examples/borzoi_example_ipaqtl_chr10_116664061_G_A.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7dbf2734",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-06 10:14:14.263859: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-10-06 10:14:14.263912: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-10-06 10:14:14.264958: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-10-06 10:14:14.271837: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-10-06 10:14:15.998526: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pysam\n",
    "import pyfaidx\n",
    "import tensorflow as tf\n",
    "\n",
    "from baskerville import seqnn\n",
    "from baskerville import gene as bgene\n",
    "from baskerville import dna\n",
    "\n",
    "from borzoi_helpers import *\n",
    "\n",
    "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
    "#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8911e01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f3c0 model already exists.\n",
      "f3c1 model already exists.\n",
      "f3c2 model already exists.\n",
      "f3c3 model already exists.\n",
      "Gene annotation already exists.\n",
      "Gene annotation (no read-through, protein-coding) already exists.\n",
      "Gene annotation (protein-coding) already exists.\n",
      "TSS annotation already exists.\n",
      "Splice site annotation already exist.\n",
      "Splice site annotation already exist.\n",
      "PolyA site annotation already exist.\n",
      "Human genome FASTA already exists.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "#Download model weights (data fold 3, 4 replicates)\n",
    "for rep in f3c0,f0 f3c1,f1 f3c2,f2 f3c3,f3; do IFS=\",\"; set -- $rep; \n",
    "  mkdir -p \"saved_models/$1/train\"\n",
    "  local_model=\"saved_models/$1/train/model0_best.h5\"\n",
    "  if [ -f \"$local_model\" ]; then\n",
    "    echo \"$1 model already exists.\"\n",
    "  else\n",
    "    wget --progress=bar:force \"https://storage.googleapis.com/seqnn-share/borzoi/$2/model0_best.h5\" -O \"$local_model\"\n",
    "  fi\n",
    "done\n",
    "\n",
    "#Download and uncompress annotation files\n",
    "mkdir -p hg38/genes/gencode41\n",
    "mkdir -p hg38/genes/polyadb\n",
    "\n",
    "if [ -f hg38/genes/gencode41/gencode41_basic_nort.gtf ]; then\n",
    "  echo \"Gene annotation already exists.\"\n",
    "else\n",
    "  wget -O - https://storage.googleapis.com/seqnn-share/helper/gencode41_basic_nort.gtf.gz | gunzip -c > hg38/genes/gencode41/gencode41_basic_nort.gtf\n",
    "fi\n",
    "\n",
    "if [ -f hg38/genes/gencode41/gencode41_basic_nort_protein.gtf ]; then\n",
    "  echo \"Gene annotation (no read-through, protein-coding) already exists.\"\n",
    "else\n",
    "  wget -O - https://storage.googleapis.com/seqnn-share/helper/gencode41_basic_nort_protein.gtf.gz | gunzip -c > hg38/genes/gencode41/gencode41_basic_nort_protein.gtf\n",
    "fi\n",
    "\n",
    "if [ -f hg38/genes/gencode41/gencode41_basic_protein.gtf ]; then\n",
    "  echo \"Gene annotation (protein-coding) already exists.\"\n",
    "else\n",
    "  wget -O - https://storage.googleapis.com/seqnn-share/helper/gencode41_basic_protein.gtf.gz | gunzip -c > hg38/genes/gencode41/gencode41_basic_protein.gtf\n",
    "fi\n",
    "\n",
    "if [ -f hg38/genes/gencode41/gencode41_basic_tss2.bed ]; then\n",
    "  echo \"TSS annotation already exists.\"\n",
    "else\n",
    "  wget -O - https://storage.googleapis.com/seqnn-share/helper/gencode41_basic_tss2.bed.gz | gunzip -c > hg38/genes/gencode41/gencode41_basic_tss2.bed\n",
    "fi\n",
    "\n",
    "if [ -f hg38/genes/gencode41/gencode41_basic_protein_splice.csv.gz ]; then\n",
    "  echo \"Splice site annotation already exist.\"\n",
    "else\n",
    "  wget https://storage.googleapis.com/seqnn-share/helper/gencode41_basic_protein_splice.csv.gz -O hg38/genes/gencode41/gencode41_basic_protein_splice.csv.gz\n",
    "fi\n",
    "\n",
    "if [ -f hg38/genes/gencode41/gencode41_basic_protein_splice.gff ]; then\n",
    "  echo \"Splice site annotation already exist.\"\n",
    "else\n",
    "  wget -O - https://storage.googleapis.com/seqnn-share/helper/gencode41_basic_protein_splice.gff.gz | gunzip -c > hg38/genes/gencode41/gencode41_basic_protein_splice.gff\n",
    "fi\n",
    "\n",
    "if [ -f hg38/genes/polyadb/polyadb_human_v3.csv.gz ]; then\n",
    "  echo \"PolyA site annotation already exist.\"\n",
    "else\n",
    "  wget https://storage.googleapis.com/seqnn-share/helper/polyadb_human_v3.csv.gz -O hg38/genes/polyadb/polyadb_human_v3.csv.gz\n",
    "fi\n",
    "\n",
    "#Download and index hg38 genome\n",
    "mkdir -p hg38/assembly/ucsc\n",
    "\n",
    "if [ -f hg38/assembly/ucsc/hg38.fa ]; then\n",
    "  echo \"Human genome FASTA already exists.\"\n",
    "else\n",
    "  wget -O - http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz | gunzip -c > hg38/assembly/ucsc/hg38.fa\n",
    "fi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5c8db8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Faidx(\"hg38/assembly/ucsc/hg38.fa\")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyfaidx.Faidx('hg38/assembly/ucsc/hg38.fa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91245dce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-06 10:14:18.639467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10520 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1\n"
     ]
    }
   ],
   "source": [
    "#Model configuration\n",
    "\n",
    "params_file = 'params_pred.json'\n",
    "targets_file = 'targets_gtex.txt' #Subset of targets_human.txt\n",
    "\n",
    "seq_len = 524288\n",
    "n_reps = 1       #To use only one model replicate, set to 'n_reps = 1'. To use all four replicates, set 'n_reps = 4'.\n",
    "rc = True         #Average across reverse-complement prediction\n",
    "\n",
    "#Read model parameters\n",
    "\n",
    "with open(params_file) as params_open :\n",
    "    \n",
    "    params = json.load(params_open)\n",
    "    \n",
    "    params_model = params['model']\n",
    "    params_train = params['train']\n",
    "\n",
    "#Read targets\n",
    "\n",
    "targets_df = pd.read_csv(targets_file, index_col=0, sep='\\t')\n",
    "target_index = targets_df.index\n",
    "\n",
    "#Create local index of strand_pair (relative to sliced targets)\n",
    "if rc :\n",
    "    strand_pair = targets_df.strand_pair\n",
    "    \n",
    "    target_slice_dict = {ix : i for i, ix in enumerate(target_index.values.tolist())}\n",
    "    slice_pair = np.array([\n",
    "        target_slice_dict[ix] if ix in target_slice_dict else ix for ix in strand_pair.values.tolist()\n",
    "    ], dtype='int32')\n",
    "\n",
    "#Initialize model ensemble\n",
    "\n",
    "models = []\n",
    "for rep_ix in range(n_reps) :\n",
    "    \n",
    "    model_file = \"saved_models/f3c\" + str(rep_ix) + \"/train/model0_best.h5\"\n",
    "\n",
    "    seqnn_model = seqnn.SeqNN(params_model)\n",
    "    seqnn_model.restore(model_file, 0)\n",
    "    seqnn_model.build_slice(target_index)\n",
    "    if rc :\n",
    "        seqnn_model.strand_pair.append(slice_pair)\n",
    "    seqnn_model.build_ensemble(rc, [0])\n",
    "    \n",
    "    models.append(seqnn_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8e09390",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(splice_df) = 404837\n"
     ]
    }
   ],
   "source": [
    "#Initialize fasta sequence extractor\n",
    "\n",
    "fasta_open = pysam.Fastafile('hg38/assembly/ucsc/hg38.fa')\n",
    "\n",
    "#Load splice site annotation\n",
    "\n",
    "splice_df = pd.read_csv('hg38/genes/gencode41/gencode41_basic_protein_splice.csv.gz', sep='\\t', compression='gzip')\n",
    "\n",
    "print(\"len(splice_df) = \" + str(len(splice_df)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bea48e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thyroid tracks = [81, 82, 83]\n",
      "nerve tracks = [50, 51]\n"
     ]
    }
   ],
   "source": [
    "#Print index of GTEx thyroid and nerve tracks in targets file\n",
    "\n",
    "targets_df['local_index'] = np.arange(len(targets_df))\n",
    "\n",
    "print(\"thyroid tracks = \" + str(targets_df.loc[targets_df['description'] == 'RNA:thyroid']['local_index'].tolist()))\n",
    "print(\"nerve tracks = \" + str(targets_df.loc[targets_df['description'] == 'RNA:nerve']['local_index'].tolist()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3bd4e6c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Counts --\n",
      " - max_y_wt = 291.0626\n",
      " - max_y_mut = 332.1575\n",
      " -- (max_y = 332.1575)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - max_y_wt = 472.8082\n",
      " - max_y_mut = 551.0536\n",
      " -- (max_y = 551.0536)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - max_y_wt = 227.878\n",
      " - max_y_mut = 265.866\n",
      " -- (max_y = 265.866)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.26 s, sys: 313 ms, total: 5.57 s\n",
      "Wall time: 8.55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Predict for chr10_116664061_G_A\n",
    "# (~6 minutes on CPU w 1 replicate; ~15 seconds on GPU)\n",
    "\n",
    "save_figs = False\n",
    "save_suffix = '_chr10_116664061_G_A'\n",
    "\n",
    "center_pos = 116664061\n",
    "\n",
    "chrom = 'chr10'\n",
    "poses = [116664061]\n",
    "alts = ['A']\n",
    "\n",
    "start = center_pos - seq_len // 2\n",
    "end = center_pos + seq_len // 2\n",
    "\n",
    "sequence_one_hot_wt = process_sequence(fasta_open, chrom, start, end)\n",
    "\n",
    "#Induce mutation(s)\n",
    "sequence_one_hot_mut = np.copy(sequence_one_hot_wt)\n",
    "\n",
    "for pos, alt in zip(poses, alts) :\n",
    "    alt_ix = -1\n",
    "    if alt == 'A' :\n",
    "        alt_ix = 0\n",
    "    elif alt == 'C' :\n",
    "        alt_ix = 1\n",
    "    elif alt == 'G' :\n",
    "        alt_ix = 2\n",
    "    elif alt == 'T' :\n",
    "        alt_ix = 3\n",
    "\n",
    "    sequence_one_hot_mut[pos-start-1] = 0.\n",
    "    sequence_one_hot_mut[pos-start-1, alt_ix] = 1.\n",
    "\n",
    "#Make predictions\n",
    "y_wt = predict_tracks(models, sequence_one_hot_wt)\n",
    "y_mut = predict_tracks(models, sequence_one_hot_mut)\n",
    "\n",
    "#Visualize quantized tracks over SNP\n",
    "\n",
    "plot_window = 4096 * 2\n",
    "bin_size = 32\n",
    "pad = 16\n",
    "\n",
    "untransform_old = True\n",
    "normalize_counts = False\n",
    "\n",
    "anno_df = splice_df\n",
    "\n",
    "#Tracks\n",
    "track_indices = [\n",
    "    np.arange(0, 89).tolist(),\n",
    "    [81, 82, 83],\n",
    "    [50, 51],\n",
    "]\n",
    "\n",
    "track_names = [\n",
    "    'GTEx Coverage (All tissues)',\n",
    "    'GTEx Coverage (Thyroid)',\n",
    "    'GTEx Coverage (Nerve)',\n",
    "]\n",
    "\n",
    "track_scales = [\n",
    "    0.01,\n",
    "    0.01,\n",
    "    0.01,\n",
    "]\n",
    "\n",
    "track_transforms = [\n",
    "    3./4.,\n",
    "    3./4.,\n",
    "    3./4.,\n",
    "]\n",
    "\n",
    "soft_clips = [\n",
    "    384.,\n",
    "    384.,\n",
    "    384.,\n",
    "]\n",
    "\n",
    "print(\"-- Counts --\")\n",
    "plot_coverage_track_pair_bins(y_wt, y_mut, chrom, start, center_pos, poses, track_indices, track_names, track_scales, track_transforms, soft_clips, plot_window=plot_window, normalize_window=8*plot_window, bin_size=bin_size, pad=pad, normalize_counts=normalize_counts, save_figs=save_figs, save_suffix=save_suffix, anno_df=anno_df, untransform_old=untransform_old)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3bb0e25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mut_bin = 8176\n",
      "\n",
      "splice_ix = 0: bin = 8175 (8173 - 8178)\n",
      "splice_ix = 1: bin = 8179 (8177 - 8182)\n",
      "splice_ix = 2: bin = 8179 (8177 - 8182)\n",
      "splice_ix = 3: bin = 8182 (8180 - 8185)\n",
      "splice_ix = 4: bin = 8197 (8195 - 8200)\n",
      "splice_ix = 5: bin = 8203 (8201 - 8206)\n",
      "splice_ix = 6: bin = 8224 (8222 - 8227)\n",
      "splice_ix = 7: bin = 8231 (8229 - 8234)\n"
     ]
    }
   ],
   "source": [
    "#Print splice site bin positions and other info\n",
    "\n",
    "plot_start = center_pos - plot_window // 2\n",
    "plot_end = center_pos + plot_window // 2\n",
    "\n",
    "plot_start_bin = (plot_start - start) // bin_size - pad\n",
    "plot_end_bin = (plot_end - start) // bin_size - pad\n",
    "\n",
    "mut_bin = (poses[0] - start) // bin_size - pad\n",
    "\n",
    "#Get splice site positions\n",
    "splice_poses = splice_df.query(\"chrom == '\" + chrom + \"' and position_hg38 >= \" + str(plot_start) + \" and position_hg38 < \" + str(plot_end))['position_hg38'].unique().tolist()\n",
    "\n",
    "print(\"mut_bin = \" + str(mut_bin))\n",
    "print(\"\")\n",
    "\n",
    "#Print splice site positions\n",
    "for splice_ix, splice_pos in enumerate(splice_poses) :\n",
    "    \n",
    "    splice_bin = int((splice_pos - start) // 32) - 16\n",
    "    \n",
    "    bin_end = splice_bin + 3\n",
    "    bin_start = bin_end - 5\n",
    "    \n",
    "    print(\"splice_ix = \" + str(splice_ix) + \": bin = \" + str(splice_bin) + \" (\" + str(bin_start) + \" - \" + str(bin_end) + \")\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a310c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choose splice sites to use for attributions\n",
    "\n",
    "exon_1_start_bin = 8173\n",
    "exon_1_end_bin = 8176\n",
    "\n",
    "exon_2_start_bin = 8176\n",
    "exon_2_end_bin = 8179\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12af2ad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 410s 410s/step\n",
      "1/1 [==============================] - 220s 220s/step\n",
      "1/1 [==============================] - 410s 410s/step\n",
      "1/1 [==============================] - 220s 220s/step\n",
      "CPU times: user 18min 44s, sys: 2min 44s, total: 21min 28s\n",
      "Wall time: 22min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Get contribution scores (gradient) for nerve GTEX tracks\n",
    "# (~20 minutes on CPU; otherwise runnable only on 40GB GPU cards, e.g. A100)\n",
    "\n",
    "_, _, [pred_grad_wt, pred_grad_mut] = get_prediction_gradient_w_rc(\n",
    "    models,\n",
    "    [sequence_one_hot_wt, sequence_one_hot_mut],\n",
    "    exon_2_start_bin,\n",
    "    exon_2_end_bin,\n",
    "    exon_1_start_bin,\n",
    "    exon_1_end_bin,\n",
    "    target_index[[50, 51]].tolist(),\n",
    "    0.01,\n",
    "    3./4.,\n",
    "    clip_soft=384.,\n",
    "    use_mean=False,\n",
    "    use_ratio=True,\n",
    "    use_logodds=False,\n",
    "    subtract_avg=True,\n",
    "    fold_index=np.arange(n_reps).tolist(),\n",
    "    untransform_old=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5fe23b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- WT ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Mut ---\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize gradient contribution scores (128 bp window centered on variant); shown in reverse-complement\n",
    "\n",
    "visualize_input_gradient_pair(\n",
    "    pred_grad_wt[::-1, ::-1], #'::-1' on pos and channel axes causes rev-comp\n",
    "    pred_grad_mut[::-1, ::-1],\n",
    "    plot_start=seq_len - (poses[0] - start) - 64 - 1,\n",
    "    plot_end=seq_len - (poses[0] - start) + 64 - 1,\n",
    "    save_figs=False,\n",
    "    fig_name=chrom + '_' + str(poses[0]) + '_prediction_grad_gtex_nerve'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "42b0e259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example_ix = 0\n",
      "example_ix = 1\n",
      "CPU times: user 15min 42s, sys: 28.5 s, total: 16min 11s\n",
      "Wall time: 34min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Get contribution scores (ISM) for nerve GTEX tracks\n",
    "# (not feasible to run on CPU; ~34 minutes on GPU)\n",
    "\n",
    "[pred_ism_wt, pred_ism_mut] = get_ism(\n",
    "    models,\n",
    "    [sequence_one_hot_wt, sequence_one_hot_mut],\n",
    "    (poses[0] - start) - 64,\n",
    "    (poses[0] - start) + 64,\n",
    "    exon_2_start_bin,\n",
    "    exon_2_end_bin,\n",
    "    exon_1_start_bin,\n",
    "    exon_1_end_bin,\n",
    "    [50, 51],\n",
    "    0.01,\n",
    "    3./4.,\n",
    "    384.,\n",
    "    use_mean=False,\n",
    "    use_ratio=True,\n",
    "    use_logodds=False,\n",
    "    untransform_old=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c77b8eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- WT ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Mut ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize ISM contribution scores (128 bp window centered on variant); shown in reverse-complement\n",
    "\n",
    "visualize_input_gradient_pair(\n",
    "    pred_ism_wt[::-1, ::-1],\n",
    "    pred_ism_mut[::-1, ::-1],\n",
    "    plot_start=seq_len - (poses[0] - start) - 64 - 1,\n",
    "    plot_end=seq_len - (poses[0] - start) + 64 - 1,\n",
    "    save_figs=False,\n",
    "    fig_name=chrom + '_' + str(poses[0]) + '_prediction_ism_gtex_nerve'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f6ad34d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example_ix = 0\n",
      "example_ix = 1\n",
      "CPU times: user 1h 8min 37s, sys: 2min 12s, total: 1h 10min 50s\n",
      "Wall time: 2h 16min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Get contribution scores (ISM shuffle) for nerve GTEX tracks\n",
    "# (not feasible to run on CPU; ~135 minutes on GPU)\n",
    "\n",
    "[pred_ism_wt, pred_ism_mut] = get_ism_shuffle(\n",
    "    models,\n",
    "    [sequence_one_hot_wt, sequence_one_hot_mut],\n",
    "    (poses[0] - start) - 64,\n",
    "    (poses[0] - start) + 64,\n",
    "    exon_2_start_bin,\n",
    "    exon_2_end_bin,\n",
    "    exon_1_start_bin,\n",
    "    exon_1_end_bin,\n",
    "    [50, 51],\n",
    "    0.01,\n",
    "    3./4.,\n",
    "    384.,\n",
    "    window_size=5,\n",
    "    n_samples=12,\n",
    "    use_mean=False,\n",
    "    use_ratio=True,\n",
    "    use_logodds=False,\n",
    "    untransform_old=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7329459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- WT ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Mut ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize ISM Shuffle contribution scores (128 bp window centered on variant); shown in reverse-complement\n",
    "\n",
    "visualize_input_gradient_pair(\n",
    "    pred_ism_wt[::-1, ::-1],\n",
    "    pred_ism_mut[::-1, ::-1],\n",
    "    plot_start=seq_len - (poses[0] - start) - 64 - 1,\n",
    "    plot_end=seq_len - (poses[0] - start) + 64 - 1,\n",
    "    save_figs=False,\n",
    "    fig_name=chrom + '_' + str(poses[0]) + '_prediction_ism_shuffle_gtex_nerve'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "35c74bd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choose new exons to use for attributions (isolate intronic polyadenylation determinant)\n",
    "\n",
    "pas_pos = 116664310\n",
    "\n",
    "exon_1_start_bin = 8170\n",
    "exon_1_end_bin = 8176\n",
    "\n",
    "exon_2_start_bin = 8197\n",
    "exon_2_end_bin = 8203\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bcca6669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 403s 403s/step\n",
      "1/1 [==============================] - 221s 221s/step\n",
      "1/1 [==============================] - 403s 403s/step\n",
      "1/1 [==============================] - 220s 220s/step\n",
      "CPU times: user 18min 23s, sys: 2min 51s, total: 21min 14s\n",
      "Wall time: 21min 57s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Get contribution scores (gradient) for nerve GTEX tracks\n",
    "# (~20 minutes on CPU; otherwise runnable only on 40GB GPU cards, e.g. A100)\n",
    "\n",
    "_, _, [pred_grad_wt, pred_grad_mut] = get_prediction_gradient_w_rc(\n",
    "    models,\n",
    "    [sequence_one_hot_wt, sequence_one_hot_mut],\n",
    "    exon_2_start_bin,\n",
    "    exon_2_end_bin,\n",
    "    exon_1_start_bin,\n",
    "    exon_1_end_bin,\n",
    "    target_index[[50, 51]].tolist(),\n",
    "    0.01,\n",
    "    3./4.,\n",
    "    clip_soft=384.,\n",
    "    use_mean=False,\n",
    "    use_ratio=True,\n",
    "    use_logodds=False,\n",
    "    subtract_avg=True,\n",
    "    fold_index=np.arange(n_reps).tolist(),\n",
    "    untransform_old=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af977487",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- WT ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Mut ---\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize gradient contribution scores (128 bp window centered on variant); shown in reverse-complement\n",
    "\n",
    "visualize_input_gradient_pair(\n",
    "    pred_grad_wt[::-1, ::-1],\n",
    "    pred_grad_mut[::-1, ::-1],\n",
    "    plot_start=seq_len - (pas_pos - start) - 64 - 1,\n",
    "    plot_end=seq_len - (pas_pos - start) + 64 - 1,\n",
    "    save_figs=False,\n",
    "    fig_name=chrom + '_' + str(poses[0]) + '_prediction_grad_gtex_nerve'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6f6f994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example_ix = 0\n",
      "example_ix = 1\n",
      "CPU times: user 17min 54s, sys: 31.4 s, total: 18min 26s\n",
      "Wall time: 33min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Get contribution scores (ISM) for nerve GTEX tracks\n",
    "# (not feasible to run on CPU; ~33 minutes on GPU)\n",
    "\n",
    "[pred_ism_wt, pred_ism_mut] = get_ism(\n",
    "    models,\n",
    "    [sequence_one_hot_wt, sequence_one_hot_mut],\n",
    "    (pas_pos - start) - 64,\n",
    "    (pas_pos - start) + 64,\n",
    "    exon_2_start_bin,\n",
    "    exon_2_end_bin,\n",
    "    exon_1_start_bin,\n",
    "    exon_1_end_bin,\n",
    "    [50, 51],\n",
    "    0.01,\n",
    "    3./4.,\n",
    "    384.,\n",
    "    use_mean=False,\n",
    "    use_ratio=True,\n",
    "    use_logodds=False,\n",
    "    untransform_old=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "18b1b848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- WT ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Mut ---\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize ISM contribution scores (128 bp window centered on variant); shown in reverse-complement\n",
    "\n",
    "visualize_input_gradient_pair(\n",
    "    pred_ism_wt[::-1, ::-1],\n",
    "    pred_ism_mut[::-1, ::-1],\n",
    "    plot_start=seq_len - (pas_pos - start) - 64 - 1,\n",
    "    plot_end=seq_len - (pas_pos - start) + 64 - 1,\n",
    "    save_figs=False,\n",
    "    fig_name=chrom + '_' + str(poses[0]) + '_prediction_ism_gtex_nerve'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fafb96e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example_ix = 0\n",
      "example_ix = 1\n",
      "CPU times: user 1h 11min 31s, sys: 2min 3s, total: 1h 13min 34s\n",
      "Wall time: 2h 15min 34s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Get contribution scores (ISM shuffle) for nerve GTEX tracks\n",
    "# (not feasible to run on CPU; ~135 minutes on GPU)\n",
    "\n",
    "[pred_ism_wt, pred_ism_mut] = get_ism_shuffle(\n",
    "    models,\n",
    "    [sequence_one_hot_wt, sequence_one_hot_mut],\n",
    "    (pas_pos - start) - 64,\n",
    "    (pas_pos - start) + 64,\n",
    "    exon_2_start_bin,\n",
    "    exon_2_end_bin,\n",
    "    exon_1_start_bin,\n",
    "    exon_1_end_bin,\n",
    "    [50, 51],\n",
    "    0.01,\n",
    "    3./4.,\n",
    "    384.,\n",
    "    window_size=5,\n",
    "    n_samples=12,\n",
    "    use_mean=False,\n",
    "    use_ratio=True,\n",
    "    use_logodds=False,\n",
    "    untransform_old=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a541a96f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- WT ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Mut ---\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize ISM Shuffle contribution scores (128 bp window centered on variant); shown in reverse-complement\n",
    "\n",
    "visualize_input_gradient_pair(\n",
    "    pred_ism_wt[::-1, ::-1],\n",
    "    pred_ism_mut[::-1, ::-1],\n",
    "    plot_start=seq_len - (pas_pos - start) - 64 - 1,\n",
    "    plot_end=seq_len - (pas_pos - start) + 64 - 1,\n",
    "    save_figs=False,\n",
    "    fig_name=chrom + '_' + str(poses[0]) + '_prediction_ism_shuffle_gtex_nerve'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78d47c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load samples with reference- and alternate alleles respectively\n",
    "\n",
    "#These files are protected by dbGaP - email to request access\n",
    "\n",
    "cov_files_wt = pd.read_csv(\"gtex_ref_chr10_116664061_G_A.txt\", sep='\\t', names=['file'])['file'].values.tolist()\n",
    "cov_files_mut = pd.read_csv(\"gtex_alt_chr10_116664061_G_A.txt\", sep='\\t', names=['file'])['file'].values.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9da3cbe5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gtex_targets_wt.shape = (16352, 31)\n",
      "gtex_targets_mut.shape = (16352, 31)\n",
      "-- Counts --\n",
      " - max_y_wt = 2566.1792\n",
      " - max_y_mut = 2891.292\n",
      " -- (max_y = 2891.292)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Read measured coverage tracks for chr10_116664061_G_A\n",
    "\n",
    "save_figs = False\n",
    "save_suffix = '_chr10_116664061_G_A_meas_16_subjects'\n",
    "\n",
    "center_pos = 116664061\n",
    "\n",
    "chrom = 'chr10'\n",
    "poses = [116664061]\n",
    "alts = ['A']\n",
    "\n",
    "start = center_pos - seq_len // 2\n",
    "end = center_pos + seq_len // 2\n",
    "\n",
    "blacklist_bed = \"hg38/blacklist/blacklist_hg38_all.bed\"\n",
    "\n",
    "read_coverage_func_wt, close_coverage_func_wt = get_coverage_reader(cov_files_wt, 16384, 16, blacklist_bed)\n",
    "read_coverage_func_mut, close_coverage_func_mut = get_coverage_reader(cov_files_mut, 16384, 16, blacklist_bed)\n",
    "\n",
    "gtex_targets_wt = read_coverage_func_wt(chrom, start, end, clip_soft=384., clip=768., scale=0.01, transform_old=True)\n",
    "gtex_targets_mut = read_coverage_func_mut(chrom, start, end, clip_soft=384., clip=768., scale=0.01, transform_old=True)\n",
    "\n",
    "close_coverage_func_wt()\n",
    "close_coverage_func_mut()\n",
    "\n",
    "print(\"gtex_targets_wt.shape = \" + str(gtex_targets_wt.shape))\n",
    "print(\"gtex_targets_mut.shape = \" + str(gtex_targets_mut.shape))\n",
    "\n",
    "plot_window = 4096 * 2\n",
    "bin_size = 32\n",
    "pad = 16\n",
    "\n",
    "untransform_old = True\n",
    "normalize_counts = True\n",
    "\n",
    "#Tracks\n",
    "track_indices = [\n",
    "    np.arange(16),\n",
    "]\n",
    "\n",
    "track_names = [\n",
    "    'GTEx Nerve measured (16 subjects)',\n",
    "]\n",
    "\n",
    "track_scales = [\n",
    "    0.01,\n",
    "]\n",
    "\n",
    "track_transforms = [\n",
    "    3./4.,\n",
    "]\n",
    "\n",
    "soft_clips = [\n",
    "    384.,\n",
    "]\n",
    "\n",
    "print(\"-- Counts --\")\n",
    "plot_coverage_track_pair_bins(\n",
    "    gtex_targets_wt[None, None, ...],\n",
    "    gtex_targets_mut[None, None, ...],\n",
    "    chrom,\n",
    "    start,\n",
    "    center_pos,\n",
    "    poses,\n",
    "    track_indices,\n",
    "    track_names,\n",
    "    track_scales,\n",
    "    track_transforms,\n",
    "    soft_clips,\n",
    "    plot_window=plot_window,\n",
    "    normalize_window=8*plot_window,\n",
    "    bin_size=bin_size,\n",
    "    pad=pad,\n",
    "    normalize_counts=normalize_counts,\n",
    "    save_figs=save_figs,\n",
    "    save_suffix=save_suffix,\n",
    "    anno_df=anno_df,\n",
    "    untransform_old=untransform_old,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7efea964",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}