to predict expression for the genes in your metadata. The `predict_on_dataset` method returns a dictionary containing the predicted expression values and if there is any warnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d147aaff",
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     "shell.execute_reply": "2025-11-21T06:27:05.812096Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:01.2 (580.7MB/s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done. 00:00:02.7 (1164.2MB/s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TPU available: False, using: 0 TPU cores\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLURM auto-requeueing enabled. Setting signal handlers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:433: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n"
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       "Predicting: |          | 0/? [00:00<?, ?it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'expression': array([[2.9330175, 2.8167777, 2.7431197, ..., 2.8092291, 1.580334 ,\n",
      "        2.0142236],\n",
      "       [2.0205972, 1.9390993, 2.3948462, ..., 1.5032245, 0.51865  ,\n",
      "        0.8084921],\n",
      "       [4.795624 , 4.9177237, 4.8060527, ..., 4.6602893, 2.7781048,\n",
      "        3.1485069]], dtype=float32),\n",
      " 'warnings': Counter({'unknown': tensor(0),\n",
      "                      'allele_mismatch_with_reference_genome': tensor(0)})}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "from decima.data.dataset import GeneDataset\n",
    "from decima.hub import load_decima_model\n",
    "\n",
    "model = load_decima_model(\"rep0\", device=0)\n",
    "ds = GeneDataset(genes=[\"STRADA\", \"ETV4\", \"USP25\"])\n",
    "\n",
    "preds = model.predict_on_dataset(ds, device=0)\n",
    "pprint(preds)"
   ]
  },
  {
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   },
   "source": [
    "## Custom Expression for custom genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd07af0",
   "metadata": {},
   "source": [
    "If you have custom genes, you can create a `SeqDataset` object to predict expression for those genes. \n",
    "\n",
    "To do this, prepare a FASTA file where:\n",
    "  - Each sequence is exactly the Decima context size (524,288 bases).\n",
    "  - The FASTA header for each sequence must include the gene name and the gene mask coordinates, using the format:\n",
    "        `>gene_name|gene_mask_start=X|gene_mask_end=Y`\n",
    "    where `X` and `Y` specify the start and end positions (0-based, inclusive) of the gene region within the sequence. The gene mask indicates which region of the sequence corresponds to the gene for which expression will be predicted.\n",
    "\n",
    "For example, seqs.fasta contains these information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    {
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     "output_type": "stream",
     "text": [
      "cat: ../tests/data/seqs.fasta: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "! cat ../tests/data/seqs.fasta | cut -c 1-200"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
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     "output_type": "stream",
     "text": [
      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "TPU available: False, using: 0 TPU cores\n"
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      "SLURM auto-requeueing enabled. Setting signal handlers.\n"
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     "text": [
      "array([[0.22026505, 0.21946757, 0.23086782, ..., 1.0358492 , 1.2124686 ,\n",
      "        1.441437  ],\n",
      "       [0.26515013, 0.13341773, 0.14887223, ..., 0.44430685, 0.26546437,\n",
      "        0.3042384 ]], dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "from decima.data.dataset import SeqDataset\n",
    "\n",
    "\n",
    "ds = SeqDataset.from_fasta(\"../../tests/data/seqs.fasta\")\n",
    "\n",
    "preds = model.predict_on_dataset(ds, device=0)\n",
    "pprint(preds[\"expression\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "171fd3d1",
   "metadata": {},
   "source": [
    "See the documentation of SeqDataset for more details. SeqDataset can be created from a pandas DataFrame with the following columns: `seq`, `gene_mask_start`, `gene_mask_end`, and `gene_name` with `SeqDataset.from_dataframe` or from a one-hot encoded tensor with `SeqDataset.from_one_hot`."
   ]
  }
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