438 lines
282 KiB
Plaintext
438 lines
282 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": 1,
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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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"2025-08-14 15:56:50.631778: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
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"2025-08-14 15:56:50.632231: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
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"2025-08-14 15:56:50.684833: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
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"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/attr_value.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/tensor.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/resource_handle.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/tensor_shape.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/types.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/full_type.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/function.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/node_def.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/op_def.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/graph.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/graph_debug_info.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/versions.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/protobuf/config.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at xla/tsl/protobuf/coordination_config.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/cost_graph.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/step_stats.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/allocation_description.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/tensor_description.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/protobuf/cluster.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/google/protobuf/runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/protobuf/debug.proto. Please update the gencode to avoid compatibility violations in the next runtime release.\n",
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" warnings.warn(\n",
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"2025-08-14 15:56:52.092041: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
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"2025-08-14 15:56:52.093165: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n"
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]
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}
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],
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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": 2,
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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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" Position Codon 33bp \\\n",
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"0 114 GCC TCTGGAAGAAGTAAACGCCGAGCTGGAACAGCC \n",
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"1 1794 CCC GGGGCAGTCCCCTAGCCCCGCTCAAAAGGGGGA \n",
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"2 234 GTC CCACAAGTCTCGTTTCGTCGATCTTCTGGAGTT \n",
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"3 129 TCC GGCTGCGGTTGCAAACTCCGAAGTCGATGCACT \n",
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"4 216 GAC GGAGCAGCGGGTAAATGACCTCTTGGAGCTGTT \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 CAATTCCAATTCCATGTCGATGATCGGTCAAAGCCCCCCGTGCTGC... 5 \n",
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"3 NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN... 12 \n",
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"4 TGAACTTAAGAGCCGCATTCTTTCCGATATGGTGCGACTTGACATT... 14 \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 ATGTCGAGTAGTATCGTCCTCAGTAATAATAATTCCAATTCCAATT... \n",
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"3 ATGAACAAAGAAAATGTCATTACCCTGGACAATCCGGTCAAACGTG... \n",
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"4 ATGCAAGACATATTAAGTGAACTTAAGAGCCGCATTCTTTCCGATA... \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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"/mnt/lmpbe/guest01/FScanpy-package-main/FScanpy/predictor.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
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" from pkg_resources import resource_filename\n",
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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/sklearn/base.py:380: InconsistentVersionWarning: Trying to unpickle estimator _BinMapper from version 1.6.0 when using version 1.6.1. 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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"/home/guest01/.conda/envs/fs/lib/python3.9/site-packages/sklearn/base.py:380: InconsistentVersionWarning: Trying to unpickle estimator HistGradientBoostingClassifier from version 1.6.0 when using version 1.6.1. 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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"/mnt/lmpbe/guest01/FScanpy-package-main/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_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": "iVBORw0KGgoAAAANSUhEUgAABS4AAAL0CAYAAAAC1KkVAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkYtJREFUeJzs3QeYHVXZAOCz6QkQegsEAqhApIMgIEW6KIqiICpNRBARBUVAkYDIj91YULAAoiCIBVEQBARRAelFKUpN6KEmkJBkN/d/vrM7m7ubzWZ3s2Xu3vd9npvsnXvvzJk5M2dmvjmloVKpVBIAAAAAQIkMGegEAAAAAAC0J3AJAAAAAJSOwCUAAAAAUDoClwAAAABA6QhcAgAAAAClI3AJAAAAAJSOwCUAAAAAUDoClwAAAABA6QhcAgAAAAClI3AJAIPQ9ddfnxoaGtLLL7+c35933nlpmWWWWax59sY8asUpp5ySNtlkkz6Z78orr5zz5tJLL+31+dM3fvazn6Xddtst1et+S/8566yz0l577TXQyQCA0hC4BKDuHHzwwTlwFK8RI0akN7zhDenLX/5yamxsbBP0K14rrrhi2nPPPdO999670PlUvx566KEOl9t+vhHA2meffdIjjzzS5+u83377pf/+979d/v6ECRPS5MmTF2sePbXjjju2bqNRo0alN73pTemMM85IlUolDZTI67333nux5nH//fenU089NZ199tnp6aefTu94xztSb7r77rvTu9/97rTSSivl7RZ5GHn23HPP9epyatXrr7+e83HDDTdMw4YN63J+xu++9KUvpUmTJrUJEFYfy0svvXTabrvt0t/+9rc+XIOUfvvb3+bjI5a35JJLpo022iiXXS+++GLqa/314KKz5fRFwL9sDxE++tGPpjvuuCP9/e9/H+ikAEApCFwCUJf22GOPHDz63//+lz772c/mQMQ3vvGNNt958MEH83euuuqqNHv27PTOd74zzZkzp8P5VL/WWmutTpcd833qqafSJZdckv7zn//k2jVNTU0LfC8CdUUwdXGNHj06B7QGeh5dddhhh+VtGdvqxBNPTCeffHKuiVTLHn744fz/e97znrTKKqukkSNH9mg+c+fOXWDatGnT0s4775yWW265vL9GkPTcc89N48aNS6+99tpip30wiGMs9uGjjz467bLLLl3+3W9+85s0duzYtO2227aZ/uY3v7n1mL/pppvSG9/4xvSud70rvfLKK4uVxnnz5nX42Re/+MUciH7LW96S/vznP6d///vf6Vvf+lYOWP/iF79Ifamjfa6romyNgDFdEw/TPvShD6Xvfe97A50UACgFgUsA6lIEjSJ4tOaaa6ZPfOITOZBx2WWXtflOBOniO5tttln6zGc+k6ZOnZoeeOCBDudT/Ro6dGiny475rrrqqmn77bfPAbn77rsv19IsamRGUGLzzTfP8/7HP/6RAxlR4zACohF42XjjjXMwpdoVV1yRaybG529/+9vTY489tshaTH/84x9zECRq562wwgrpve99b54eNboef/zxdMwxx7TWKFvYPH70ox+lddZZJ99sr7vuugsEUOK3P/3pT/O8x4wZk4M77bdzR+K7Rf4ccsghuWbZ1Vdf3fp5BJI/97nPpdVWWy0tscQSaauttsrbrxDpj4Dwsssumz+PIFNso4WtR9S4Ktazo8DLz3/+8/SHP/yhdXvEsiKIfdRRR+W8jG0YaY18Wtg8iuafQ4YMaV1W5G3UmFt99dVzfkcz3yuvvLL1d5GP8d2LL7447bDDDnk5F1xwwQLz/+c//5kDZrGtN91007yvxH7wne98p00gPYJdUdMzautFjd8DDjggPf/8862fR5DzwAMPzJ/HekVgLPaH2P87q6EW2zO2ayGOlX333TdPj2BqBGur98miBus3v/nNvJzll18+ffKTn2wTIIs8Pv7449P48ePztoma0dFku6vr0l7sB7G/RlA89q2uuuiiizpsuhu1NotjfuLEiTkfX3311Ta1kr/97W/nGp6x7FiPI488Mn+nUOyLcUzEPGI9p0yZssCybrnllvR///d/OT/iAcs222yTa9TuuuuuuRbmQQcd1Ob7cRzG51Ez84Mf/GCaMWNG62exf73tbW/Ly43tHsHWIqje2T4Xx2HsY8UxEPv0QFvUfnbrrbfmbRTlW2yLWJ+ozViIbRSifIp1Kt4XTe7POeectMYaa+R9LPIuAstf//rXc55HOX766ae3SU9X8zuOnygLY9vuvvvueT2qxf4W+8SsWbP6bNsBQK0QuASAltqE7WtTFuJmPYIXIQJ0vb3cUL3sE044IX31q1/NteYiYBfBsPPPPz/XOIwamhFQ/MhHPtLaLDVuet/3vvflm9277rorfexjH8vz6Mzll1+eb9ajCfydd96Zrr322rTlllvmz373u9/lQFoEYooaZR35/e9/nz796U/nGqsRRDr88MNzcOO6665r871oHh3BhXvuuScv78Mf/nCXm7ZGrdNoMhkB4+ptHwHDqOUW+RLz/cAHPpBrv0YN2hBBsAh83XDDDbmJ/9e+9rUcfOiJCJBG+qtr10bgKGpERXDh17/+da4ZGsGdIvDR0TyiBmSo3qbf/e53czAqAnixHhHEiObexXoUIj9jW8c+Ed9pLwIpUTs38mRhTeqjv9OddtopBzZvu+22HMB69tln87oVjjvuuLxfRZD2L3/5Sw7QVgd6uiKCj5HGpZZaKuddBFVj28f2q97PYz+JgFn8H4HhCOpUBz8jgPqrX/0qb+dY72hiX+RhV9alt8TDgy222KLT78S+FvkbQakI4BciSB3pj+M21vGvf/1r+vznP9/mtzNnzsz7ZwSd43sd1WqOfasInnWkOhAf2zQCY3/605/yK/IzypPq4PSxxx6bt1sc95HGKAva1/Ss3uciCB5dR0TN02L/jX16IHVlP4uAbQR1Iw9vvvnmHCyMMqgI5EZgM0TexToV74vtGA+RYt+K/TCC5lHr/oknnsjbNPLspJNOSv/617+6nd8R8IwyPdIc+3IEl6vF/hbHc/W8AaBuVQCgzhx00EGV97znPfnvefPmVa6++urKyJEjK5/73OfytOuuuy4iP5Ulllgiv+LveL373e9eYD5Dhw5t/V683v/+9y90ucV8X3rppfz+qaeeqmyzzTaV1VZbrTJ79uzWzy+99NLW37z++uuVMWPGVG688cY28zr00EMr+++/f/77xBNPrEycOLHN58cff3ybZZ177rmVpZdeuvXzrbfeuvLhD394oWldc801K9/5znfaTGs/j0j7YYcd1uY7H/jAByp77rln6/tIw0knndT6/tVXX83T/vznPy902TvssENl+PDheXvG//H9UaNGVf75z3/mzx9//PG83Z988sk2v9t5553ztggbbrhh5ZRTTulw/u3XI/z+97/PyylMmjSpsvHGG3e4zxQ+9alPVXbaaae8D3VF+2WEcePGVU4//fQ2097ylrdUjjzyyPz3o48+mn8zefLkRc7/C1/4QmXYsGGV5ZZbrrLHHntUvv71r1eeeeaZ1s9PO+20ym677dbmN1OnTs3zf/DBByszZsyojBgxovLrX/+69fMXXnihMnr06MqnP/3p1mnx/ViXarE9Y7uGX/ziF5V11123zXaJ/Tvmc9VVV7Vuz9jHGhsb2+w7++23X/470hPLiWOzI4tal0XpKD87EsdPzPOGG25oMz32jyFDhrQe9w0NDZWxY8d2ul+HSy65pLL88su3vo9tFvO/6667Ov3dO97xjspGG220yPRGuqK8mD59euu04447rrLVVlst9DfTpk3Labj33ns73ec6Om66ItIU27urim1SXa5Wl8XFvteV/ay9pqamylJLLVX54x//2On+3NF23H333SsTJkzI8yjE8s8444xu5/fNN9/cOu3+++/P0/71r3+1+e2yyy5bOe+88xa5vQBgsBs20IFTABgIURMpaudErZ2oaRR9irVv+hi1eKLJctTUiWaaHfWxGDWRovlpIZoILkrUZoz75ah5E82+o6lndW3C6tpd0YQ8vhfNHatFjaKobRaiRlQ0la6
|
|||
|
|
"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": {
|
|||
|
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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": {
|
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|
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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": "3.9.23"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|