403 lines
268 KiB
Plaintext
403 lines
268 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Import FScanpy related modules\n",
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"from FScanpy import PRFPredictor, predict_prf, plot_prf_prediction\n",
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"from FScanpy.data import get_test_data_path, list_test_data\n",
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"from FScanpy.utils import fscanr, extract_prf_regions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" GB_Probability CNN_Probability Voting_Probability Position Codon \\\n",
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"0 0.883519 0.950972 0.923991 114 GCC \n",
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"1 0.910635 0.988631 0.957433 1794 CCC \n",
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"2 0.890379 0.979877 0.944078 1821 AAC \n",
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"3 0.953772 0.962459 0.958984 1881 AAC \n",
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"4 0.941618 0.946840 0.944751 1194 CTT \n",
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"\n",
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" 33bp \\\n",
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"0 TCTGGAAGAAGTAAACGCCGAGCTGGAACAGCC \n",
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"1 GGGGCAGTCCCCTAGCCCCGCTCAAAAGGGGGA \n",
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"2 ACCACCCCATCAGGGAAACCGGGTGGAGGGGCC \n",
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"3 CACCGGGCCAGGAAATAACCCAGTATTCCCAGT \n",
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"4 ACTAATAGAGGGGGGACTTAGCGCCCCCCAAAC \n",
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"\n",
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" 399bp Sequence_ID \\\n",
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"0 NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN... 0 \n",
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"1 GACAGGACACATCAGAAAAGACTGTAAGGATGAAAAGGGCTCAAAA... 1 \n",
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"2 AAATAAAGAGAAAGGAGGGTGTTGCTTTAAATGCGGTAAAAAAGGA... 2 \n",
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"3 CCTGTACCTCCCTGAGGCAAAAAGGCCGCCTGTAATCTTGCCAATA... 3 \n",
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"4 GCCCGGGCCTCGGCAACCGGCCCCCAAAAGGCCCCCCCCGGGACCA... 4 \n",
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"\n",
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" Full_Sequence \n",
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"0 ATGTTTGAAATTAACCCGGTGAATAACCGCATTCAGGACCTCACGG... \n",
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"1 ATGGGGGTCTCGGGATCAAAAGGGCAGAAACTCTTTGTTTCTGTTC... \n",
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"2 ATGGGGCAAGAATTAAGCCAGCATGAACGTTATGTAGAACAATTGA... \n",
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"3 ATGGGCCAAATCTTTTCCCGTAGCGCTAGCCCTATTCCGCGGCCGC... \n",
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"4 ATGGGAAATTCCCCCTCCTATAACCCCCCCGCTGGTATCTCCCCCT... \n"
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]
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}
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],
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"source": [
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"data = pd.read_excel(get_test_data_path('full_seq.xlsx'))\n",
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"print(data.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
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" warnings.warn(\n",
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"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",
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"https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
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" warnings.warn(\n",
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"Exception in thread Thread-9 (_readerthread):\n",
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"Traceback (most recent call last):\n",
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" File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
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" self.run()\n",
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" File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 766, in run_closure\n",
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" _threading_Thread_run(self)\n",
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" File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\threading.py\", line 953, in run\n",
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" self._target(*self._args, **self._kwargs)\n",
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" File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\subprocess.py\", line 1515, in _readerthread\n",
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" buffer.append(fh.read())\n",
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" File \"c:\\Users\\31598\\.conda\\envs\\fs\\lib\\codecs.py\", line 322, in decode\n",
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" (result, consumed) = self._buffer_decode(data, self.errors, final)\n",
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"UnicodeDecodeError: 'utf-8' codec can't decode byte 0xce in position 4: invalid continuation byte\n",
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"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",
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" plt.tight_layout()\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1600x800 with 3 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"sequence_results0, fig0 = plot_prf_prediction(\n",
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" sequence=str(data.iloc[0]['Full_Sequence']),\n",
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" window_size=3,\n",
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" short_threshold=0.2,\n",
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" long_threshold=0.2,\n",
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" ensemble_weight=0.6,\n",
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" title=f\"PRF Prediction Results for Sequence {data.iloc[0]['Sequence_ID']} (Bar Chart + Heatmap)\",\n",
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" figsize=(16, 8),\n",
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" dpi=150\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 结果解读：Sequence0\n",
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"### 真实情况\n",
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"该序列核糖体程序性移码发生于第113nt处\n",
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"### 图上信息\n",
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"在该处我们可以看到一个显著的最高峰，并且明显较粗。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
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" warnings.warn(\n",
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"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",
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"https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
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" warnings.warn(\n",
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"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",
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" plt.tight_layout()\n"
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]
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},
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{
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"data": {
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"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": {
|
|||
|
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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"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
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"data": {
|
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABSYAAAL0CAYAAAARA+nhAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAe3VJREFUeJzt3QeYXFX9P+CTQgolFKmBSEAEDSU0QToISpEqYgSkSQkC0hQpIgEREFCMJUBEigUEQUSRJiKICEovSq+JlNBJaKnzf77n95/N7GZ3s7uZvbOz877PM8/utDtn7pl7597PnNKnVCqVEgAAAABAgfoW+WIAAAAAAEEwCQAAAAAUTjAJAAAAABROMAkAAAAAFE4wCQAAAAAUTjAJAAAAABROMAkAAAAAFE4wCQAAAAAUTjAJAAAAABROMAkAvcBtt92W+vTpk95+++18/ZJLLkmLLLLIPC2zGsuoFyeffHJac801u2W5Sy21VK6ba665purLp3b22muvdPrpp6eebvPNN09HHnlkrYvBPPjyl7+cfvjDH9a6GADQLQSTAPR6++67bw6G4jJgwIC00korpe9+97tpxowZzUK98mWJJZZI2223XXrkkUfaXE7l5emnn271dVsuNwKqXXfdNT377LPd/p5HjRqVnnzyyQ4/fvjw4Wns2LHztIx5CU7K62jQoEFp5ZVXTmeccUYqlUqpVqKud95553laxmOPPZZOOeWUNH78+PTyyy+nbbfdNlXTQw89lHbccce05JJL5vUWdRh19uqrr1b1derVhx9+mOtx9dVXT/37959rff7zn//Mj+tIQB3r/vrrr0+HH354q5/j8va+2267pRdeeCF1l2nTpqWzzjorjRw5Ms0///xp8cUXTxtttFG6+OKL0/Tp01M9bCfz8jotf5Cphu5Y5rw68cQT02mnnZbeeeedWhcFAKpOMAlAQ9hmm21yOPTUU0+lb3zjG7kl29lnn93sMU888UR+zE033ZSmTp2aPv/5z+cT/9aWU3lZYYUV2n3tWO5LL72UrrzyyvTf//437bDDDmnmzJlzPC6CuHJYOq8GDx6cA6taL6OjDjzwwLwuY10df/zx6aSTTkrnn39+qmfPPPNM/rvTTjulpZdeOg0cOLBLy2ktYHrttdfSlltumRZbbLH8eY0QNMKooUOHpvfee2+ey94bxDYWn+EID7faaqt2Hxsh1N57753XaUf89Kc/zaHjggsu2OrnOLb3P/7xj2nixInpK1/5yjy9j5b7oMrbt9566/T9738/HXTQQenOO+9Md999dzr00ENz+WJf053rdtasWV1+foS40SKbjllttdXSxz72sfSb3/ym1kUBgKoTTALQECIUinBo+eWXT1/72tdyUPGnP/2p2WMihIvHrL322rnrY4QKjz/+eKvLqbz069ev3deO5S6zzDJp0003zYHbo48+mltZllvm3HDDDWmdddbJy77jjjvyCX+0GIzAM4KVaA111VVXNVtmtNaKloVx/xZbbJGef/75uXbDvvbaa9OnPvWp3LouWlbtsssuTSFBtOo66qijmlp7tbWM8847L58gR8vTVVZZJf36179udn889xe/+EVedrTg+vjHPz7Hem5NPLZcP/vtt19aY4010s0339x0fwTF3/zmN9Oyyy6bFlhggbT++uvn9VcW5Y/Ad9FFF833r7rqqnkdtfU+olt1+X22FKH1L3/5yxwslddHvFYEQYcddliuy1iHUdaop7aWEeUJffv2bXqtqNtorbvccsvl+o7WeTfeeGPT86Ie47FXXHFF2myzzfLrXHrppa227ovWU7Gu11prrfxZic/Bj370o2ZB+X/+85/cUjMCtGjBF92PX3/99ab7I8SMQC7uj/cV3UVbdv1trRt6rM/KYCm2lS996Uv59ghLI4yt/EyWW7z94Ac/yK/zkY98JAdolaFr1PGxxx6bhg0bltdNtGy+8MILO/xeWorPQXxeIyyMz1Z7Dj744LTHHnukDTbYIHUklIvtsVy/rX2O4z1++tOfzp+X+++/v9lz999//6ZtO7ahH//4x82WUV5X0UIuguZ4TGuihfPtt9+ebrnllrwu47O04oor5vfx73//O297ZfG5+9a3vpXrJsoXn89K55xzTm5ZGuss1v8hhxyS3n333ab7y9tQbMsjRozI9fPVr3611e2k1mIfuskmm+T1G+8lgunKsD72Weuuu25aaKGF8rqI9VVuZRyf2diOQuxL4j1FfYTYLr7+9a/nbSPui8/gBRdckJcd+6xYXnxmY3/elfqO1tXRWn/IkCH589gykI7P2+WXX96t6w4AakEwCUBDipPEtloiReBTPgGMAK7arxsqX/u4447LrZ6i1VsEchF2/epXv8otBqPVUwSG0erq73//e1MI9IUvfCGfqD744IPpgAMOyMtoz3XXXZfDwuii/sADD+QwY7311sv3XX311Tkoi8Cs3Aq0NX/4wx/SEUcckVucRkg0evTofEJ+6623NntcnGBHSPXwww/n19tzzz3Tm2++2aH1E61G//GPf+RAuHLdR8Bz11135XqJ5UZrtWi9Gi1gQwQzEWxFUBNd8M8888w5WrN1VASgUf7K1rEbbrhh+slPfpKDmd/97ne5ZWcEhtF9uq1lRAvGULlOI5SI8C8Cungf0eItumOX30dZ1Ges6/hMxGNaikAlWtdGnbTV5T1aAX7mM5/JweW9996bA9BJkybl91Z2zDHH5M9VhEt/+ctfcrBUGaR1RISLUcYIZqLuIjSNdR/rr/JzHp+TaEUafyPQirCrMtyMgPS3v/1tXs/xvqMLfLkOO/JeuirqKYZXGDNmTIceH/UW+4gIt9oTn/n4rESIXhkQxrYWrafjB4r4oeKEE07Ij6sU22d8xiKc//Of/9zq8uPzFz+wxDppab755sshY1ms77gegWV0/Y5tvTL4j/A81nvsb+Kxf/vb33KQWen999/P21WE4fG4eHxr20ktxecryhNDZkQ9RcAfQWXsPyo/r6eeemrujh+Be4SR5fAxgszf//73zVrQVwaJsW7iR51omRohZfzIFfuieN+x3Xzuc5/LgXmsq87Wd3zmY/uLbSD2ybEfrRT763jd2M8BQK9SAoBebp999inttNNO+f9Zs2aVbr755tLAgQNL3/zmN/Ntt956ayQ7pQUWWCBf4v+47LjjjnMsp1+/fk2Pi8sXv/jFNl+3vNy33norX3/ppZdKG264YWnZZZctTZ06ten+a665puk5H374YWn++ecv3Xnnnc2Wtf/++5d23333/P/xxx9fGjFiRLP7jz322GavdfHFF5cWXnjhpvs32GCD0p577tlmWZdffvnSj370o2a3tVxGlP3AAw9s9pjddtuttN122zVdjzKceOKJTdfffffdfNsNN9zQ5mtvttlmpfnmmy+vz/gbjx80aFDpn//8Z77/hRdeyOv9xRdfbPa8LbfcMq+LsPrqq5dOPvnkVpff8n2EP/zhD/l1ysaMGVMaOXJkq5+Zsq9//eulz3zmM/kz1BEtXyMMHTq0dNpppzW77VOf+lTpkEMOyf8/99xz+Tljx46d6/JPOOGEUv/+/UuLLbZYaZtttimdddZZpVdeeaXp/lNPPbX0uc99rtlzJk6cmJf/xBNPlKZMmVIaMGBA6Xe/+13T/W+88UZp8ODBpSOOOKLptnh8vJdKsT5jvYZf//rXpVVWWaXZeonPdyznpptualqf8RmbMWNGs8/OqFGj8v9Rnnid2DZbM7f3Mjet1Wd48sknS0suuWTTMlp+DloT6yI+jy0/B5Wf49iGo2wrr7xyrtP2HHrooaVdd921WVmXWmqpvA7bE+v38MMPb/cx5XJtvPHGc3zmYp/RliuvvLL0kY98pOl61HW8nwcffLBD67UjZSp/fjqitX1vXGI/Ubnfi/3kQQcd1Oy5//jHP0p9+/YtffDBB60u+5577snLiO2htf12W+sxPstRhr322qvptpdffjk/96677upUfcc2/N577zXddt5555UWXHDB0syZM5tue+ihh/Kyn3/++Q6tMwCoF1pMAtAQotVRtL6KrrHRHTQmCWn
|
|||
|
|
"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
|
|||
|
|
}
|