FScanpy-package/predict_sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import FScanpy related modules\n",
    "from FScanpy import PRFPredictor, predict_prf, plot_prf_prediction\n",
    "from FScanpy.data import get_test_data_path, list_test_data\n",
    "from FScanpy.utils import fscanr, extract_prf_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   GB_Probability  CNN_Probability  Voting_Probability  Position Codon  \\\n",
      "0        0.883519         0.950972            0.923991       114   GCC   \n",
      "1        0.910635         0.988631            0.957433      1794   CCC   \n",
      "2        0.890379         0.979877            0.944078      1821   AAC   \n",
      "3        0.953772         0.962459            0.958984      1881   AAC   \n",
      "4        0.941618         0.946840            0.944751      1194   CTT   \n",
      "\n",
      "                                33bp  \\\n",
      "0  TCTGGAAGAAGTAAACGCCGAGCTGGAACAGCC   \n",
      "1  GGGGCAGTCCCCTAGCCCCGCTCAAAAGGGGGA   \n",
      "2  ACCACCCCATCAGGGAAACCGGGTGGAGGGGCC   \n",
      "3  CACCGGGCCAGGAAATAACCCAGTATTCCCAGT   \n",
      "4  ACTAATAGAGGGGGGACTTAGCGCCCCCCAAAC   \n",
      "\n",
      "                                               399bp  Sequence_ID  \\\n",
      "0  NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...            0   \n",
      "1  GACAGGACACATCAGAAAAGACTGTAAGGATGAAAAGGGCTCAAAA...            1   \n",
      "2  AAATAAAGAGAAAGGAGGGTGTTGCTTTAAATGCGGTAAAAAAGGA...            2   \n",
      "3  CCTGTACCTCCCTGAGGCAAAAAGGCCGCCTGTAATCTTGCCAATA...            3   \n",
      "4  GCCCGGGCCTCGGCAACCGGCCCCCAAAAGGCCCCCCCCGGGACCA...            4   \n",
      "\n",
      "                                       Full_Sequence  \n",
      "0  ATGTTTGAAATTAACCCGGTGAATAACCGCATTCAGGACCTCACGG...  \n",
      "1  ATGGGGGTCTCGGGATCAAAAGGGCAGAAACTCTTTGTTTCTGTTC...  \n",
      "2  ATGGGGCAAGAATTAAGCCAGCATGAACGTTATGTAGAACAATTGA...  \n",
      "3  ATGGGCCAAATCTTTTCCCGTAGCGCTAGCCCTATTCCGCGGCCGC...  \n",
      "4  ATGGGAAATTCCCCCTCCTATAACCCCCCCGCTGGTATCTCCCCCT...  \n"
     ]
    }
   ],
   "source": [
    "data = pd.read_excel(get_test_data_path('full_seq.xlsx'))\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator _BinMapper from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator HistGradientBoostingClassifier from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "Exception in thread Thread-9 (_readerthread):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 766, in run_closure\n",
      "    _threading_Thread_run(self)\n",
      "  File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\subprocess.py\", line 1515, in _readerthread\n",
      "    buffer.append(fh.read())\n",
      "  File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\codecs.py\", line 322, in decode\n",
      "    (result, consumed) = self._buffer_decode(data, self.errors, final)\n",
      "UnicodeDecodeError: 'utf-8' codec can't decode byte 0xce in position 4: invalid continuation byte\n",
      "a:\\Code\\fscanpy-package\\FScanpy\\predictor.py:347: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_results0, fig0 = plot_prf_prediction(\n",
    "    sequence=str(data.iloc[0]['Full_Sequence']),\n",
    "    window_size=3,\n",
    "    short_threshold=0.2,\n",
    "    long_threshold=0.2,\n",
    "    ensemble_weight=0.6,\n",
    "    title=f\"PRF Prediction Results for Sequence {data.iloc[0]['Sequence_ID']} (Bar Chart + Heatmap)\",\n",
    "    figsize=(16, 8),\n",
    "    dpi=150\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结果解读：Sequence0\n",
    "### 真实情况\n",
    "该序列核糖体程序性移码发生于第113nt处\n",
    "### 图上信息\n",
    "在该处我们可以看到一个显著的最高峰，并且明显较粗。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator _BinMapper from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator HistGradientBoostingClassifier from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "a:\\Code\\fscanpy-package\\FScanpy\\predictor.py:347: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_results1, fig1 = plot_prf_prediction(\n",
    "    sequence=str(data.iloc[1]['Full_Sequence']),\n",
    "    window_size=3,\n",
    "    short_threshold=0.2,\n",
    "    long_threshold=0.2,\n",
    "    ensemble_weight=0.6,\n",
    "    title=f\"PRF Prediction Results for Sequence {data.iloc[1]['Sequence_ID']} (Bar Chart + Heatmap)\",\n",
    "    figsize=(16, 8),\n",
    "    dpi=150\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结果解读：Sequence1\n",
    "\n",
    "### 真实情况\n",
    "该序列核糖体程序性移码发生于第1794nt处。\n",
    "### 图上信息\n",
    "在该处我们可以看到一个显著的高峰，但是肉眼难以分辨改高峰与其他位置的高峰的差异，因此需要提高分辨率。将window size参数调整更小，查看每个高峰周围碱基的移码概率，基于高概率位点的集中程度判断其移码可能性。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator _BinMapper from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator HistGradientBoostingClassifier from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "a:\\Code\\fscanpy-package\\FScanpy\\predictor.py:347: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_results1, fig1 = plot_prf_prediction(\n",
    "    sequence=str(data.iloc[1]['Full_Sequence']),\n",
    "    window_size=1,\n",
    "    short_threshold=0.2,\n",
    "    long_threshold=0.2,\n",
    "    ensemble_weight=0.6,\n",
    "    title=f\"PRF Prediction Results for Sequence {data.iloc[1]['Sequence_ID']} (Bar Chart + Heatmap)\",\n",
    "    figsize=(16, 8),\n",
    "    dpi=150\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高分辨率的结果解读：Sequence1\n",
    "\n",
    "### 真实情况\n",
    "该序列核糖体程序性移码发生于第1794nt处。\n",
    "### 图上信息\n",
    "在该位置存在大量的高概率碱基集中，但是其他高峰周围并不存在，因此其PRF可能性高于其余高峰。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator _BinMapper from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator HistGradientBoostingClassifier from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "a:\\Code\\fscanpy-package\\FScanpy\\predictor.py:347: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_results2, fig2 = plot_prf_prediction(\n",
    "    sequence=str(data.iloc[2]['Full_Sequence']),\n",
    "    window_size=3,\n",
    "    short_threshold=0.2,\n",
    "    long_threshold=0.2,\n",
    "    ensemble_weight=0.6,\n",
    "    title=f\"PRF Prediction Results for Sequence {data.iloc[12]['Sequence_ID']} (Bar Chart + Heatmap)\",\n",
    "    figsize=(16, 8),\n",
    "    dpi=150\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator _BinMapper from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator HistGradientBoostingClassifier from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "a:\\Code\\fscanpy-package\\FScanpy\\predictor.py:347: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_results0, fig0 = plot_prf_prediction(\n",
    "    sequence=str(data.iloc[3]['Full_Sequence']),\n",
    "    window_size=3,\n",
    "    short_threshold=0.2,\n",
    "    long_threshold=0.2,\n",
    "    ensemble_weight=0.6,\n",
    "    title=f\"PRF Prediction Results for Sequence {data.iloc[3]['Sequence_ID']} (Bar Chart + Heatmap)\",\n",
    "    figsize=(16, 8),\n",
    "    dpi=150\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator _BinMapper from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\sklearn\\base.py:440: InconsistentVersionWarning: Trying to unpickle estimator HistGradientBoostingClassifier from version 1.6.0 when using version 1.7.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "a:\\Code\\fscanpy-package\\FScanpy\\predictor.py:347: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_results4, fig4 = plot_prf_prediction(\n",
    "    sequence=str(data.iloc[4]['Full_Sequence']),\n",
    "    window_size=3,\n",
    "    short_threshold=0.2,\n",
    "    long_threshold=0.2,\n",
    "    ensemble_weight=0.4,\n",
    "    title=f\"PRF Prediction Results for Sequence {data.iloc[4]['Sequence_ID']} (Bar Chart + Heatmap)\",\n",
    "    figsize=(16, 8),\n",
    "    dpi=150\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结果解读：Sequence4\n",
    "### 真实情况\n",
    "该序列核糖体程序性移码发生于第216nt处\n",
    "### 图上信息\n",
    "我们的算法并不能总是解决问题：在该处我们可以看到三个显著的高峰，其中80nt左右和216nt左右的高峰并无明显差距。我们需要通过湿实验验证位点的真实性。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fs",
   "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": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}