tutorials/TrainData_AML_biofounction_context.ipynb

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  {
   "cell_type": "markdown",
   "id": "e48c5f86-f0aa-4963-8913-604443fa0a3c",
   "metadata": {},
   "source": [
    "# Tutorial: Train single cell data using genomic context method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aac073f-ef61-49f9-bf3e-13b2a3137121",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This tutorial demonstrates how to use **scGenAI** to train a model for predicting cell types from single-cell RNA sequencing (scRNA-seq) data. In this example, we apply the **gene list** method to generate input sequences.\n",
    "\n",
    "**Note:**\n",
    "1. The dataset used for this tutorial is reported in [**van Galen et al., 2019**](https://pubmed.ncbi.nlm.nih.gov/30827681/). The input data train and validation files can be downloaded from link of [**AML_biofounction_context**](https://zenodo.org/records/14036452).\n",
    "2. In this tutorial, we will load the model template directly from the cache folder.\n",
    "3. While the model in this example is trained to predict cell types, it can be used to predict any feature in practical applications.\n",
    "4. In this tutorial, the genomic context is built using the cytoband information of the human genes, in the practical case, this can be other lists that may facilitate the prediction.\n",
    "\n",
    "**Reference**\n",
    "1. Zeng, A. G. X., Bansal, S., Jin, L., Mitchell, A., Chen, W. C., Abbas, H. A., Chan-Seng-Yue, M., Voisin, V., van Galen, P., Tierens, A., Cheok, M., Preudhomme, C., Dombret, H., Daver, N., Futreal, P. A., Minden, M. D., Kennedy, J. A., Wang, J. C. Y., & Dick, J. E. (2022). A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nature medicine, 28(6), 1212–1223. https://doi.org/10.1038/s41591-022-01819-x\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8294154a-4036-4788-b358-0e91342ac224",
   "metadata": {},
   "source": [
    "## Train the Model Using Genelist (Multiple GPU)\n",
    "\n",
    "The training process can be easily initiated by calling the `run_training_from_config` function in **scGenAI**, using the input files specified in a configuration YAML file. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2989a022-6bea-4f9c-861d-452b0d85e11f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scGenAI.training.train import run_training_from_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a140f4c-6d3c-4a18-9f15-87d148179ac8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "config_file = '/home/ubuntu/scgenai/tutorials/yaml/config_biofounction_context_AML.yaml'\n",
    "run_training_from_config(config_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe550d5-16c0-425a-bfd7-d257c1ecfe1d",
   "metadata": {},
   "source": [
    "## Post-Training Model and Results\n",
    "\n",
    "Upon completion of the training, the trained model will be saved in the `model_dir` as defined in the configuration file. Within this directory, two subfolders will be created: `best_model` and `last_model`. The `best_model` folder contains the model that achieved the highest training performance, while the `last_model` contains the model from the final epoch. Additionally, a `combined_epoch_results.csv` file will be generated in the `model_dir`, summarizing the training performance metrics across all epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc88e128-eaba-4557-8fca-eae0966e482d",
   "metadata": {},
   "source": [
    "### Training Summary\n",
    "\n",
    "In addition to the output summary files generated after training, the training summary can be directly accessed in Python by loading the saved configuration file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd7b43ca-2f9a-4845-a284-1d7aea601dd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scGenAI.config import Config\n",
    "from scGenAI.data.postprocess import TrainSummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9bc20420-8143-4ff7-b474-d4a3f6936991",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Epoch</th>\n",
       "      <th>Validation Accuracy</th>\n",
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       "      <th>Train Accuracy</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.713902</td>\n",
       "      <td>1.600441</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.293804</td>\n",
       "      <td>0.901693</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.801209</td>\n",
       "      <td>0.190301</td>\n",
       "      <td>0.936557</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.815312</td>\n",
       "      <td>0.129957</td>\n",
       "      <td>0.956938</td>\n",
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       "      <th>6</th>\n",
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       "      <td>0.815312</td>\n",
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       "      <td>0.980231</td>\n",
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       "      <td>0.811283</td>\n",
       "      <td>0.038027</td>\n",
       "      <td>0.987204</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.799194</td>\n",
       "      <td>0.036632</td>\n",
       "      <td>0.987970</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.807253</td>\n",
       "      <td>0.031143</td>\n",
       "      <td>0.989273</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.813298</td>\n",
       "      <td>0.029472</td>\n",
       "      <td>0.991572</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.815312</td>\n",
       "      <td>0.025418</td>\n",
       "      <td>0.992797</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.791807</td>\n",
       "      <td>0.026473</td>\n",
       "      <td>0.991878</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.816655</td>\n",
       "      <td>0.016191</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
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       "      <td>0.802552</td>\n",
       "      <td>0.017635</td>\n",
       "      <td>0.993717</td>\n",
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       "      <td>0.021403</td>\n",
       "      <td>0.993870</td>\n",
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       "      <th>19</th>\n",
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       "      <td>0.819342</td>\n",
       "      <td>0.019041</td>\n",
       "      <td>0.994866</td>\n",
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       "      <th>21</th>\n",
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       "      <td>0.807253</td>\n",
       "      <td>0.016515</td>\n",
       "      <td>0.994866</td>\n",
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       "      <th>22</th>\n",
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       "      <td>0.794493</td>\n",
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       "      <td>0.994866</td>\n",
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       "      <th>23</th>\n",
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       "      <td>0.815312</td>\n",
       "      <td>0.012284</td>\n",
       "      <td>0.996475</td>\n",
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       "      <th>24</th>\n",
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       "      <td>0.791135</td>\n",
       "      <td>0.013286</td>\n",
       "      <td>0.995633</td>\n",
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       "      <th>25</th>\n",
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       "      <td>0.811283</td>\n",
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       "      <td>0.803895</td>\n",
       "      <td>0.012234</td>\n",
       "      <td>0.996322</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0.807253</td>\n",
       "      <td>0.015970</td>\n",
       "      <td>0.996169</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0.798522</td>\n",
       "      <td>0.012769</td>\n",
       "      <td>0.996169</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0.785762</td>\n",
       "      <td>0.010979</td>\n",
       "      <td>0.996475</td>\n",
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      "text/plain": [
       "    Epoch  Validation Accuracy  Train Loss  Train Accuracy\n",
       "0       1             0.713902    1.600441        0.458586\n",
       "1       2             0.770987    0.714110        0.747452\n",
       "2       3             0.796508    0.451370        0.842541\n",
       "3       4             0.799194    0.293804        0.901693\n",
       "4       5             0.801209    0.190301        0.936557\n",
       "5       6             0.815312    0.129957        0.956938\n",
       "6       7             0.815312    0.089315        0.970347\n",
       "7       8             0.799866    0.063191        0.979925\n",
       "8       9             0.806582    0.057216        0.980231\n",
       "9      10             0.807253    0.044638        0.986361\n",
       "10     11             0.811283    0.038027        0.987204\n",
       "11     12             0.799194    0.036632        0.987970\n",
       "12     13             0.807253    0.031143        0.989273\n",
       "13     14             0.813298    0.029472        0.991572\n",
       "14     15             0.815312    0.025418        0.992797\n",
       "15     16             0.791807    0.026473        0.991878\n",
       "16     17             0.816655    0.016191        0.994866\n",
       "17     18             0.802552    0.017635        0.993717\n",
       "18     19             0.785762    0.021403        0.993870\n",
       "19     20             0.819342    0.019041        0.994866\n",
       "20     21             0.805238    0.015936        0.994636\n",
       "21     22             0.807253    0.016515        0.994866\n",
       "22     23             0.794493    0.016721        0.994866\n",
       "23     24             0.815312    0.012284        0.996475\n",
       "24     25             0.791135    0.013286        0.995633\n",
       "25     26             0.811283    0.013380        0.995709\n",
       "26     27             0.803895    0.012234        0.996322\n",
       "27     28             0.807253    0.015970        0.996169\n",
       "28     29             0.798522    0.012769        0.996169\n",
       "29     30             0.785762    0.010979        0.996475"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = Config(config_file)\n",
    "summary = TrainSummary(config.model_dir)\n",
    "dfsummary = summary.table()\n",
    "dfsummary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ba9299-9ce7-4625-8f21-c49a6e3fd75a",
   "metadata": {},
   "source": [
    "It can also be plotted using the `summary.plot`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24962a8b-d8b7-4b38-b982-10633416b86e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot saved to /home/ubuntu/fulldataset/model/AML_biofoun_context/train_summary.pdf\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary.plot(saveplot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346b4f66-b17c-462e-947f-0a3d08972cb4",
   "metadata": {},
   "source": [
    "## Prediction Using the Trained Model\n",
    "\n",
    "Similar to the training step, predictions can be run using the `run_prediction_from_config` function in scGenAI with a simple configuration file. Unlike the training configuration, the prediction configuration file only requires the following parameters to be defined: `mode`, `predict_file`, `batch_size`, `model_dir`, and `outputfile`. All other parameters will be adopted from the trained model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10cd73c8-8ea4-44cf-a2c5-6b2f4aecd53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scGenAI.prediction.predict import run_prediction_from_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d8cab06-6cec-49b1-8fa8-53aa426c38df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_dir: /home/ubuntu/fulldataset/model/AML_biofoun_context/best_model/\n",
      "gmtfile: /home/ubuntu/fulldataset/model/AML_biofoun_context/best_model/biofounction_context\n",
      "predict_file: /efs/common_folder/240501-SCML-00697/vangalen_uniformed.val.h5ad\n",
      "log_dir: /home/ubuntu/scgenai/examples/logs\n",
      "output_dir: /home/ubuntu/scgenai/examples/output\n",
      "seed: 1314521\n",
      "mode: Predict\n",
      "min_cells: 50\n",
      "multiomics: No\n",
      "keepIntermediateFiles: Yes\n",
      "savelog: No\n",
      "savesetting: Yes\n",
      "model_backbone_name: llama\n",
      "model_backbone_size: small\n",
      "context_method: biofounction\n",
      "num_bins: 10\n",
      "optimizer: AdamW\n",
      "weight_decay: 0.01\n",
      "depth: 2\n",
      "max_length: 1024\n",
      "batch_size: 2\n",
      "learning_rate: 1e-05\n",
      "num_epochs: 30\n",
      "world_size: 1\n",
      "evaluate_during_training: False\n",
      "hidden_size: 768\n",
      "num_layers: 12\n",
      "num_heads: 12\n",
      "intermediate_size: 1024\n",
      "stride: 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-29 17:04:47.022 | INFO     | scGenAI.utils.distributed:setup_distributed:26 - Distributed setup complete for GPU 0, Total GPUs used: 4\n",
      "2024-10-29 17:04:47.028 | INFO     | scGenAI.utils.distributed:setup_distributed:26 - Distributed setup complete for GPU 3, Total GPUs used: 4\n",
      "2024-10-29 17:04:47.029 | INFO     | scGenAI.utils.distributed:setup_distributed:26 - Distributed setup complete for GPU 1, Total GPUs used: 4\n",
      "2024-10-29 17:04:47.042 | INFO     | scGenAI.utils.distributed:setup_distributed:26 - Distributed setup complete for GPU 2, Total GPUs used: 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 1489/1489 cells\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU 2 Predicting: 100%|██████████| 1645/1645 [01:27<00:00, 18.88it/s]\n",
      "GPU 0 Predicting: 100%|██████████| 1645/1645 [01:28<00:00, 18.68it/s]\n",
      "GPU 3 Predicting: 100%|██████████| 1645/1645 [01:28<00:00, 18.65it/s]\n",
      "GPU 1 Predicting: 100%|██████████| 1645/1645 [01:29<00:00, 18.41it/s]\n",
      "2024-10-29 17:22:49.233 | INFO     | scGenAI.utils.distributed:cleanup:33 - Cleaned up DDP environment\n",
      "2024-10-29 17:22:49.233 | INFO     | scGenAI.utils.distributed:cleanup:33 - Cleaned up DDP environment\n",
      "2024-10-29 17:22:49.233 | INFO     | scGenAI.utils.distributed:cleanup:33 - Cleaned up DDP environment\n",
      "2024-10-29 17:22:49.233 | INFO     | scGenAI.utils.distributed:cleanup:33 - Cleaned up DDP environment\n",
      "\u001b[32m2024-10-29 17:22:50.882\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mscGenAI.data.postprocess\u001b[0m:\u001b[36mcombine_prediction_results\u001b[0m:\u001b[36m103\u001b[0m - \u001b[1mCompleted post-processing for prediction\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "pred_config_file = '/home/ubuntu/scgenai/tutorials/yaml/config_prediction_AML.yaml'\n",
    "run_prediction_from_config(pred_config_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa3e6fc-55c8-4ef4-b64e-f6347b84fbb4",
   "metadata": {},
   "source": [
    "The prediction output is a CSV file, as defined in the configuration file. It contains the original metadata extracted from the input prediction file (`obs` slot) along with three additional columns: `context_id`, `PredictedFeature`, and `prediction_score`. \n",
    "\n",
    "- `context_id` represents the context used to determine the prediction for the corresponding cell. \n",
    "- `PredictedFeature` is the final predicted feature for the cell using the trained model. \n",
    "- `prediction_score` indicates the confidence level of the prediction, with a maximum value of 1.\n",
    "\n",
    "For example in this tutorials:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b4c89ab7-232d-41a1-8cb4-f59851b5fe6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>orig.ident</th>\n",
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       "      <th>NumberOfGenes</th>\n",
       "      <th>...</th>\n",
       "      <th>Score_Plasma</th>\n",
       "      <th>Score_T</th>\n",
       "      <th>Score_CTL</th>\n",
       "      <th>Score_NK</th>\n",
       "      <th>NanoporeTranscripts</th>\n",
       "      <th>mutation</th>\n",
       "      <th>cell_barcode</th>\n",
       "      <th>context_id</th>\n",
       "      <th>PredictedFeature</th>\n",
       "      <th>prediction_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>BM5-34p38n</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>1158</td>\n",
       "      <td>BM5-34p38n_AAGTTCACACAC</td>\n",
       "      <td>40357.0</td>\n",
       "      <td>33657.0</td>\n",
       "      <td>21238.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HSC_bm</td>\n",
       "      <td>BM5-34p38n_AAGTTCACACAC</td>\n",
       "      <td>0_BM5-34p38n_AAGTTCACACAC_window_EARLY.V1_RAF_...</td>\n",
       "      <td>HSC_bm</td>\n",
       "      <td>0.999993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BM4_TGATGAACCCAN</td>\n",
       "      <td>BM4</td>\n",
       "      <td>14268.0</td>\n",
       "      <td>3829</td>\n",
       "      <td>BM4_TGATGAACCCAN</td>\n",
       "      <td>353818.0</td>\n",
       "      <td>282297.0</td>\n",
       "      <td>157627.0</td>\n",
       "      <td>14268.0</td>\n",
       "      <td>3829.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lateEry_bm</td>\n",
       "      <td>BM4_TGATGAACCCAN</td>\n",
       "      <td>2_BM4_TGATGAACCCAN_window_CAMP_UP_DN.V1_P130_E...</td>\n",
       "      <td>earlyEry_bm</td>\n",
       "      <td>0.999991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BM4_CAAGTTCTGTTC</td>\n",
       "      <td>BM4</td>\n",
       "      <td>5293.0</td>\n",
       "      <td>1700</td>\n",
       "      <td>BM4_CAAGTTCTGTTC</td>\n",
       "      <td>83915.0</td>\n",
       "      <td>67154.0</td>\n",
       "      <td>51102.0</td>\n",
       "      <td>5293.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prog_bm</td>\n",
       "      <td>BM4_CAAGTTCTGTTC</td>\n",
       "      <td>1_BM4_CAAGTTCTGTTC_window_PTEN_EIF4E_STK33_DEP...</td>\n",
       "      <td>Prog_bm</td>\n",
       "      <td>0.999996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BM3_CATGAGGACTAN</td>\n",
       "      <td>BM3</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>540</td>\n",
       "      <td>BM3_CATGAGGACTAN</td>\n",
       "      <td>15708.0</td>\n",
       "      <td>13850.0</td>\n",
       "      <td>10240.0</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T_bm</td>\n",
       "      <td>BM3_CATGAGGACTAN</td>\n",
       "      <td>2_BM3_CATGAGGACTAN_window_UP_EGFR_DN.V2_BMI_CA...</td>\n",
       "      <td>T_bm</td>\n",
       "      <td>0.999973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BM4_ATCCTGCTTGGT</td>\n",
       "      <td>BM4</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>621</td>\n",
       "      <td>BM4_ATCCTGCTTGGT</td>\n",
       "      <td>18320.0</td>\n",
       "      <td>14737.0</td>\n",
       "      <td>8932.0</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T_bm</td>\n",
       "      <td>BM4_ATCCTGCTTGGT</td>\n",
       "      <td>3_BM4_ATCCTGCTTGGT_window_ERBB2_BILD_SHH_UP.N4...</td>\n",
       "      <td>T_bm</td>\n",
       "      <td>0.999997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     index  orig.ident  nCount_RNA  nFeature_RNA   \n",
       "0  BM5-34p38n_AAGTTCACACAC  BM5-34p38n      3450.0          1158  \\\n",
       "1         BM4_TGATGAACCCAN         BM4     14268.0          3829   \n",
       "2         BM4_CAAGTTCTGTTC         BM4      5293.0          1700   \n",
       "3         BM3_CATGAGGACTAN         BM3      1116.0           540   \n",
       "4         BM4_ATCCTGCTTGGT         BM4      1489.0           621   \n",
       "\n",
       "                      Cell  NumberOfReads  AlignedToGenome   \n",
       "0  BM5-34p38n_AAGTTCACACAC        40357.0          33657.0  \\\n",
       "1         BM4_TGATGAACCCAN       353818.0         282297.0   \n",
       "2         BM4_CAAGTTCTGTTC        83915.0          67154.0   \n",
       "3         BM3_CATGAGGACTAN        15708.0          13850.0   \n",
       "4         BM4_ATCCTGCTTGGT        18320.0          14737.0   \n",
       "\n",
       "   AlignedToTranscriptome  TranscriptomeUMIs  NumberOfGenes  ...   \n",
       "0                 21238.0             3450.0         1158.0  ...  \\\n",
       "1                157627.0            14268.0         3829.0  ...   \n",
       "2                 51102.0             5293.0         1700.0  ...   \n",
       "3                 10240.0             1116.0          540.0  ...   \n",
       "4                  8932.0             1489.0          621.0  ...   \n",
       "\n",
       "   Score_Plasma Score_T Score_CTL Score_NK NanoporeTranscripts    mutation   \n",
       "0         0.008   0.009     0.010    0.009                 NaN      HSC_bm  \\\n",
       "1         0.004   0.004     0.002    0.002                 NaN  lateEry_bm   \n",
       "2         0.029   0.024     0.010    0.010                 NaN     Prog_bm   \n",
       "3         0.029   0.281     0.261    0.115                 NaN        T_bm   \n",
       "4         0.018   0.470     0.184    0.068                 NaN        T_bm   \n",
       "\n",
       "              cell_barcode                                         context_id   \n",
       "0  BM5-34p38n_AAGTTCACACAC  0_BM5-34p38n_AAGTTCACACAC_window_EARLY.V1_RAF_...  \\\n",
       "1         BM4_TGATGAACCCAN  2_BM4_TGATGAACCCAN_window_CAMP_UP_DN.V1_P130_E...   \n",
       "2         BM4_CAAGTTCTGTTC  1_BM4_CAAGTTCTGTTC_window_PTEN_EIF4E_STK33_DEP...   \n",
       "3         BM3_CATGAGGACTAN  2_BM3_CATGAGGACTAN_window_UP_EGFR_DN.V2_BMI_CA...   \n",
       "4         BM4_ATCCTGCTTGGT  3_BM4_ATCCTGCTTGGT_window_ERBB2_BILD_SHH_UP.N4...   \n",
       "\n",
       "   PredictedFeature  prediction_score  \n",
       "0            HSC_bm          0.999993  \n",
       "1       earlyEry_bm          0.999991  \n",
       "2           Prog_bm          0.999996  \n",
       "3              T_bm          0.999973  \n",
       "4              T_bm          0.999997  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "preconfig = Config(pred_config_file)\n",
    "dfprediction = pd.read_csv(preconfig.outputfile)\n",
    "dfprediction.head()"
   ]
  }
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