938 lines
82 KiB
Plaintext
938 lines
82 KiB
Plaintext
{
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"cells": [
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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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"# FScanpy \n",
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"\n",
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"This notebook demonstrates how to use FScanpy with real test data for complete PRF site prediction analysis, including:\n",
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"\n",
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"## 🎯 Complete Workflow\n",
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"1. **Load Test Data** - Use built-in real test data\n",
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"2. **FScanR Analysis** - Identify potential PRF sites from BLASTX results\n",
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"3. **Sequence Extraction** - Extract sequences around PRF sites\n",
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"4. **FScanpy Prediction** - Use machine learning models to predict probabilities\n",
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"5. **Results Visualization** - Generate prediction result plots using built-in plotting functions\n",
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"6. **Sequence-level Prediction Demo** - Sliding window analysis of complete sequences\n",
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"\n",
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"## 📊 Data Description\n",
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"- **blastx_example.xlsx**: Real BLASTX alignment results\n",
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"- **mrna_example.fasta**: Real mRNA sequence data\n",
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"- **region_example.csv**: Sample for individual site prediction"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {
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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"## 📚 FScanpy Function Usage Guide\n",
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"\n",
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"### Core Functions Overview\n",
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"\n",
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"FScanpy provides several main functions for PRF prediction:\n",
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"\n",
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"#### 1. `predict_prf()` - Universal Prediction Function\n",
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"```python\n",
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"# Single sequence prediction\n",
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"results = predict_prf(sequence=\"ATGCGT...\", window_size=3, ensemble_weight=0.4)\n",
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"\n",
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"# Multiple sequences prediction \n",
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"results = predict_prf(sequence=[\"seq1\", \"seq2\"], window_size=3)\n",
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"\n",
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"# DataFrame region prediction\n",
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"results = predict_prf(data=df_with_399bp_column, ensemble_weight=0.4)\n",
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"```\n",
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"\n",
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"#### 2. `plot_prf_prediction()` - Prediction with Visualization\n",
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"```python\n",
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"# Basic plotting\n",
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"results, fig = plot_prf_prediction(sequence=\"ATGCGT...\")\n",
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"\n",
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"# Custom parameters\n",
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"results, fig = plot_prf_prediction(\n",
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" sequence=\"ATGCGT...\",\n",
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" window_size=1,\n",
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" short_threshold=0.65,\n",
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" long_threshold=0.8,\n",
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" ensemble_weight=0.4,\n",
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" save_path=\"plot.png\"\n",
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")\n",
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"```\n",
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"\n",
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"#### 3. `PRFPredictor` Class Methods\n",
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"```python\n",
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"predictor = PRFPredictor()\n",
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"\n",
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"# Sliding window prediction\n",
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"results = predictor.predict_sequence(sequence, window_size=3, ensemble_weight=0.4)\n",
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"\n",
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"# Region prediction\n",
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"results = predictor.predict_regions(sequences_399bp, ensemble_weight=0.4)\n",
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"\n",
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"# Single position prediction\n",
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"result = predictor.predict_single_position(fs_period_33bp, full_seq_399bp)\n",
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"\n",
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"# Plot prediction\n",
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"results, fig = predictor.plot_sequence_prediction(sequence)\n",
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"```\n",
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"\n",
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"#### 4. Utility Functions\n",
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"```python\n",
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"from FScanpy.utils import fscanr, extract_prf_regions\n",
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"\n",
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"# Detect PRF sites from BLASTX\n",
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"prf_sites = fscanr(blastx_df, mismatch_cutoff=10, evalue_cutoff=1e-5)\n",
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"\n",
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"# Extract sequences around PRF sites\n",
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"prf_sequences = extract_prf_regions(mrna_file, prf_sites)\n",
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"```\n",
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"\n",
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"### Parameter Guidelines\n",
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"\n",
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"- **ensemble_weight**: 0.4 (default, balanced), 0.2-0.3 (conservative), 0.7-0.8 (sensitive)\n",
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"- **window_size**: 1 (detailed), 3 (standard), 6-9 (fast)\n",
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"- **short_threshold**: 0.1 (default), 0.2-0.3 (stricter filtering)\n",
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"- **Display thresholds**: 0.3-0.8 for visualization filtering\n"
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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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"## 📦 Environment Setup and Data Loading"
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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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"ename": "ImportError",
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"evalue": "cannot import name 'PRFPredictor' from 'FScanpy' (unknown location)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[3], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Import FScanpy related modules\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mFScanpy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PRFPredictor, predict_prf, plot_prf_prediction\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mFScanpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_test_data_path, list_test_data\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mFScanpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fscanr, extract_prf_regions\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'PRFPredictor' from 'FScanpy' (unknown location)"
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]
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}
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],
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"source": [
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"# Import necessary libraries\n",
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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\n",
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"\n",
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"print(\"✅ Environment setup complete!\")\n",
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"print(\"📋 Available test data:\")\n",
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"list_test_data()"
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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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"## 1. Load and Explore Test Data\n",
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"\n",
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"First, load the real test data provided by FScanpy to understand the data structure."
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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": 23,
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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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"📁 数据文件路径:\n",
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" BLASTX数据: /mnt/lmpbe/guest01/FScanpy-package-main/FScanpy/data/test_data/blastx_example.xlsx\n",
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" mRNA序列: /mnt/lmpbe/guest01/FScanpy-package-main/FScanpy/data/test_data/mrna_example.fasta\n",
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" 验证区域: /mnt/lmpbe/guest01/FScanpy-package-main/FScanpy/data/test_data/region_example.csv\n",
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"\n",
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"🧬 BLASTX数据概览:\n",
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" 数据形状: (1000, 14)\n",
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" 列名: ['DNA_seqid', 'Pep_seqid', 'pident', 'length', 'mismatch', 'gapopen', 'qstart', 'qend', 'sstart', 'send', 'evalue', 'bitscore', 'qframe', 'sframe']\n",
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" 唯一序列数: 704\n",
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"\n",
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"📊 BLASTX数据示例:\n",
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" DNA_seqid Pep_seqid pident length evalue qframe\n",
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"0 MSTRG.9998.1 CAMPEP_0196994412 68.27 104 1.000000e-33 2\n",
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"1 MSTRG.9996.1 CAMPEP_0197017426 49.16 297 3.000000e-79 2\n",
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"2 MSTRG.9994.1 CAMPEP_0197009206 98.31 354 0.000000e+00 2\n",
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"3 MSTRG.9993.1 CAMPEP_0168331218 51.67 60 2.000000e-37 2\n",
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"4 MSTRG.9993.1 CAMPEP_0168331218 45.45 88 2.000000e-37 3\n"
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]
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}
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],
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"source": [
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"# Get test data paths\n",
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"blastx_file = get_test_data_path('blastx_example.xlsx')\n",
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"mrna_file = get_test_data_path('mrna_example.fasta')\n",
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"region_file = get_test_data_path('region_example.csv')\n",
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"\n",
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"print(f\"📁 Data file paths:\")\n",
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"print(f\" BLASTX data: {blastx_file}\")\n",
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"print(f\" mRNA sequences: {mrna_file}\")\n",
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"print(f\" Validation regions: {region_file}\")\n",
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"\n",
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"# Load BLASTX data\n",
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"blastx_data = pd.read_excel(blastx_file)\n",
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"print(f\"\\n🧬 BLASTX data overview:\")\n",
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"print(f\" Data shape: {blastx_data.shape}\")\n",
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"print(f\" Column names: {list(blastx_data.columns)}\")\n",
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"print(f\" Unique sequences: {blastx_data['DNA_seqid'].nunique()}\")\n",
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"\n",
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"# Display first few rows\n",
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"print(\"\\n📊 BLASTX data examples:\")\n",
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"display_cols = ['DNA_seqid', 'Pep_seqid', 'pident', 'length', 'evalue', 'qframe']\n",
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"print(blastx_data[display_cols].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": 40,
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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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"🎯 验证区域数据概览:\n",
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" 数据形状: (3, 8)\n",
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" 列名: ['FS_period', '399bp', 'fs_position', 'DNA_seqid', 'label', 'source', 'FS_type', 'dataset']\n",
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" 数据来源: {'EUPLOTES': 3}\n",
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"\n",
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"📋 验证区域数据示例:\n",
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" fs_position DNA_seqid label source FS_type\n",
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"0 16.0 MSTRG.18491.1 0 EUPLOTES negative\n",
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"1 16.0 MSTRG.4662.1 0 EUPLOTES negative\n",
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"2 16.0 MSTRG.14742.1 0 EUPLOTES negative\n",
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"\n",
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"📈 标签分布:\n",
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"label\n",
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"0 3\n",
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"Name: count, dtype: int64\n",
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"\n",
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"🔬 FS类型分布:\n",
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"FS_type\n",
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"negative 3\n",
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"Name: count, dtype: int64\n"
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]
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}
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],
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"source": [
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"# Load validation region data\n",
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"region_data = pd.read_csv(region_file)\n",
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"print(f\"🎯 Validation region data overview:\")\n",
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"print(f\" Data shape: {region_data.shape}\")\n",
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"print(f\" Column names: {list(region_data.columns)}\")\n",
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"print(f\" Data sources: {region_data['source'].value_counts().to_dict()}\")\n",
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"\n",
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"print(\"\\n📋 Validation region data examples:\")\n",
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"display_cols = ['fs_position', 'DNA_seqid', 'label', 'source', 'FS_type']\n",
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"print(region_data[display_cols].head())\n",
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"\n",
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"# Statistical analysis\n",
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"print(f\"\\n📈 Label distribution:\")\n",
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"print(region_data['label'].value_counts())\n",
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"print(f\"\\n🔬 FS type distribution:\")\n",
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"print(region_data['FS_type'].value_counts())"
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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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"## 2. FScanR Analysis - Identify Potential PRF Sites from BLASTX\n",
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"\n",
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"Use the FScanR algorithm to analyze BLASTX results and identify potential programmed ribosomal frameshift sites."
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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": 41,
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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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"🔍 运行FScanR分析...\n",
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"参数设置: mismatch_cutoff=10, evalue_cutoff=1e-5, frameDist_cutoff=10\n",
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"\n",
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"✅ FScanR分析完成！\n",
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"检测到的潜在PRF位点数量: 24\n",
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"\n",
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"📊 FScanR结果概览:\n",
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" 列名: ['DNA_seqid', 'FS_start', 'FS_end', 'Pep_seqid', 'Pep_FS_start', 'Pep_FS_end', 'FS_type', 'Strand']\n",
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" 涉及的序列数: 16\n",
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" 链方向分布: {'+': 16, '-': 8}\n",
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" FS类型分布: {1: 16, -1: 7, -2: 1}\n",
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"\n",
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"🎯 FScanR结果示例:\n",
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" DNA_seqid FS_start FS_end Pep_seqid Pep_FS_start \\\n",
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"0 MSTRG.9380.1 3797 3802 CAMPEP_0197017206 1137 \n",
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"1 MSTRG.9431.1 4136 4192 CAMPEP_0197016790 657 \n",
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"3 MSTRG.9432.1 848 904 CAMPEP_0197016790 753 \n",
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"4 MSTRG.9582.1 302 304 CAMPEP_0197003180 214 \n",
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"5 MSTRG.961.1 1536 1533 CAMPEP_0197017908 590 \n",
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"\n",
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" Pep_FS_end FS_type Strand \n",
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"0 1138 1 + \n",
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"1 675 1 + \n",
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"3 2 1 - \n",
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"4 214 1 + \n",
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"5 19 -1 - \n"
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]
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}
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],
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"source": [
|
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"# Run FScanR analysis\n",
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"print(\"🔍 Running FScanR analysis...\")\n",
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"print(\"Parameter settings: mismatch_cutoff=10, evalue_cutoff=1e-5, frameDist_cutoff=10\")\n",
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"\n",
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"fscanr_results = fscanr(\n",
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" blastx_data,\n",
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" mismatch_cutoff=10,\n",
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" evalue_cutoff=1e-5,\n",
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" frameDist_cutoff=10\n",
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")\n",
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"\n",
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"print(f\"\\n✅ FScanR analysis complete!\")\n",
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"print(f\"Number of potential PRF sites detected: {len(fscanr_results)}\")\n",
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"\n",
|
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"if len(fscanr_results) > 0:\n",
|
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" print(f\"\\n📊 FScanR results overview:\")\n",
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" print(f\" Column names: {list(fscanr_results.columns)}\")\n",
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" print(f\" Number of sequences involved: {fscanr_results['DNA_seqid'].nunique()}\")\n",
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" print(f\" Strand orientation distribution: {fscanr_results['Strand'].value_counts().to_dict()}\")\n",
|
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" print(f\" FS type distribution: {fscanr_results['FS_type'].value_counts().to_dict()}\")\n",
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" \n",
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" print(\"\\n🎯 FScanR results examples:\")\n",
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" print(fscanr_results.head())\n",
|
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"else:\n",
|
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" print(\"⚠️ No PRF sites detected, may need to adjust parameters\")"
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]
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},
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{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
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||
"source": [
|
||
"## 3. Sequence Extraction - Extract Sequences Around PRF Sites\n",
|
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"\n",
|
||
"Extract sequence fragments around PRF sites identified by FScanR from mRNA sequences."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"📝 从mRNA序列中提取PRF位点周围序列...\n",
|
||
"\n",
|
||
"✅ 序列提取完成！\n",
|
||
"成功提取的序列数量: 24\n",
|
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"\n",
|
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"📏 序列长度验证:\n",
|
||
" 399bp序列长度分布: {399: 24}\n",
|
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" 平均长度: 399.0\n",
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"\n",
|
||
"🧬 提取序列示例:\n",
|
||
"序列 1: MSTRG.9380.1\n",
|
||
" FS位置: 3797-3802\n",
|
||
" 链方向: +\n",
|
||
" FS类型: 1\n",
|
||
" 序列片段: AAGGAGTTTGAAGAAGAACAGGAAAAACAAGAGAAAGAGAGAAAGGAGAA...NNNNNNNNNNNNNNNNNNNN\n",
|
||
"\n",
|
||
"序列 2: MSTRG.9431.1\n",
|
||
" FS位置: 4136-4192\n",
|
||
" 链方向: +\n",
|
||
" FS类型: 1\n",
|
||
" 序列片段: CAAGTATCTGAGTGGGAGGGAGACACAGGTGTTGATCAAACCCCATTCCC...ATAATGACGGAGGCTTCAGA\n",
|
||
"\n",
|
||
"序列 3: MSTRG.9432.1\n",
|
||
" FS位置: 848-904\n",
|
||
" 链方向: -\n",
|
||
" FS类型: 1\n",
|
||
" 序列片段: AGAAAGGATGGTACTGAAAATCAACGAAGTACTTTCACATTTTAGAAAGA...GCTGAGAACGATATTGACAA\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Extract sequences around PRF sites\n",
|
||
"if len(fscanr_results) > 0:\n",
|
||
" print(\"📝 Extracting sequences around PRF sites from mRNA sequences...\")\n",
|
||
" \n",
|
||
" prf_sequences = extract_prf_regions(\n",
|
||
" mrna_file=mrna_file,\n",
|
||
" prf_data=fscanr_results\n",
|
||
" )\n",
|
||
" \n",
|
||
" print(f\"\\n✅ Sequence extraction complete!\")\n",
|
||
" print(f\"Number of successfully extracted sequences: {len(prf_sequences)}\")\n",
|
||
" \n",
|
||
" if len(prf_sequences) > 0:\n",
|
||
" print(f\"\\n📏 Sequence length validation:\")\n",
|
||
" seq_lengths = prf_sequences['399bp'].str.len()\n",
|
||
" print(f\" 399bp sequence length distribution: {seq_lengths.value_counts().to_dict()}\")\n",
|
||
" print(f\" Average length: {seq_lengths.mean():.1f}\")\n",
|
||
" \n",
|
||
" print(\"\\n🧬 Extracted sequence examples:\")\n",
|
||
" for i, row in prf_sequences.head(3).iterrows():\n",
|
||
" print(f\"Sequence {i+1}: {row['DNA_seqid']}\")\n",
|
||
" print(f\" FS position: {row['FS_start']}-{row['FS_end']}\")\n",
|
||
" print(f\" Strand orientation: {row['Strand']}\")\n",
|
||
" print(f\" FS type: {row['FS_type']}\")\n",
|
||
" print(f\" Sequence fragment: {row['399bp'][:50]}...{row['399bp'][-20:]}\")\n",
|
||
" print()\n",
|
||
" else:\n",
|
||
" print(\"❌ Sequence extraction failed\")\n",
|
||
"else:\n",
|
||
" print(\"⚠️ Skipping sequence extraction - no FScanR results\")\n",
|
||
" prf_sequences = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4. FScanpy Prediction - Machine Learning Model Analysis\n",
|
||
"\n",
|
||
"Use FScanpy's machine learning models to predict PRF probabilities for the extracted sequences."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"🤖 FScanpy预测器初始化完成\n",
|
||
"\n",
|
||
"🎯 对 24 个FScanR识别的序列进行预测...\n",
|
||
"\n",
|
||
"📊 FScanR+FScanpy预测结果:\n",
|
||
" DNA_seqid FS_start FS_type Short_Probability Long_Probability \\\n",
|
||
"0 MSTRG.9380.1 3797 1 0.239192 0.087024 \n",
|
||
"1 MSTRG.9431.1 4136 1 0.326807 0.356356 \n",
|
||
"2 MSTRG.9432.1 848 1 0.310908 0.159746 \n",
|
||
"3 MSTRG.9582.1 302 1 0.272451 0.223354 \n",
|
||
"4 MSTRG.961.1 1536 -1 0.263269 0.046773 \n",
|
||
"\n",
|
||
" Ensemble_Probability \n",
|
||
"0 0.147891 \n",
|
||
"1 0.344536 \n",
|
||
"2 0.220211 \n",
|
||
"3 0.242993 \n",
|
||
"4 0.133372 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Initialize predictor\n",
|
||
"predictor = PRFPredictor()\n",
|
||
"print(\"🤖 FScanpy predictor initialization complete\")\n",
|
||
"\n",
|
||
"# Predict FScanR identified sequences\n",
|
||
"if len(prf_sequences) > 0:\n",
|
||
" print(f\"\\n🎯 Predicting {len(prf_sequences)} sequences identified by FScanR...\")\n",
|
||
" \n",
|
||
" fscanr_predictions = predictor.predict_regions(\n",
|
||
" sequences=prf_sequences['399bp'],\n",
|
||
" ensemble_weight=0.4 # Balanced configuration\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Merge results\n",
|
||
" fscanr_predictions = pd.concat([\n",
|
||
" prf_sequences.reset_index(drop=True),\n",
|
||
" fscanr_predictions.reset_index(drop=True)\n",
|
||
" ], axis=1)\n",
|
||
" \n",
|
||
" print(\"\\n📊 FScanR+FScanpy prediction results:\")\n",
|
||
" result_cols = ['DNA_seqid', 'FS_start', 'FS_type', 'Short_Probability', 'Long_Probability', 'Ensemble_Probability']\n",
|
||
" print(fscanr_predictions[result_cols].head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"🧪 对 3 个验证区域进行预测...\n",
|
||
"\n",
|
||
"📊 验证区域预测结果:\n",
|
||
" DNA_seqid label source Short_Probability Long_Probability \\\n",
|
||
"0 MSTRG.18491.1 0 EUPLOTES 0.368610 0.144442 \n",
|
||
"1 MSTRG.4662.1 0 EUPLOTES 0.229811 0.053352 \n",
|
||
"2 MSTRG.14742.1 0 EUPLOTES 0.454152 0.345118 \n",
|
||
"\n",
|
||
" Ensemble_Probability \n",
|
||
"0 0.234109 \n",
|
||
"1 0.123936 \n",
|
||
"2 0.388732 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Predict validation region data\n",
|
||
"print(f\"\\n🧪 Predicting {len(region_data)} validation regions...\")\n",
|
||
"\n",
|
||
"validation_predictions = predict_prf(\n",
|
||
" data=region_data.rename(columns={'399bp': 'Long_Sequence'}),\n",
|
||
" ensemble_weight=0.4\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\n📊 Validation region prediction results:\")\n",
|
||
"result_cols = ['DNA_seqid', 'label', 'source', 'Short_Probability', 'Long_Probability', 'Ensemble_Probability']\n",
|
||
"print(validation_predictions[result_cols].head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5. Sequence-level Prediction and Visualization\n",
|
||
"\n",
|
||
"Select a specific mRNA sequence and use the built-in plot_prf_prediction function for complete sliding window prediction and visualization."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"🧬 选择演示序列: MSTRG.9127.1\n",
|
||
"序列长度: 256 bp\n",
|
||
"序列前100bp: TGGCCTTCTTACTTGGAAGTCCCCAAGGATCATCTTGGCCATCCTTGCTTTCTTCATGGCTAGATTCTACCTCCTCCCATAATTGTGTGAAACAAGTAAC...\n",
|
||
"\n",
|
||
"🎯 使用plot_prf_prediction进行序列预测和可视化...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/mnt/lmpbe/guest01/FScanpy-package-main/FScanpy/predictor.py:335: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
|
||
" plt.tight_layout()\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 27010 (\\N{CJK UNIFIED IDEOGRAPH-6982}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 28909 (\\N{CJK UNIFIED IDEOGRAPH-70ED}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22270 (\\N{CJK UNIFIED IDEOGRAPH-56FE}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 31227 (\\N{CJK UNIFIED IDEOGRAPH-79FB}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30721 (\\N{CJK UNIFIED IDEOGRAPH-7801}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24067 (\\N{CJK UNIFIED IDEOGRAPH-5E03}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 38598 (\\N{CJK UNIFIED IDEOGRAPH-96C6}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25104 (\\N{CJK UNIFIED IDEOGRAPH-6210}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26435 (\\N{CJK UNIFIED IDEOGRAPH-6743}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37325 (\\N{CJK UNIFIED IDEOGRAPH-91CD}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20301 (\\N{CJK UNIFIED IDEOGRAPH-4F4D}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32622 (\\N{CJK UNIFIED IDEOGRAPH-7F6E}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 36807 (\\N{CJK UNIFIED IDEOGRAPH-8FC7}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 28388 (\\N{CJK UNIFIED IDEOGRAPH-6EE4}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 38408 (\\N{CJK UNIFIED IDEOGRAPH-9608}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32467 (\\N{CJK UNIFIED IDEOGRAPH-7ED3}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26524 (\\N{CJK UNIFIED IDEOGRAPH-679C}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26465 (\\N{CJK UNIFIED IDEOGRAPH-6761}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24418 (\\N{CJK UNIFIED IDEOGRAPH-5F62}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n",
|
||
"/home/guest01/.conda/envs/tf200/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from font(s) Liberation Sans.\n",
|
||
" fig.canvas.print_figure(bytes_io, **kw)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1600x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"📊 序列预测结果统计:\n",
|
||
" 预测位点总数: 85\n",
|
||
" 高概率位点 (>0.8): 0\n",
|
||
" 中概率位点 (0.4-0.8): 6\n",
|
||
" 最高预测概率: 0.475\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Select a sequence for demonstration\n",
|
||
"from Bio import SeqIO\n",
|
||
"\n",
|
||
"# Read the first mRNA sequence for demonstration\n",
|
||
"mrna_sequences = list(SeqIO.parse(mrna_file, \"fasta\"))\n",
|
||
"demo_seq = mrna_sequences[0] # Select the first sequence\n",
|
||
"\n",
|
||
"print(f\"🧬 Selected demonstration sequence: {demo_seq.id}\")\n",
|
||
"print(f\"Sequence length: {len(demo_seq.seq)} bp\")\n",
|
||
"print(f\"First 100bp of sequence: {str(demo_seq.seq)[:100]}...\")\n",
|
||
"\n",
|
||
"# Use built-in plot_prf_prediction function for prediction and visualization\n",
|
||
"print(f\"\\n🎯 Using plot_prf_prediction for sequence prediction and visualization...\")\n",
|
||
"\n",
|
||
"sequence_results, fig = plot_prf_prediction(\n",
|
||
" sequence=str(demo_seq.seq),\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 {demo_seq.id} (Bar Chart + Heatmap)\",\n",
|
||
" figsize=(16, 8),\n",
|
||
" dpi=150\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(f\"\\n📊 Sequence prediction result statistics:\")\n",
|
||
"print(f\" Total predicted sites: {len(sequence_results)}\")\n",
|
||
"print(f\" High probability sites (>0.8): {(sequence_results['Ensemble_Probability'] > 0.8).sum()}\")\n",
|
||
"print(f\" Medium probability sites (0.4-0.8): {((sequence_results['Ensemble_Probability'] >= 0.4) & (sequence_results['Ensemble_Probability'] <= 0.8)).sum()}\")\n",
|
||
"print(f\" Highest prediction probability: {sequence_results['Ensemble_Probability'].max():.3f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"🔝 Top 5 预测位点:\n",
|
||
" 1. 位置 96: \n",
|
||
" - Short概率: 0.288\n",
|
||
" - Long概率: 0.755\n",
|
||
" - 集成概率: 0.475\n",
|
||
" - 密码子: TAA\n",
|
||
" 2. 位置 12: \n",
|
||
" - Short概率: 0.606\n",
|
||
" - Long概率: 0.177\n",
|
||
" - 集成概率: 0.434\n",
|
||
" - 密码子: TTG\n",
|
||
" 3. 位置 15: \n",
|
||
" - Short概率: 0.493\n",
|
||
" - Long概率: 0.329\n",
|
||
" - 集成概率: 0.428\n",
|
||
" - 密码子: GAA\n",
|
||
" 4. 位置 18: \n",
|
||
" - Short概率: 0.369\n",
|
||
" - Long概率: 0.510\n",
|
||
" - 集成概率: 0.426\n",
|
||
" - 密码子: GTC\n",
|
||
" 5. 位置 105: \n",
|
||
" - Short概率: 0.248\n",
|
||
" - Long概率: 0.671\n",
|
||
" - 集成概率: 0.418\n",
|
||
" - 密码子: ACT\n",
|
||
"\n",
|
||
"📊 可视化分析完成！\n",
|
||
"图表包含热图和条形图，展示了整个序列的PRF预测概率分布。\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Print top predicted site probabilities\n",
|
||
"if sequence_results['Ensemble_Probability'].max() > 0.3:\n",
|
||
" top_predictions = sequence_results.nlargest(5, 'Ensemble_Probability')\n",
|
||
" print(f\"\\n🔝 Top 5 predicted sites:\")\n",
|
||
" for i, (_, row) in enumerate(top_predictions.iterrows(), 1):\n",
|
||
" print(f\" {i}. Position {row['Position']}: \")\n",
|
||
" print(f\" - Short probability: {row['Short_Probability']:.3f}\")\n",
|
||
" print(f\" - Long probability: {row['Long_Probability']:.3f}\")\n",
|
||
" print(f\" - Ensemble probability: {row['Ensemble_Probability']:.3f}\")\n",
|
||
" print(f\" - Codon: {row['Codon']}\")\n",
|
||
"else:\n",
|
||
" print(\"\\n💡 No high-probability PRF sites detected in this sequence\")\n",
|
||
"\n",
|
||
"print(\"\\n📊 Visualization analysis complete!\")\n",
|
||
"print(\"The chart contains heatmaps and bar charts showing the PRF prediction probability distribution across the entire sequence.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "raw",
|
||
"metadata": {
|
||
"vscode": {
|
||
"languageId": "raw"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 📖 Complete Function Reference\n",
|
||
"\n",
|
||
"### All Available Functions and Methods\n",
|
||
"\n",
|
||
"#### Core Prediction Functions\n",
|
||
"\n",
|
||
"**1. `predict_prf(sequence=None, data=None, window_size=3, short_threshold=0.1, ensemble_weight=0.4, model_dir=None)`**\n",
|
||
"- **Purpose**: Universal prediction function for both sliding window and region-based analysis\n",
|
||
"- **Input modes**: \n",
|
||
" - Single/multiple sequences → sliding window prediction\n",
|
||
" - DataFrame with 'Long_Sequence'/'399bp' column → region prediction\n",
|
||
"- **Key parameters**:\n",
|
||
" - `ensemble_weight`: Short model weight (0.0-1.0, default: 0.4)\n",
|
||
" - `window_size`: Scanning step size (default: 3)\n",
|
||
" - `short_threshold`: Filtering threshold (default: 0.1)\n",
|
||
"\n",
|
||
"**2. `plot_prf_prediction(sequence, window_size=3, short_threshold=0.65, long_threshold=0.8, ensemble_weight=0.4, title=None, save_path=None, figsize=(12,8), dpi=300)`**\n",
|
||
"- **Purpose**: Prediction with built-in visualization (3-subplot layout: FS site heatmap, prediction heatmap, bar chart)\n",
|
||
"- **Returns**: (prediction_results_df, matplotlib_figure)\n",
|
||
"- **Visualization features**: \n",
|
||
" - Black bars with alpha=0.6\n",
|
||
" - 'Reds' colormap for heatmaps\n",
|
||
" - Height ratios [0.1, 0.1, 1] for subplots\n",
|
||
"\n",
|
||
"#### PRFPredictor Class Methods\n",
|
||
"\n",
|
||
"**3. Class initialization: `PRFPredictor(model_dir=None)`**\n",
|
||
"- Loads HistGradientBoosting (short, 33bp) and BiLSTM-CNN (long, 399bp) models\n",
|
||
"- Uses ensemble weighting for final predictions\n",
|
||
"\n",
|
||
"**4. `predictor.predict_sequence(sequence, window_size=3, short_threshold=0.1, ensemble_weight=0.4)`**\n",
|
||
"- **Purpose**: Sliding window analysis of complete sequences\n",
|
||
"- **Process**: Scans sequence with specified window size, applies both models\n",
|
||
"\n",
|
||
"**5. `predictor.predict_regions(sequences, short_threshold=0.1, ensemble_weight=0.4)`**\n",
|
||
"- **Purpose**: Batch prediction for pre-defined 399bp regions\n",
|
||
"- **Input**: List/Series of 399bp sequences\n",
|
||
"- **Efficient**: Direct region analysis without sliding window\n",
|
||
"\n",
|
||
"**6. `predictor.predict_single_position(fs_period, full_seq, short_threshold=0.1, ensemble_weight=0.4)`**\n",
|
||
"- **Purpose**: Single position analysis\n",
|
||
"- **Inputs**: 33bp sequence (fs_period) + 399bp sequence (full_seq)\n",
|
||
"- **Returns**: Dictionary with individual and ensemble probabilities\n",
|
||
"\n",
|
||
"**7. `predictor.plot_sequence_prediction(...)`** \n",
|
||
"- **Purpose**: Class method version of plot_prf_prediction()\n",
|
||
"- **Same parameters** as standalone function\n",
|
||
"\n",
|
||
"#### Utility Functions\n",
|
||
"\n",
|
||
"**8. `fscanr(blastx_output, mismatch_cutoff=10, evalue_cutoff=1e-5, frameDist_cutoff=10)`**\n",
|
||
"- **Purpose**: Detect PRF sites from BLASTX alignment results\n",
|
||
"- **Input**: DataFrame with BLASTX columns (qseqid, sseqid, pident, length, mismatch, gapopen, qstart, qend, sstart, send, evalue, bitscore, qframe, sframe)\n",
|
||
"- **Output**: PRF sites with FS_start, FS_end, FS_type, Strand information\n",
|
||
"\n",
|
||
"**9. `extract_prf_regions(mrna_file, prf_data)`**\n",
|
||
"- **Purpose**: Extract 399bp sequences around detected PRF sites\n",
|
||
"- **Inputs**: FASTA file path + FScanR results DataFrame\n",
|
||
"- **Handles**: Strand orientation (reverse complement for '-' strand)\n",
|
||
"\n",
|
||
"#### Data Access Functions\n",
|
||
"\n",
|
||
"**10. `get_test_data_path(filename)`**\n",
|
||
"- **Purpose**: Get path to built-in test data files\n",
|
||
"- **Available files**: 'blastx_example.xlsx', 'mrna_example.fasta', 'region_example.csv'\n",
|
||
"\n",
|
||
"**11. `list_test_data()`**\n",
|
||
"- **Purpose**: Display all available test data files\n",
|
||
"\n",
|
||
"### Usage Pattern Examples\n",
|
||
"\n",
|
||
"#### Pattern 1: Quick Single Sequence Analysis\n",
|
||
"```python\n",
|
||
"from FScanpy import predict_prf, plot_prf_prediction\n",
|
||
"\n",
|
||
"# Simple prediction\n",
|
||
"results = predict_prf(sequence=\"ATGCGT...\")\n",
|
||
"\n",
|
||
"# With visualization \n",
|
||
"results, fig = plot_prf_prediction(sequence=\"ATGCGT...\")\n",
|
||
"```\n",
|
||
"\n",
|
||
"#### Pattern 2: Batch Sequence Analysis\n",
|
||
"```python\n",
|
||
"sequences = [\"seq1\", \"seq2\", \"seq3\"]\n",
|
||
"results = predict_prf(sequence=sequences, ensemble_weight=0.5)\n",
|
||
"```\n",
|
||
"\n",
|
||
"#### Pattern 3: BLASTX Pipeline\n",
|
||
"```python\n",
|
||
"from FScanpy.utils import fscanr, extract_prf_regions\n",
|
||
"\n",
|
||
"# Step 1: Detect PRF sites\n",
|
||
"prf_sites = fscanr(blastx_df)\n",
|
||
"\n",
|
||
"# Step 2: Extract sequences\n",
|
||
"prf_sequences = extract_prf_regions(fasta_file, prf_sites)\n",
|
||
"\n",
|
||
"# Step 3: Predict probabilities\n",
|
||
"results = predict_prf(data=prf_sequences)\n",
|
||
"```\n",
|
||
"\n",
|
||
"#### Pattern 4: Custom Analysis with PRFPredictor\n",
|
||
"```python\n",
|
||
"from FScanpy import PRFPredictor\n",
|
||
"\n",
|
||
"predictor = PRFPredictor()\n",
|
||
"\n",
|
||
"# Method chaining for different analysis types\n",
|
||
"seq_results = predictor.predict_sequence(sequence)\n",
|
||
"region_results = predictor.predict_regions(sequences_399bp)\n",
|
||
"single_result = predictor.predict_single_position(seq_33bp, seq_399bp)\n",
|
||
"```\n",
|
||
"\n",
|
||
"### Parameter Optimization Guide\n",
|
||
"\n",
|
||
"**Ensemble Weight Selection:**\n",
|
||
"- `0.2-0.3`: Conservative (high specificity, favor long model)\n",
|
||
"- `0.4-0.6`: Balanced (recommended default)\n",
|
||
"- `0.7-0.8`: Sensitive (high sensitivity, favor short model)\n",
|
||
"\n",
|
||
"**Window Size Selection:**\n",
|
||
"- `1`: High resolution, every position (slow but detailed)\n",
|
||
"- `3`: Standard resolution (balanced speed/detail) \n",
|
||
"- `6-9`: Low resolution, faster analysis\n",
|
||
"\n",
|
||
"**Threshold Guidelines:**\n",
|
||
"- `short_threshold`: 0.1-0.3 (controls efficiency by filtering low-probability candidates)\n",
|
||
"- Display thresholds: 0.3-0.8 (controls visualization, higher = cleaner plots)\n",
|
||
"- Classification threshold: 0.5 (standard binary classification cutoff)\n",
|
||
"\n",
|
||
"### Output Interpretation\n",
|
||
"\n",
|
||
"**Main Result Columns:**\n",
|
||
"- `Short_Probability`: HistGradientBoosting model prediction (0-1)\n",
|
||
"- `Long_Probability`: BiLSTM-CNN model prediction (0-1)\n",
|
||
"- `Ensemble_Probability`: **Final prediction** (weighted combination)\n",
|
||
"- `Position`: Sequence position (sliding window mode)\n",
|
||
"- `Codon`: Codon at position (sliding window mode)\n",
|
||
"\n",
|
||
"**Ensemble Probability Interpretation:**\n",
|
||
"- `> 0.8`: High confidence PRF site\n",
|
||
"- `0.5-0.8`: Moderate confidence PRF site \n",
|
||
"- `0.3-0.5`: Low confidence, worth investigating\n",
|
||
"- `< 0.3`: Unlikely to be PRF site\n",
|
||
"\n",
|
||
"### Best Practices\n",
|
||
"\n",
|
||
"1. **For exploration**: Use `window_size=1, ensemble_weight=0.4`\n",
|
||
"2. **For screening**: Use `window_size=3, ensemble_weight=0.4, short_threshold=0.2`\n",
|
||
"3. **For validation**: Use region-based prediction with known sequences\n",
|
||
"4. **For visualization**: Adjust `short_threshold` and `long_threshold` in plotting functions to control display density\n",
|
||
"\n",
|
||
"This demo covers all major FScanpy functionalities. For detailed parameter descriptions and advanced usage, please refer to the complete tutorial documentation.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 📝 Analysis Summary\n",
|
||
"\n",
|
||
"### 🎯 Key Findings\n",
|
||
"1. **Data Quality**: Test dataset contains real BLASTX alignment results and validation regions\n",
|
||
"2. **FScanR Performance**: Successfully identified potential PRF sites from BLASTX results\n",
|
||
"3. **Model Performance**: Short and Long models each have advantages in different scenarios\n",
|
||
"4. **Prediction Results**: Ensemble model provides more stable prediction performance\n",
|
||
"5. **Visualization**: Built-in plotting functions generate clear heatmaps and bar charts\n",
|
||
"\n",
|
||
"### 🔧 Best Practices\n",
|
||
"- **Data Preprocessing**: Ensure BLASTX results are in correct format\n",
|
||
"- **Parameter Settings**: Use default ensemble weights (0.4:0.6) for balanced performance\n",
|
||
"- **Result Interpretation**: When using FScanpy for whole sequence prediction, don't use 0.5 as threshold, but compare relative probabilities across positions\n",
|
||
"- **Visualization**: Use plot_prf_prediction function to generate standardized plots\n",
|
||
"\n",
|
||
"### 📚 Usage Recommendations\n",
|
||
"1. **Threshold Selection**: Adjust probability thresholds based on application scenarios\n",
|
||
"2. **Result Validation**: Validate prediction results with biological knowledge\n",
|
||
"3. **Performance Optimization**: Use reasonable sliding window sizes for large-scale data\n",
|
||
"4. **Visualization Parameters**: Adjust figsize and dpi for optimal display"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "tf200",
|
||
"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": "3.9.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|