{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras import backend as K\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "import aparent.visualization as vis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Load APARENT model</h2>\n",
    "<br/>\n",
    "-- Load APARENT, which has been trained to predict the isoform abundance and cut profile of a proximal PAS given a fixed background distal PAS (trained on random 3' UTR APA MPRA data).<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/johli/anaconda3/envs/aparent/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /home/johli/anaconda3/envs/aparent/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/johli/anaconda3/envs/aparent/lib/python3.6/site-packages/keras/engine/saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "#Load base APARENT model\n",
    "\n",
    "save_dir = os.path.join(os.getcwd(), '../saved_models')\n",
    "model_name = 'aparent_large_lessdropout_all_libs_no_sampleweights.h5'\n",
    "model_path = os.path.join(save_dir, model_name)\n",
    "\n",
    "aparent_model = load_model(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Example: PSMC6 Gene variant prediction</h2>\n",
    "<br/>\n",
    "-- Predict the isoform log fold change w.r.t the wildtype PAS for every possible SNV).<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Example PAS sequence from APADB (gene = PSMC6)\n",
    "\n",
    "#PAS Sequence\n",
    "seq = 'AGATAGTGGTATAAGAAAGCATTTCTTATGACTTATTTTGTATCATTTGTTTTCCTCATCTAAAAAGTTGAATAAAATCTGTTTGATTCAGTTCTCCTACATATATATTCTTGTCTTTTCTGAGTATATTTACTGTGGTCCTTTAGGTTCTTTAGCAAGTAAACTATTTGATAACCCAGATGGATTGTGGATTTTTGAATATTAT'\n",
    "\n",
    "#Predict mutation map (single-nucleotide variants)\n",
    "vis.mut_map(\n",
    "    aparent_model,\n",
    "    seq,\n",
    "    [(100, 'T', 'red')],\n",
    "    seq_start=70-45,\n",
    "    seq_end=70+6+45,\n",
    "    isoform_start=80,\n",
    "    isoform_end=110,\n",
    "    figsize=(14, 6),\n",
    "    height_ratios=[4, 1, 1.5],\n",
    "    logodds_clip=1.0\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:aparent]",
   "language": "python",
   "name": "conda-env-aparent-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}