{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decode gene regulation mechanism\n",
    "This tutorial is to show how to use scDiffusion-X to resolve regulatory relationships between genes and chromatin regions (peaks). First, you need to finishi the training process on the dataset you are interested in. The following codes will show you how to obtain the cell-type specific heterogeneous gene regulatory network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import the related packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/lightning_lite/__init__.py:29: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('lightning_lite')`.\n",
      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
      "  __import__(\"pkg_resources\").declare_namespace(__name__)\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/pytorch_lightning/__init__.py:45: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pytorch_lightning')`.\n",
      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
      "  __import__(\"pkg_resources\").declare_namespace(__name__)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '4'\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import muon as mu\n",
    "import yaml\n",
    "import os\n",
    "from scdiffusionX.utils import *\n",
    "\n",
    "from scdiffusionX.Autoencoder.data.scrnaseq_loader import RNAseqLoader\n",
    "from scdiffusionX.Autoencoder.models.base.encoder_model import EncoderModel\n",
    "\n",
    "import argparse\n",
    "from scdiffusionX.DiffusionBackbone.multimodal_script_util import (\n",
    "    model_and_diffusion_defaults,\n",
    "    create_model_and_diffusion,\n",
    "    add_dict_to_argparser,\n",
    "    args_to_dict\n",
    ")\n",
    "from scdiffusionX.DiffusionBackbone import dist_util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the trained model, you can calculate the attention map of the DCA. Load data, autoencoder and diffusion model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_config = \"script/training_autoencoder/configs/encoder/encoder_multimodal.yaml\"\n",
    "dataset_path = '/stor/lep/diffusion/multiome/openproblem_filtered.h5mu'\n",
    "covariate_keys = \"cell_type\" \n",
    "num_class = 22\n",
    "ae_path = \"/stor/lep/workspace/multi_diffusion/CFGen/project_folder/experiments/train_autoencoder_openproblem_multimodal/checkpoints/last.ckpt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13850, 13431)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata = mu.read_h5mu(dataset_path)\n",
    "real_cell = mdata['rna'][::5].X.toarray()\n",
    "real_cell2 = mdata['atac'][::5].X.toarray()\n",
    "# real_cell = mdata['rna'][mdata['rna'].obs['cell_type']=='CD4+ T activated'].X.toarray()[::7]\n",
    "# real_cell2 = mdata['atac'][mdata['atac'].obs['cell_type']=='CD4+ T activated'].X.toarray()[::7]\n",
    "real_cell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load autoencoder\n",
    "with open(encoder_config, 'r') as file:\n",
    "    yaml_content = file.read()\n",
    "autoencoder_args = yaml.safe_load(yaml_content)\n",
    "\n",
    "# Initialize encoder\n",
    "encoder_model = EncoderModel(in_dim={'atac': real_cell2.shape[1], 'rna': real_cell.shape[1]},\n",
    "                                    n_cat=mdata['rna'].obs[covariate_keys].unique().shape[0],\n",
    "                                    conditioning_covariate=covariate_keys, \n",
    "                                    encoder_type='learnt_autoencoder',\n",
    "                                    **autoencoder_args)\n",
    "\n",
    "# Load weights \n",
    "encoder_model.load_state_dict(torch.load(ae_path)[\"state_dict\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set diffusion backbone config\n",
    "defaults = dict(\n",
    "    clip_denoised=True,\n",
    "    batch_size=16,\n",
    "    sample_fn=\"ddim\",\n",
    "    multimodal_model_path=\"/stor/lep/workspace/multi_diffusion/MM-Diffusion/outputs/checkpoints_cross/open_lr1e4_w512_scale124_drop0_80w_rescale10_3crossatt64_condi/model800000.pt\",\n",
    "    output_dir=\"test\",\n",
    "    classifier_scale=0,\n",
    "    devices='0',\n",
    "    is_strict=True,\n",
    "    all_save_num= 1024,\n",
    "    seed=42,\n",
    "    load_noise=\"\",\n",
    "    data_dir=dataset_path,\n",
    "    condition='cell_type',\n",
    ")\n",
    "defaults.update(model_and_diffusion_defaults())\n",
    "parser = argparse.ArgumentParser()\n",
    "defaults['rna_dim'] = '1,100'\n",
    "defaults['atac_dim'] = '1,100'\n",
    "defaults['num_channels'] = 128\n",
    "defaults['num_res_blocks'] = 1\n",
    "defaults['resblock_updown'] = True\n",
    "defaults['num_class'] = 22\n",
    "defaults['class_cond'] = True\n",
    "add_dict_to_argparser(parser, defaults)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "creating model and diffusion...\n"
     ]
    }
   ],
   "source": [
    "# load diffusion backbone\n",
    "args = parser.parse_known_args()[0]\n",
    "args.rna_dim = [int(i) for i in args.rna_dim.split(',')]\n",
    "args.atac_dim = [int(i) for i in args.atac_dim.split(',')]\n",
    "\n",
    "dist_util.setup_dist(args.devices)\n",
    "\n",
    "print(\"creating model and diffusion...\")\n",
    "multimodal_model, multimodal_diffusion = create_model_and_diffusion(\n",
    "        **args_to_dict(args, [key for key in model_and_diffusion_defaults().keys()])\n",
    ")\n",
    "multimodal_model.load_state_dict_(\n",
    "        dist_util.load_state_dict(args.multimodal_model_path, map_location=\"cpu\"), is_strict=args.is_strict\n",
    ")\n",
    "multimodal_model.to(dist_util.dev())\n",
    "optimizer2 = optim.Adam(multimodal_model.parameters(), lr=0.001)\n",
    "\n",
    "# mdata = mu.read_h5mu(args.data_dir)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labels = mdata['rna'].obs[args.condition].values\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(labels)\n",
    "classes_all = label_encoder.transform(labels)\n",
    "\n",
    "rna = mdata['rna']#[mdata['rna'].obs['cell_type']=='Erythroblast']\n",
    "atac = mdata['atac']#[mdata['atac'].obs['cell_type']=='Erythroblast']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For different cell type, we have different cell-type specific attention maps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ordered type list\n",
    "type_list = np.array(['CD14+ Mono', 'ID2-hi myeloid prog', 'CD16+ Mono', 'cDC2',\n",
    "                       'pDC', 'HSC', 'G/M prog','Lymph prog','MK/E prog', 'Naive CD20+ B',\n",
    "                       'B1 B', 'Transitional B', 'Plasma cell','CD4+ T naive','CD4+ T activated',\n",
    "         'CD8+ T', 'CD8+ T naive','NK', 'ILC', 'Proerythroblast','Erythroblast','Normoblast', ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are three DCAs in the scDiffusion-X, we can calculate the attention map in all the three DCAs. However, ablation study showed that the information richness are highest in the seconde DCA module and the last time step. Therefore, the analysis for gene regulatory will be conduct in this attention map. This will provide a stable output, and you don't need additional experiments to select hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate attention maps in each layers\n",
    "time_step = 1\n",
    "down_sample = 2\n",
    "model_kwargs = {}\n",
    "batch = {}\n",
    "\n",
    "x1 = torch.tensor(rna[::down_sample].X.toarray(),requires_grad=True)\n",
    "x2 = torch.tensor(atac[::down_sample].X.toarray(),requires_grad=True)\n",
    "\n",
    "batch[\"X_norm\"] = {'rna':x1,'atac':x2}\n",
    "z = encoder_model.encode(batch)\n",
    "video_start = z['rna'].unsqueeze(1).to(dist_util.dev())\n",
    "audio_start = z['atac'].unsqueeze(1).to(dist_util.dev())\n",
    "model_kwargs[\"label\"] = torch.tensor(classes_all[::down_sample]).to(dist_util.dev())\n",
    "\n",
    "noise ={\"video\":torch.randn_like(video_start),\\\n",
    "    \"audio\":torch.randn_like(audio_start)}\n",
    "\n",
    "#0 means t_th step, 1 means the audio gives groundtruth, 2 means the video gives the groundtruth\n",
    "# condition_index = x_start[\"condition\"]  \n",
    "t = (torch.ones(video_start.shape[0], device=dist_util.dev())*time_step).to(dtype=torch.int)\n",
    "video_t = multimodal_diffusion.q_sample(video_start, t, noise = noise[\"video\"])#.detach()\n",
    "audio_t = multimodal_diffusion.q_sample(audio_start, t, noise = noise[\"audio\"])#.detach()\n",
    "\n",
    "att_layer1 = []\n",
    "att_layer1_atac = []\n",
    "att_layer2 = []\n",
    "att_layer2_atac = []\n",
    "att_layer3 = []\n",
    "att_layer3_atac = []\n",
    "for celltype in type_list:\n",
    "    index = (rna[::down_sample].obs['cell_type'] == celltype)\n",
    "    # sample_id = np.random.choice(np.arange(0, index.sum()), size=22, replace=False)\n",
    "    video_t_i = video_t[index][:10000]#[sample_id]\n",
    "    audio_t_i = audio_t[index][:10000]#[sample_id]\n",
    "    t_i = t[index][:10000]#[sample_id]\n",
    "    labels = model_kwargs[\"label\"][index][:10000]#[sample_id]\n",
    "\n",
    "    noise_pred_video, noise_pred_video, att_maps = multimodal_model(video_t_i,audio_t_i,t_i,labels,return_attvec=True)\n",
    "    att_layer1.append(att_maps[3])\n",
    "    att_layer2.append(att_maps[7])\n",
    "    att_layer3.append(att_maps[11])\n",
    "    att_layer1_atac.append(att_maps[2])\n",
    "    att_layer2_atac.append(att_maps[6])\n",
    "    att_layer3_atac.append(att_maps[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the attention maps, you can find the key positions in different cell type, for example, in the CD4+ T activated cell (number 14 in all cell type):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'gene')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fine the key elements in gene and peak. 14 is the target cell type index (CD4+ T activated here)\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.imshow(att_layer2[14].mean(0).cpu().detach().numpy()+att_layer2_atac[14].mean(0).cpu().detach().numpy().T,vmax=0.2,cmap='coolwarm')\n",
    "plt.ylabel('peak')\n",
    "plt.xlabel('gene')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These elements are later used to obtain the cell-type specific heterogeneous gene regulatory network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The position (x,y) of the top10 elements:\n",
      "x(peak): [ 50  50  81  79  50  45  28  50  57 107]\n",
      "y(gene): [ 11  47 100 100  38 100 100 126 100 100]\n",
      "Top 10 values: [0.22636247 0.2294471  0.23520574 0.23761243 0.23839217 0.2453197\n",
      " 0.24915317 0.25201848 0.25299454 0.2953034 ]\n"
     ]
    }
   ],
   "source": [
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "cross_map = att_layer2[14].mean(0).cpu().detach().numpy()+att_layer2_atac[14].mean(0).cpu().detach().numpy().T\n",
    "flattened_indices = np.argsort(cross_map, axis=None)[-10:]\n",
    "positions = np.unravel_index(flattened_indices, cross_map.shape)\n",
    "max_values = cross_map[positions]\n",
    "print(\"The position (x,y) of the top10 elements:\")\n",
    "print('x(peak):', positions[0])\n",
    "print('y(gene):', positions[1])\n",
    "print(\"Top 10 values:\", max_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, you can find which RNA elements are most concerned by peak. For example, in the second DCA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'what gene element to focus')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "key_col = np.zeros((len(att_layer2),att_layer2[0].shape[-1]))\n",
    "for i in range(len(att_layer2)):\n",
    "    for j in range(att_layer2[i].shape[0]):\n",
    "        att_mean = att_layer2[i][j].mean(0).detach().cpu()\n",
    "        key_c = np.where(att_mean>0.35)[0]\n",
    "        for c in key_c:\n",
    "            key_col[i,c] += 1\n",
    "    key_col[i] = key_col[i]/att_layer2[i].shape[0]\n",
    "\n",
    "plt.figure(figsize=(32,8))\n",
    "sns.heatmap(key_col, cmap='coolwarm', vmax=0.2)\n",
    "plt.yticks(ticks=np.arange(type_list.shape[0]), labels=type_list)\n",
    "plt.xticks(ticks=np.arange(0,key_col.shape[1],5), labels=np.arange(0,key_col.shape[1],5))\n",
    "plt.title('what RNA element to focus')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see apparent cell type specific pattern in this map. Similarly, you can also obtain the map about which ATAC elements are most concerned by gene:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'what peak elements to focus')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "key_col = np.zeros((len(att_layer2_atac),att_layer2_atac[0].shape[-1]))\n",
    "for i in range(len(att_layer2_atac)):\n",
    "    for j in range(att_layer2_atac[i].shape[0]):\n",
    "        att_mean = att_layer2_atac[i][j].mean(0).detach().cpu()\n",
    "        key_c = np.where(att_mean>0.35)[0]\n",
    "        for c in key_c:\n",
    "            key_col[i,c] += 1\n",
    "    key_col[i] = key_col[i]/att_layer2_atac[i].shape[0]\n",
    "\n",
    "plt.figure(figsize=(32,8))\n",
    "sns.heatmap(key_col, cmap='coolwarm', vmax=0.2)\n",
    "plt.yticks(ticks=np.arange(type_list.shape[0]), labels=type_list)\n",
    "plt.xticks(ticks=np.arange(0,key_col.shape[1],5), labels=np.arange(0,key_col.shape[1],5))\n",
    "plt.title('what ATAC elements to focus')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we show you how to use gradient back propagation approch to analysis these attention maps. For analysis the chromosomes and genes, you need to first import the reference gene data. Here we use the hg38 reference gene from the genecode. You can download it from: https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.annotation.gtf.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.read_csv('hg38/gencode_hg38.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use CD4+ T activated as an example to analysis the top related genes/peaks to the cell type specific elements and construct the cell-type specific heterogeneous gene regulatory network. First, prepare the inference data (the scRNA-seq and scATAC-seq of CD4+ T activated cell):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 1\n",
    "interested_type = type_list[14] \n",
    "down_sample = 5\n",
    "model_kwargs = {}\n",
    "batch = {}\n",
    "\n",
    "x1 = torch.tensor(rna[::down_sample].X.toarray(),requires_grad=True)\n",
    "x2 = torch.tensor(atac[::down_sample].X.toarray(),requires_grad=True)\n",
    "index = list(range(rna[::down_sample].shape[0])) if interested_type=='all' else (rna[::down_sample].obs['cell_type'] == interested_type)\n",
    "\n",
    "batch[\"X_norm\"] = {'rna':x1,'atac':x2}\n",
    "z = encoder_model.encode(batch)\n",
    "video_start = z['rna'].unsqueeze(1).to(dist_util.dev())\n",
    "audio_start = z['atac'].unsqueeze(1).to(dist_util.dev())\n",
    "model_kwargs[\"label\"] = torch.tensor(classes_all[::down_sample]).to(dist_util.dev())\n",
    "\n",
    "noise ={\"video\":torch.randn_like(video_start),\\\n",
    "    \"audio\":torch.randn_like(audio_start)}\n",
    "\n",
    "t = (torch.ones(video_start.shape[0], device=dist_util.dev())*time_step).to(dtype=torch.int)\n",
    "video_t = multimodal_diffusion.q_sample(video_start, t, noise = noise[\"video\"])#.detach()\n",
    "audio_t = multimodal_diffusion.q_sample(audio_start, t, noise = noise[\"audio\"])#.detach()\n",
    "\n",
    "video_t = video_t[index]\n",
    "audio_t = audio_t[index]\n",
    "t = t[index]\n",
    "labels = model_kwargs[\"label\"][index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you can use the gradient backpropagation approch to obtain the gene/peak related to the key elements we get in the previous section. \n",
    "**note**: the returned att_vec has the following content<br>\n",
    "att_vec[0]-att_vec[3] for first cross attention<br>\n",
    "att_vec[0] - rna feature vector ; att_vec[1] - atac feature vector <br>\n",
    "att_vec[2] - attention map of atac to rna (what peak to focus)<br>\n",
    "att_vec[3] - attention map of rna to atac (what gene to focus)<br>\n",
    "[4]-[7] for second cross attention, [8]-[11] for third cross attention\n",
    "Here we use the second cross attention as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/torch/autograd/__init__.py:251: UserWarning: Using backward() with create_graph=True will create a reference cycle between the parameter and its gradient which can cause a memory leak. We recommend using autograd.grad when creating the graph to avoid this. If you have to use this function, make sure to reset the .grad fields of your parameters to None after use to break the cycle and avoid the leak. (Triggered internally at ../torch/csrc/autograd/engine.cpp:1171.)\n",
      "  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n"
     ]
    }
   ],
   "source": [
    "all_key_genes = [] \n",
    "all_key_peaks = [] \n",
    "all_position = []\n",
    "\n",
    "for key_ele in np.unique(positions[1]):\n",
    "\n",
    "    batch[\"X_norm\"] = {'rna':x1,'atac':x2}\n",
    "    z = encoder_model.encode(batch)\n",
    "    video_start = z['rna'].unsqueeze(1).to(dist_util.dev())\n",
    "    audio_start = z['atac'].unsqueeze(1).to(dist_util.dev())\n",
    "    model_kwargs[\"label\"] = torch.tensor(classes_all[::down_sample]).to(dist_util.dev())\n",
    "    noise ={\"video\":torch.randn_like(video_start),\\\n",
    "        \"audio\":torch.randn_like(audio_start)}\n",
    "    t = (torch.ones(video_start.shape[0], device=dist_util.dev())*time_step).to(dtype=torch.int)\n",
    "    video_t = multimodal_diffusion.q_sample(video_start, t, noise = noise[\"video\"])\n",
    "    audio_t = multimodal_diffusion.q_sample(audio_start, t, noise = noise[\"audio\"])\n",
    "    video_t = video_t[index]\n",
    "    audio_t = audio_t[index]\n",
    "    t = t[index]\n",
    "    labels = model_kwargs[\"label\"][index]\n",
    "\n",
    "    noise_pred_video, noise_pred_audio, att_vec = multimodal_model(video_t,audio_t,t,labels,return_attvec=True)\n",
    "    att_vec[4][:,:,key_ele].mean().backward(create_graph=True,)\n",
    "    top_k = mdata['rna'].shape[1]\n",
    "    values, indices = torch.topk(abs(x1.grad[index]).mean(0)*(x1[index].sum(0)>int(x1[index].shape[0]*0.2)), top_k)#\n",
    "\n",
    "    top_gene = np.array([mdata['rna'].var_names[id] for id in np.array(indices)])\n",
    "    all_key_genes += list(top_gene[:100])\n",
    "\n",
    "    multimodal_model.zero_grad()\n",
    "    encoder_model.zero_grad()\n",
    "    x1.grad.zero_()\n",
    "    x2.grad.zero_()\n",
    "\n",
    "for key_ele in np.unique(positions[0]):\n",
    "\n",
    "    batch[\"X_norm\"] = {'rna':x1,'atac':x2}\n",
    "    z = encoder_model.encode(batch)\n",
    "    video_start = z['rna'].unsqueeze(1).to(dist_util.dev())\n",
    "    audio_start = z['atac'].unsqueeze(1).to(dist_util.dev())\n",
    "    model_kwargs[\"label\"] = torch.tensor(classes_all[::down_sample]).to(dist_util.dev())\n",
    "    noise ={\"video\":torch.randn_like(video_start),\\\n",
    "        \"audio\":torch.randn_like(audio_start)}\n",
    "    t = (torch.ones(video_start.shape[0], device=dist_util.dev())*time_step).to(dtype=torch.int)\n",
    "    video_t = multimodal_diffusion.q_sample(video_start, t, noise = noise[\"video\"])\n",
    "    audio_t = multimodal_diffusion.q_sample(audio_start, t, noise = noise[\"audio\"])\n",
    "    video_t = video_t[index]\n",
    "    audio_t = audio_t[index]\n",
    "    t = t[index]\n",
    "    labels = model_kwargs[\"label\"][index]\n",
    "\n",
    "    noise_pred_video, noise_pred_audio, att_vec = multimodal_model(video_t,audio_t,t,labels,return_attvec=True)\n",
    "    att_vec[5][:,:,key_ele].mean().backward(create_graph=True)\n",
    "    values2, indices2 = torch.topk(abs(x2.grad[index]).mean(0)*(x2[index].sum(0)>int(x2[index].shape[0]*0.05)), 36553)#\n",
    "    top_peak = np.array([mdata['atac'].var_names[id] for id in np.array(indices2)])\n",
    "    all_key_peaks += list(top_peak[:100])\n",
    "\n",
    "    multimodal_model.zero_grad()\n",
    "    encoder_model.zero_grad()\n",
    "    x1.grad.zero_()\n",
    "    x2.grad.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_ele_gene = {}\n",
    "key_ele_peak = {}\n",
    "for i in range(np.unique(positions[1]).shape[0]):\n",
    "    key_ele_gene[np.unique(positions[1])[i]]=all_key_genes[i*100:(i+1)*100]\n",
    "for i in range(np.unique(positions[0]).shape[0]):\n",
    "    key_ele_peak[np.unique(positions[0])[i]]=all_key_peaks[i*100:(i+1)*100]\n",
    "# np.savez('hete_net_14.npz', elements=positions, key_gene=np.array([key_ele_gene], dtype=object), key_peak=np.array([key_ele_peak], dtype=object))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each key RNA elements, we have the top related genes. For each key ATAC elements, we have the top related peaks. In this example, we have 5 distinct RNA elements and 7 distinct ATAC elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dict_keys([11, 38, 47, 100, 126]), dict_keys([28, 45, 50, 57, 79, 81, 107]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_ele_gene.keys(),key_ele_peak.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each RNA element, we obtain the top 100 related genes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAF\n",
      "MAST4\n",
      "LINC00299\n",
      "FAAH2\n",
      "AC139720.1\n",
      "ADAM19\n",
      "GZMK\n",
      "PBXIP1\n",
      "ARRDC3\n",
      "RGS1\n",
      "OST4\n",
      "CD226\n",
      "NOL4L\n",
      "HBP1\n",
      "AC022217.3\n",
      "STK17A\n",
      "LPXN\n",
      "A2M\n",
      "GALM\n",
      "TAGAP\n",
      "ZFYVE28\n",
      "ETNK1\n",
      "TRIR\n",
      "UQCRB\n",
      "MTRNR2L12\n",
      "GNLY\n",
      "ITGA6\n",
      "NELL2\n",
      "KLRB1\n",
      "LEPROTL1\n",
      "CD28\n",
      "CD5\n",
      "GPSM3\n",
      "TNFSF8\n",
      "AC013652.1\n",
      "TGFBR3\n",
      "TRAT1\n",
      "CSGALNACT1\n",
      "ZNF331\n",
      "DNAJB1\n",
      "EPHA4\n",
      "DDX3X\n",
      "MT-ND3\n",
      "FRY\n",
      "CFDP1\n",
      "ZNF217\n",
      "MYO1G\n",
      "SFMBT1\n",
      "SNRNP200\n",
      "TSHZ2\n",
      "EPSTI1\n",
      "RNPC3\n",
      "RNF166\n",
      "RANBP2\n",
      "DDX18\n",
      "PTPRM\n",
      "GATA3\n",
      "NSRP1\n",
      "CREM\n",
      "UBL5\n",
      "IFRD1\n",
      "H1FX\n",
      "CMSS1\n",
      "GPR155\n",
      "S100A8\n",
      "DGKA\n",
      "LRIG1\n",
      "PCSK7\n",
      "LBH\n",
      "CD2AP\n",
      "EIF4E3\n",
      "LAT\n",
      "GBP5\n",
      "LPIN2\n",
      "PRKCQ-AS1\n",
      "IGF2R\n",
      "TESPA1\n",
      "TIAL1\n",
      "GPR174\n",
      "PCMTD1\n",
      "TOB1\n",
      "CAPN2\n",
      "TRG-AS1\n",
      "ATP5MG\n",
      "RORA-AS1\n",
      "WWP2\n",
      "TRAF3IP3\n",
      "FAM53B\n",
      "GOLGA8A\n",
      "LINC-PINT\n",
      "FCMR\n",
      "NLRC3\n",
      "NOP58\n",
      "GIMAP4\n",
      "MAPRE2\n",
      "KLRG1\n",
      "COMMD6\n",
      "CHD3\n",
      "MT-ATP6\n",
      "EIF3M\n"
     ]
    }
   ],
   "source": [
    "# an example of the gene related to 47th RNA element\n",
    "for gene in key_ele_gene[47]:\n",
    "    print(gene)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each ATCA element, we obtain the top 100 related peaks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GZMA\n",
      "SMG6\n",
      "GGA3\n",
      "C4orf50\n",
      "DPP4\n",
      "ENSG00000309832\n",
      "SCAMP2\n",
      "SH3RF3\n",
      "ENSG00000308252\n",
      "ELAPOR2\n",
      "COPS8-DT\n",
      "STAM\n",
      "ENSG00000301728\n",
      "ENSG00000250602\n",
      "ENSG00000309950\n",
      "ITK\n",
      "CCDC7\n",
      "SLC7A6\n",
      "LINC02646\n",
      "ENSG00000298044\n",
      "ENSG00000259704\n",
      "ENSG00000285635\n",
      "ITGB1-DT\n",
      "DAB1\n",
      "PRKCB\n",
      "ENSG00000289377\n",
      "RNA5SP431\n",
      "TSPEAR\n",
      "ZDHHC11B\n",
      "CRACDL\n",
      "MED15\n",
      "ARHGAP15\n",
      "ENSG00000299620\n",
      "RN7SKP166\n",
      "NLRC5\n",
      "ENSG00000233538\n",
      "RUNX2\n",
      "NPM1P2\n",
      "DSTN\n",
      "NDFIP2-AS1\n",
      "CXCR5\n",
      "PVT1\n",
      "RNF216\n",
      "ADAM19\n",
      "SNX9\n",
      "ENSG00000300850\n",
      "SUPT3H\n",
      "ENSG00000291013\n",
      "HDAC4\n",
      "LRRC8C-DT\n",
      "SPOCK2\n",
      "ENSG00000299987\n",
      "PRDM2\n",
      "XYLT1\n",
      "CFAP299\n",
      "PECAM1\n",
      "ARHGAP10\n",
      "EMILIN2\n",
      "CNNM2\n",
      "LRRC8C\n",
      "BEGAIN\n",
      "LEF1\n",
      "CDK5RAP1\n",
      "EVI5\n",
      "DGKZ\n",
      "ENSG00000309104\n",
      "FIRRM\n",
      "EHD4\n",
      "OXNAD1\n",
      "ENSG00000288724\n",
      "CORO1B\n",
      "ARHGAP25\n",
      "TIAM1\n",
      "ADARB1\n",
      "ENSG00000225649\n",
      "BTF3L4P3\n",
      "PRKCA\n",
      "KCNJ1\n",
      "USP12\n",
      "SUSD4\n",
      "ENSG00000308933\n",
      "BEX5\n",
      "ENSG00000306277\n",
      "ITK\n",
      "TRAF1\n",
      "TRAF2\n",
      "Y_RNA\n",
      "ENSG00000288729\n",
      "IKZF1\n",
      "BPGM\n",
      "LINC01991\n",
      "ZBTB46\n",
      "FYB1\n",
      "NIBAN1\n",
      "TMEM106B\n",
      "ENSG00000300042\n",
      "ETS1\n",
      "EPAS1\n",
      "GATA3\n",
      "SLC25A26\n"
     ]
    }
   ],
   "source": [
    "# an example to fine the related peaks' corresponding (nearst) gene to 104th ATAC element\n",
    "for peak in key_ele_peak[104]:\n",
    "    print(find_nearest_gene(final_df, peak)[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also print all the related gene/peak for this specific cell type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A2M\n",
      "AC013652.1\n",
      "AC022217.3\n",
      "AC079793.1\n",
      "AC139720.1\n",
      "AC245297.3\n",
      "ADAM19\n",
      "AGFG1\n",
      "AL136456.1\n",
      "ANKRD36\n",
      "ANO6\n",
      "ANTXR2\n",
      "ANXA11\n",
      "ANXA6\n",
      "AP001011.1\n",
      "APBA2\n",
      "AQR\n",
      "ARHGAP45\n",
      "ARHGEF6\n",
      "ARHGEF7\n",
      "ARL4C\n",
      "ARRDC3\n",
      "ATF2\n",
      "ATF7IP2\n",
      "ATP10A\n",
      "ATP5IF1\n",
      "ATP5MG\n",
      "BRD1\n",
      "BRD7\n",
      "BTBD11\n",
      "C12orf57\n",
      "CAPN2\n",
      "CBFB\n",
      "CCL5\n",
      "CCND2\n",
      "CCSER2\n",
      "CD226\n",
      "CD28\n",
      "CD2AP\n",
      "CD37\n",
      "CD3D\n",
      "CD3E\n",
      "CD3G\n",
      "CD48\n",
      "CD5\n",
      "CD81\n",
      "CDC42SE1\n",
      "CDK5RAP2\n",
      "CDKN1B\n",
      "CDV3\n",
      "CFDP1\n",
      "CHD3\n",
      "CHURC1\n",
      "CLIC1\n",
      "CLIP1\n",
      "CMSS1\n",
      "CNOT1\n",
      "COMMD6\n",
      "COX6A1\n",
      "COX6B1\n",
      "CREM\n",
      "CRIP1\n",
      "CSGALNACT1\n",
      "CYTOR\n",
      "DAZAP2\n",
      "DDX10\n",
      "DDX18\n",
      "DDX3X\n",
      "DDX46\n",
      "DENND4C\n",
      "DGKA\n",
      "DIPK1A\n",
      "DNAJB1\n",
      "DUSP16\n",
      "EDF1\n",
      "EIF3M\n",
      "EIF4E3\n",
      "EIF5B\n",
      "ELP2\n",
      "EPHA4\n",
      "EPSTI1\n",
      "ERAP2\n",
      "ERP29\n",
      "ETNK1\n",
      "FAAH2\n",
      "FAM53B\n",
      "FARS2\n",
      "FCMR\n",
      "FLNA\n",
      "FMN1\n",
      "FRY\n",
      "GADD45B\n",
      "GALM\n",
      "GATA3\n",
      "GBP5\n",
      "GIMAP4\n",
      "GNLY\n",
      "GOLGA8A\n",
      "GOLGA8B\n",
      "GPR155\n",
      "GPR174\n",
      "GPR183\n",
      "GPSM3\n",
      "GRAP2\n",
      "GZMA\n",
      "GZMK\n",
      "H1FX\n",
      "HBP1\n",
      "HELB\n",
      "HINT1\n",
      "HIVEP1\n",
      "HMGN1\n",
      "IFRD1\n",
      "IGF2R\n",
      "IKZF2\n",
      "IL10RA\n",
      "IL2RG\n",
      "ITGA6\n",
      "IVNS1ABP\n",
      "KAT2B\n",
      "KLRB1\n",
      "KLRG1\n",
      "LAT\n",
      "LBH\n",
      "LDHB\n",
      "LEF1\n",
      "LEPROTL1\n",
      "LINC-PINT\n",
      "LINC00299\n",
      "LINC00513\n",
      "LINC00861\n",
      "LINC01138\n",
      "LINC02694\n",
      "LPIN2\n",
      "LPXN\n",
      "LRIG1\n",
      "LTB\n",
      "MAF\n",
      "MALT1\n",
      "MAPRE2\n",
      "MAST4\n",
      "MCL1\n",
      "MED23\n",
      "MT-ATP6\n",
      "MT-CO1\n",
      "MT-CO2\n",
      "MT-CYB\n",
      "MT-ND1\n",
      "MT-ND2\n",
      "MT-ND3\n",
      "MT-ND4\n",
      "MT-ND5\n",
      "MTAP\n",
      "MTFR1\n",
      "MTRNR2L12\n",
      "MYO1F\n",
      "MYO1G\n",
      "NABP1\n",
      "NCOA7\n",
      "NELL2\n",
      "NKG7\n",
      "NLRC3\n",
      "NME2\n",
      "NOL4L\n",
      "NOP58\n",
      "NR1D2\n",
      "NR2C2\n",
      "NSRP1\n",
      "NT5DC1\n",
      "ODF2L\n",
      "OPTN\n",
      "OST4\n",
      "P2RY8\n",
      "PARP14\n",
      "PBXIP1\n",
      "PCAT1\n",
      "PCMTD1\n",
      "PCNX2\n",
      "PCSK7\n",
      "PIK3CD\n",
      "PIK3IP1\n",
      "PLCG2\n",
      "PPP1R16B\n",
      "PPP1R2\n",
      "PPP2R2A\n",
      "PRDM1\n",
      "PRKCQ-AS1\n",
      "PSMB8\n",
      "PSMB9\n",
      "PSME1\n",
      "PSME2\n",
      "PTPN2\n",
      "PTPRM\n",
      "RANBP2\n",
      "RASGRF2\n",
      "RBM5\n",
      "RCAN3\n",
      "RELL1\n",
      "RETREG1\n",
      "RGS1\n",
      "RNF166\n",
      "RNMT\n",
      "RNPC3\n",
      "RORA-AS1\n",
      "S100A11\n",
      "S100A8\n",
      "SCML4\n",
      "SELENOF\n",
      "SELL\n",
      "SERINC1\n",
      "SERP1\n",
      "SET\n",
      "SFI1\n",
      "SFMBT1\n",
      "SLC39A10\n",
      "SLC9A3R1\n",
      "SNRNP200\n",
      "SP140\n",
      "SPAG9\n",
      "SPN\n",
      "SPOCK2\n",
      "SSR4\n",
      "STAM\n",
      "STAT1\n",
      "STK17A\n",
      "STRN\n",
      "SYTL2\n",
      "TAF15\n",
      "TAGAP\n",
      "TBCA\n",
      "TCF25\n",
      "TCF7\n",
      "TESPA1\n",
      "TGFBR3\n",
      "TIAL1\n",
      "TMEM117\n",
      "TMEM135\n",
      "TMEM245\n",
      "TMEM63A\n",
      "TMEM65\n",
      "TNFSF8\n",
      "TOB1\n",
      "TPR\n",
      "TRAC\n",
      "TRAF3IP3\n",
      "TRAT1\n",
      "TRG-AS1\n",
      "TRIR\n",
      "TSHZ2\n",
      "TTC39B\n",
      "TTN\n",
      "UBASH3B\n",
      "UBL3\n",
      "UBL5\n",
      "UQCRB\n",
      "USP36\n",
      "VAMP2\n",
      "WWP2\n",
      "XAF1\n",
      "XRN2\n",
      "ZFYVE28\n",
      "ZNF217\n",
      "ZNF331\n",
      "ZNF652\n"
     ]
    }
   ],
   "source": [
    "for name in np.unique(all_key_genes):\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr1:100400124-100401025\n",
      "chr1:100452357-100453271\n",
      "chr1:100927083-100927969\n",
      "chr1:101357133-101358041\n",
      "chr1:108703358-108704087\n",
      "chr1:116556331-116557223\n",
      "chr1:116738599-116739461\n",
      "chr1:116877748-116878647\n",
      "chr1:117658424-117659293\n",
      "chr1:13702529-13703436\n",
      "chr1:151830384-151831295\n",
      "chr1:154791005-154791840\n",
      "chr1:156124729-156125582\n",
      "chr1:160664488-160665308\n",
      "chr1:167441913-167442794\n",
      "chr1:169694194-169695012\n",
      "chr1:181159372-181160283\n",
      "chr1:184843493-184844395\n",
      "chr1:18948169-18949079\n",
      "chr1:19074232-19075107\n",
      "chr1:193478979-193479887\n",
      "chr1:194289309-194290212\n",
      "chr1:19687950-19688899\n",
      "chr1:198187853-198188770\n",
      "chr1:198668796-198669696\n",
      "chr1:203682362-203683265\n",
      "chr1:206715677-206716563\n",
      "chr1:206736878-206737712\n",
      "chr1:206784197-206785074\n",
      "chr1:206816648-206817552\n",
      "chr1:221712956-221713713\n",
      "chr1:223363861-223364743\n",
      "chr1:226444671-226445586\n",
      "chr1:227773894-227774801\n",
      "chr1:229253318-229254119\n",
      "chr1:232923711-232924616\n",
      "chr1:234730807-234731559\n",
      "chr1:234758605-234759500\n",
      "chr1:25796521-25797356\n",
      "chr1:38977438-38978360\n",
      "chr1:39183542-39184421\n",
      "chr1:53413067-53413977\n",
      "chr1:57307135-57308052\n",
      "chr1:58941500-58942417\n",
      "chr1:6460071-6460918\n",
      "chr1:66351635-66352527\n",
      "chr1:7948785-7949679\n",
      "chr1:89126913-89127800\n",
      "chr1:89610395-89611174\n",
      "chr10:100433498-100434398\n",
      "chr10:101782956-101783855\n",
      "chr10:101829240-101829859\n",
      "chr10:103053822-103054756\n",
      "chr10:11150311-11151057\n",
      "chr10:114544018-114544904\n",
      "chr10:118698201-118699103\n",
      "chr10:130301012-130301843\n",
      "chr10:132159023-132159874\n",
      "chr10:132616039-132616954\n",
      "chr10:14586608-14587451\n",
      "chr10:15370768-15371634\n",
      "chr10:17648142-17649017\n",
      "chr10:32459052-32459968\n",
      "chr10:3887605-3888502\n",
      "chr10:6129697-6130570\n",
      "chr10:62082566-62083336\n",
      "chr10:62383858-62384706\n",
      "chr10:70578097-70578970\n",
      "chr10:71742058-71742855\n",
      "chr10:72083674-72084561\n",
      "chr10:7269110-7270002\n",
      "chr10:74586019-74586373\n",
      "chr10:8056783-8057586\n",
      "chr10:8059858-8060669\n",
      "chr10:8183013-8183936\n",
      "chr10:8404471-8405371\n",
      "chr10:86400922-86401559\n",
      "chr10:94545287-94546198\n",
      "chr11:118001419-118002330\n",
      "chr11:118046000-118046868\n",
      "chr11:118342298-118343130\n",
      "chr11:118343914-118344801\n",
      "chr11:118398579-118399462\n",
      "chr11:118434144-118434988\n",
      "chr11:118610635-118611510\n",
      "chr11:118892199-118893106\n",
      "chr11:118895347-118896208\n",
      "chr11:118919417-118920270\n",
      "chr11:122696361-122697108\n",
      "chr11:128422365-128423266\n",
      "chr11:128716647-128717362\n",
      "chr11:128830021-128830745\n",
      "chr11:13923934-13924707\n",
      "chr11:2467054-2467936\n",
      "chr11:326708-327585\n",
      "chr11:34248219-34249123\n",
      "chr11:46330951-46331771\n",
      "chr11:60912006-60912871\n",
      "chr11:61083380-61084214\n",
      "chr11:64900008-64900880\n",
      "chr11:64971626-64972512\n",
      "chr11:67446050-67446955\n",
      "chr11:75507926-75508817\n",
      "chr11:76231850-76232766\n",
      "chr12:107027316-107028219\n",
      "chr12:107371952-107372863\n",
      "chr12:111993327-111994151\n",
      "chr12:116924319-116925194\n",
      "chr12:121092552-121093446\n",
      "chr12:121534276-121535160\n",
      "chr12:122244509-122245416\n",
      "chr12:12481948-12482804\n",
      "chr12:31890392-31891274\n",
      "chr12:3947489-3948397\n",
      "chr12:44876103-44876888\n",
      "chr12:4506601-4507502\n",
      "chr12:51323273-51323660\n",
      "chr12:53236155-53237061\n",
      "chr12:538972-539840\n",
      "chr12:56160236-56161112\n",
      "chr12:56338407-56339298\n",
      "chr12:64679315-64680211\n",
      "chr12:66287887-66288774\n",
      "chr12:67483017-67483937\n",
      "chr12:89397905-89398807\n",
      "chr12:92398393-92399288\n",
      "chr12:96377579-96378476\n",
      "chr13:24264721-24265634\n",
      "chr13:27130907-27131808\n",
      "chr13:30773927-30774845\n",
      "chr13:31806834-31807754\n",
      "chr13:48185968-48186783\n",
      "chr13:51801825-51802554\n",
      "chr13:79451289-79452183\n",
      "chr13:95185606-95186491\n",
      "chr13:99215207-99216079\n",
      "chr14:100078718-100079623\n",
      "chr14:100546109-100547024\n",
      "chr14:104077727-104078624\n",
      "chr14:104864868-104865700\n",
      "chr14:105859561-105860473\n",
      "chr14:106004697-106005601\n",
      "chr14:22536675-22537546\n",
      "chr14:31123960-31124845\n",
      "chr14:35203636-35204566\n",
      "chr14:61335637-61336537\n",
      "chr14:61337534-61338420\n",
      "chr14:61384998-61385913\n",
      "chr14:61452226-61453106\n",
      "chr14:63650099-63651010\n",
      "chr14:64673449-64674333\n",
      "chr14:64696175-64697090\n",
      "chr14:65846857-65847697\n",
      "chr14:68779666-68780441\n",
      "chr14:76929703-76930618\n",
      "chr14:91078165-91079067\n",
      "chr14:91867136-91868005\n",
      "chr14:91869727-91870613\n",
      "chr14:96126149-96127037\n",
      "chr14:97977855-97978638\n",
      "chr14:98223498-98224398\n",
      "chr14:99191607-99192440\n",
      "chr14:99260703-99261509\n",
      "chr15:38683347-38684186\n",
      "chr15:39580097-39580829\n",
      "chr15:41969730-41970635\n",
      "chr15:52256052-52256956\n",
      "chr15:59803412-59804326\n",
      "chr15:60398635-60399362\n",
      "chr15:60580335-60581247\n",
      "chr15:63841693-63842596\n",
      "chr15:65828530-65829164\n",
      "chr15:74845020-74845932\n",
      "chr15:76996523-76997407\n",
      "chr15:84736221-84737026\n",
      "chr15:90376632-90377526\n",
      "chr15:90407754-90408659\n",
      "chr15:90856419-90857303\n",
      "chr15:91469644-91470553\n",
      "chr16:17396593-17397446\n",
      "chr16:23948592-23949371\n",
      "chr16:3076727-3077596\n",
      "chr16:4465692-4466564\n",
      "chr16:56194073-56194976\n",
      "chr16:56610178-56610882\n",
      "chr16:56938561-56939407\n",
      "chr16:56999154-57000041\n",
      "chr16:68275506-68276415\n",
      "chr16:69443812-69444760\n",
      "chr16:75074944-75075846\n",
      "chr16:75098258-75099121\n",
      "chr16:79271899-79272807\n",
      "chr16:79602009-79602814\n",
      "chr16:82655145-82656015\n",
      "chr16:89841000-89841926\n",
      "chr17:14201341-14202069\n",
      "chr17:2222723-2223644\n",
      "chr17:22520971-22521869\n",
      "chr17:35177260-35178134\n",
      "chr17:35890485-35891389\n",
      "chr17:40068518-40069395\n",
      "chr17:41341185-41342069\n",
      "chr17:416545-417324\n",
      "chr17:47706303-47707218\n",
      "chr17:48453244-48454167\n",
      "chr17:4900630-4901435\n",
      "chr17:50779702-50780594\n",
      "chr17:50871081-50872000\n",
      "chr17:55238079-55238959\n",
      "chr17:58152757-58153665\n",
      "chr17:64081812-64082735\n",
      "chr17:64347855-64348761\n",
      "chr17:65173229-65174076\n",
      "chr17:66246065-66246982\n",
      "chr17:66673418-66674321\n",
      "chr17:7045502-7046284\n",
      "chr17:74279837-74280755\n",
      "chr17:75242186-75243068\n",
      "chr17:76191615-76192480\n",
      "chr17:76772777-76773624\n",
      "chr17:78381460-78382383\n",
      "chr18:24011234-24012138\n",
      "chr18:24118716-24119593\n",
      "chr18:26215356-26216249\n",
      "chr18:2889538-2890462\n",
      "chr18:3305005-3305907\n",
      "chr18:3604370-3605286\n",
      "chr18:45962302-45963107\n",
      "chr18:48842355-48843221\n",
      "chr18:49820035-49820856\n",
      "chr18:63199549-63200482\n",
      "chr18:74493455-74494332\n",
      "chr18:75070749-75071674\n",
      "chr18:76720971-76721851\n",
      "chr18:77071623-77072497\n",
      "chr19:10142504-10143403\n",
      "chr19:1192089-1192994\n",
      "chr19:12289016-12289922\n",
      "chr19:14523130-14523809\n",
      "chr19:1874446-1875202\n",
      "chr19:29667597-29668460\n",
      "chr19:38559276-38560183\n",
      "chr19:45016286-45016813\n",
      "chr19:49568873-49569604\n",
      "chr19:55626136-55627020\n",
      "chr19:6674614-6675507\n",
      "chr19:8575296-8576160\n",
      "chr2:100643842-100644708\n",
      "chr2:10493327-10494224\n",
      "chr2:109143915-109144670\n",
      "chr2:112749531-112750371\n",
      "chr2:12856642-12857557\n",
      "chr2:130824130-130825045\n",
      "chr2:134180968-134181886\n",
      "chr2:136281174-136282058\n",
      "chr2:143258158-143258944\n",
      "chr2:148524266-148525160\n",
      "chr2:162011419-162012322\n",
      "chr2:162073705-162074539\n",
      "chr2:169107472-169108345\n",
      "chr2:172461730-172462575\n",
      "chr2:203719732-203720625\n",
      "chr2:203740776-203741641\n",
      "chr2:231425702-231426623\n",
      "chr2:234463766-234464686\n",
      "chr2:236967748-236968648\n",
      "chr2:237434666-237435561\n",
      "chr2:239321738-239322604\n",
      "chr2:239622889-239623785\n",
      "chr2:25285669-25286568\n",
      "chr2:3222587-3223541\n",
      "chr2:37585897-37586767\n",
      "chr2:38653667-38654458\n",
      "chr2:46351481-46352389\n",
      "chr2:64213982-64214908\n",
      "chr2:68721296-68722201\n",
      "chr2:70068771-70069672\n",
      "chr2:8303267-8304167\n",
      "chr2:84782862-84783699\n",
      "chr2:86825785-86826701\n",
      "chr2:97718315-97719218\n",
      "chr2:9781923-9782825\n",
      "chr2:98835648-98836483\n",
      "chr20:17610356-17611190\n",
      "chr20:32509392-32510287\n",
      "chr20:33369086-33369899\n",
      "chr20:33370186-33370967\n",
      "chr20:37014887-37015797\n",
      "chr20:44070094-44070957\n",
      "chr20:56460219-56461107\n",
      "chr20:5835644-5836501\n",
      "chr20:59165899-59166780\n",
      "chr20:59222182-59223084\n",
      "chr20:63178907-63179770\n",
      "chr20:63750718-63751637\n",
      "chr20:63781778-63782643\n",
      "chr20:63955405-63955932\n",
      "chr21:31132619-31133502\n",
      "chr21:31354364-31355279\n",
      "chr21:33936260-33937107\n",
      "chr21:42238240-42239050\n",
      "chr21:44653848-44654712\n",
      "chr21:45224215-45225137\n",
      "chr21:45266271-45267160\n",
      "chr22:20549212-20550082\n",
      "chr22:21632241-21633147\n",
      "chr22:23242818-23243716\n",
      "chr22:28822719-28823582\n",
      "chr22:31278752-31279658\n",
      "chr22:36161912-36162824\n",
      "chr22:37604694-37605549\n",
      "chr22:38984754-38985657\n",
      "chr22:39099183-39100060\n",
      "chr22:39909947-39910781\n",
      "chr22:40055220-40056072\n",
      "chr22:42269289-42269888\n",
      "chr3:101518102-101519030\n",
      "chr3:10242083-10242987\n",
      "chr3:10263571-10264487\n",
      "chr3:108806105-108807005\n",
      "chr3:114231457-114232317\n",
      "chr3:129118563-129119428\n",
      "chr3:13880365-13880913\n",
      "chr3:141410965-141411664\n",
      "chr3:15054778-15055708\n",
      "chr3:157130164-157131026\n",
      "chr3:161310665-161311584\n",
      "chr3:16311689-16312606\n",
      "chr3:183469071-183469980\n",
      "chr3:187999683-188000493\n",
      "chr3:189324432-189325342\n",
      "chr3:195609055-195609975\n",
      "chr3:20103982-20104869\n",
      "chr3:42555977-42556867\n",
      "chr3:45693811-45694740\n",
      "chr3:45869842-45870757\n",
      "chr3:45950537-45951393\n",
      "chr3:45976431-45977306\n",
      "chr3:46284878-46285709\n",
      "chr3:47056983-47057848\n",
      "chr3:5019292-5020111\n",
      "chr3:50316440-50317375\n",
      "chr3:52892769-52893536\n",
      "chr3:66306733-66307635\n",
      "chr3:93470137-93471048\n",
      "chr3:98564403-98565236\n",
      "chr4:101327919-101328850\n",
      "chr4:101526945-101527852\n",
      "chr4:102518496-102519380\n",
      "chr4:108166451-108167274\n",
      "chr4:108168395-108169302\n",
      "chr4:109648320-109649227\n",
      "chr4:139266376-139267288\n",
      "chr4:142404922-142405822\n",
      "chr4:147799720-147800598\n",
      "chr4:150827927-150828830\n",
      "chr4:151408604-151409471\n",
      "chr4:153114512-153115286\n",
      "chr4:153521906-153522746\n",
      "chr4:36322690-36323595\n",
      "chr4:38130982-38131752\n",
      "chr4:40283758-40284639\n",
      "chr4:42671222-42672116\n",
      "chr4:6180666-6181567\n",
      "chr4:80500345-80501257\n",
      "chr4:88382379-88383277\n",
      "chr4:9603857-9604711\n",
      "chr4:98706006-98706881\n",
      "chr5:126508586-126509494\n",
      "chr5:132055860-132056795\n",
      "chr5:134086651-134087565\n",
      "chr5:134100138-134101004\n",
      "chr5:134145581-134146492\n",
      "chr5:141691799-141692663\n",
      "chr5:142104258-142105158\n",
      "chr5:143434663-143434978\n",
      "chr5:157159108-157159910\n",
      "chr5:157193466-157194380\n",
      "chr5:157540575-157541445\n",
      "chr5:157603140-157603999\n",
      "chr5:175895419-175896307\n",
      "chr5:176529558-176530397\n",
      "chr5:177370464-177371383\n",
      "chr5:32531559-32532298\n",
      "chr5:35854069-35854835\n",
      "chr5:35924615-35925514\n",
      "chr5:50428281-50429186\n",
      "chr5:50711017-50711902\n",
      "chr5:55102204-55103009\n",
      "chr5:758890-759748\n",
      "chr5:76325627-76326526\n",
      "chr5:79237150-79238038\n",
      "chr5:82356835-82357723\n",
      "chr5:912209-913120\n",
      "chr5:96537124-96538040\n",
      "chr5:96876010-96876900\n",
      "chr5:99037505-99038423\n",
      "chr6:106195248-106196022\n",
      "chr6:106360242-106361056\n",
      "chr6:111605414-111606171\n",
      "chr6:118796318-118797197\n",
      "chr6:128520232-128521095\n",
      "chr6:130513458-130514370\n",
      "chr6:134240448-134241323\n",
      "chr6:135128441-135129193\n",
      "chr6:137589166-137590037\n",
      "chr6:137717209-137718081\n",
      "chr6:137819559-137820441\n",
      "chr6:137883369-137884137\n",
      "chr6:139160942-139161695\n",
      "chr6:149494664-149495516\n",
      "chr6:152184645-152185578\n",
      "chr6:156396154-156396981\n",
      "chr6:157761019-157761927\n",
      "chr6:161076050-161076976\n",
      "chr6:16420687-16421610\n",
      "chr6:16481408-16482302\n",
      "chr6:16654449-16655357\n",
      "chr6:167119411-167120299\n",
      "chr6:25041903-25042661\n",
      "chr6:25865748-25866655\n",
      "chr6:33856388-33857297\n",
      "chr6:44932553-44933350\n",
      "chr6:45586350-45587183\n",
      "chr6:46011193-46012082\n",
      "chr6:5173974-5174864\n",
      "chr6:90233702-90234616\n",
      "chr6:90397741-90398585\n",
      "chr7:102671306-102672199\n",
      "chr7:130929981-130930880\n",
      "chr7:131007235-131008109\n",
      "chr7:134669562-134670439\n",
      "chr7:138875579-138876491\n",
      "chr7:143408550-143409388\n",
      "chr7:148628361-148629251\n",
      "chr7:151206760-151207615\n",
      "chr7:151429554-151430468\n",
      "chr7:157612889-157613741\n",
      "chr7:25028420-25029301\n",
      "chr7:25862097-25863026\n",
      "chr7:36285968-36286801\n",
      "chr7:43669145-43670048\n",
      "chr7:44257555-44258478\n",
      "chr7:44914809-44915687\n",
      "chr7:50321377-50322290\n",
      "chr7:5144108-5144958\n",
      "chr7:5641673-5642553\n",
      "chr7:76008727-76009631\n",
      "chr7:7944453-7945383\n",
      "chr7:831969-832857\n",
      "chr7:86978549-86979448\n",
      "chr7:96285350-96286266\n",
      "chr7:98400722-98401593\n",
      "chr8:100409994-100410784\n",
      "chr8:11820105-11820996\n",
      "chr8:127981094-127981953\n",
      "chr8:130244631-130245519\n",
      "chr8:133062292-133063207\n",
      "chr8:133520098-133520965\n",
      "chr8:140790627-140791490\n",
      "chr8:143549172-143549998\n",
      "chr8:144262129-144262975\n",
      "chr8:19496774-19497670\n",
      "chr8:19510850-19511750\n",
      "chr8:2130737-2131641\n",
      "chr8:2133645-2134527\n",
      "chr8:23219299-23219951\n",
      "chr8:38359162-38360012\n",
      "chr8:38362558-38363375\n",
      "chr8:38368244-38369121\n",
      "chr8:60796326-60797227\n",
      "chr8:66467545-66468455\n",
      "chr8:66490502-66491419\n",
      "chr8:7354769-7355690\n",
      "chr8:80138400-80139278\n",
      "chr8:80864470-80865384\n",
      "chr8:9011665-9012579\n",
      "chr8:94119723-94120662\n",
      "chr9:105405231-105406120\n",
      "chr9:120903772-120904680\n",
      "chr9:127955141-127956054\n",
      "chr9:131709682-131710612\n",
      "chr9:132581744-132582654\n",
      "chr9:133371066-133371858\n",
      "chr9:135978758-135979682\n",
      "chr9:136902182-136902928\n",
      "chr9:33446082-33446817\n",
      "chr9:37528882-37529735\n",
      "chr9:37531465-37532388\n",
      "chr9:447376-448282\n",
      "chr9:89743198-89744076\n",
      "chr9:92953855-92954737\n",
      "chrX:102155558-102156374\n",
      "chrX:110229540-110230457\n",
      "chrX:119682023-119682946\n",
      "chrX:130339422-130340343\n",
      "chrX:136586094-136587006\n",
      "chrX:44344209-44344794\n",
      "chrX:49053241-49054181\n",
      "chrX:49273048-49273948\n",
      "chrX:72105154-72106048\n",
      "chrX:78516874-78517789\n"
     ]
    }
   ],
   "source": [
    "for name in np.unique(all_key_peaks):\n",
    "    print(name.replace('-', ':', 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we show how to find the potential regulatory relationship between the top related genes and peaks. Again, take CD4+ T activated as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CD14+ Mono', 'ID2-hi myeloid prog', 'CD16+ Mono', 'cDC2', 'pDC',\n",
       "       'HSC', 'G/M prog', 'Lymph prog', 'MK/E prog', 'Naive CD20+ B',\n",
       "       'B1 B', 'Transitional B', 'Plasma cell', 'CD4+ T naive',\n",
       "       'CD4+ T activated', 'CD8+ T', 'CD8+ T naive', 'NK', 'ILC',\n",
       "       'Proerythroblast', 'Erythroblast', 'Normoblast'], dtype='<U19')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to first load the encode H3K27ac data to find the potential enhancer position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_index = [14]\n",
    "bed_file = []\n",
    "\n",
    "for n in type_index:\n",
    "    if n in [13,14]:\n",
    "        bed_file.append(pd.read_csv('encode/CD4.bed', sep='\\t', header=None, names=['chrom', 'start', 'end', 'name', 'score', 'strand', 'signalValue','pValue','qValue','peak']))\n",
    "    elif n in [1]:\n",
    "        bed_file.append(pd.read_csv('encode/myeloid.bed', sep='\\t', header=None, names=['chrom', 'start', 'end', 'name', 'score', 'strand', 'signalValue','pValue','qValue','peak']))\n",
    "    elif n in [9,10,11]:\n",
    "        bed_file.append(pd.read_csv('encode/B cell.bed', sep='\\t', header=None, names=['chrom', 'start', 'end', 'name', 'score', 'strand', 'signalValue','pValue','qValue','peak']))\n",
    "    elif n in [15,16]:\n",
    "        bed_file.append(pd.read_csv('encode/CD8.bed', sep='\\t', header=None, names=['chrom', 'start', 'end', 'name', 'score', 'strand', 'signalValue','pValue','qValue','peak']))\n",
    "    elif n in [17]:\n",
    "        bed_file.append(pd.read_csv('encode/NK.bed', sep='\\t', header=None, names=['chrom', 'start', 'end', 'name', 'score', 'strand', 'signalValue','pValue','qValue','peak']))\n",
    "    elif n in [0]:\n",
    "        bed_file.append(pd.read_csv('encode/CD14.bed', sep='\\t', header=None, names=['chrom', 'start', 'end', 'name', 'score', 'strand', 'signalValue','pValue','qValue','peak']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, calcualte the overlap of above top peaks to genes' enhancer position or promoter (TSS) position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CD4+ T activated: 291 genes, 537 peaks, 545 enhancer, 31 overlap, 6 tts,\n"
     ]
    }
   ],
   "source": [
    "all_overlaps = []\n",
    "all_peaks = []\n",
    "all_tss = []\n",
    "\n",
    "num_genes = []\n",
    "num_enhancers = []\n",
    "\n",
    "for ii, k in enumerate(type_index):\n",
    "\n",
    "    cross_map = att_layer2[k].mean(0).cpu().detach().numpy()+att_layer2_atac[k].mean(0).cpu().detach().numpy().T\n",
    "    flattened_indices = np.argsort(cross_map, axis=None)[-15:]\n",
    "    positions = np.unravel_index(flattened_indices, cross_map.shape)\n",
    "    max_values = cross_map[positions]\n",
    "\n",
    "    time_step = 1\n",
    "    interested_type = type_list[k] \n",
    "    down_sample = 5\n",
    "    model_kwargs = {}\n",
    "    batch = {}\n",
    "\n",
    "    x1 = torch.tensor(rna[::down_sample].X.toarray(),requires_grad=True)\n",
    "    x2 = torch.tensor(atac[::down_sample].X.toarray(),requires_grad=True)\n",
    "    index = list(range(rna[::down_sample].shape[0])) if interested_type=='all' else (rna[::down_sample].obs['cell_type'] == interested_type)\n",
    "\n",
    "\n",
    "    all_key_genes = [] \n",
    "    all_key_peaks = [] \n",
    "    all_position = []\n",
    "\n",
    "    for key_ele in np.unique(positions[1][-15:]):\n",
    "\n",
    "        batch[\"X_norm\"] = {'rna':x1,'atac':x2}\n",
    "        z = encoder_model.encode(batch)\n",
    "        video_start = z['rna'].unsqueeze(1).to(dist_util.dev())\n",
    "        audio_start = z['atac'].unsqueeze(1).to(dist_util.dev())\n",
    "        model_kwargs[\"label\"] = torch.tensor(classes_all[::down_sample]).to(dist_util.dev())\n",
    "        noise ={\"video\":torch.randn_like(video_start),\\\n",
    "            \"audio\":torch.randn_like(audio_start)}\n",
    "        t = (torch.ones(video_start.shape[0], device=dist_util.dev())*time_step).to(dtype=torch.int)\n",
    "        video_t = multimodal_diffusion.q_sample(video_start, t, noise = noise[\"video\"])\n",
    "        audio_t = multimodal_diffusion.q_sample(audio_start, t, noise = noise[\"audio\"])\n",
    "        video_t = video_t[index]\n",
    "        audio_t = audio_t[index]\n",
    "        t = t[index]\n",
    "        labels = model_kwargs[\"label\"][index]\n",
    "\n",
    "        noise_pred_video, noise_pred_audio, att_vec = multimodal_model(video_t,audio_t,t,labels,return_attvec=True)\n",
    "        att_vec[4][:,:,key_ele].mean().backward(create_graph=True,)\n",
    "        top_k = mdata['rna'].shape[1]\n",
    "        values, indices = torch.topk(abs(x1.grad[index]).mean(0)*(x1[index].sum(0)>int(x1[index].shape[0]*0.2)), top_k)#\n",
    "        top_gene = np.array([mdata['rna'].var_names[id] for id in np.array(indices)])\n",
    "        all_key_genes += list(top_gene[:100])\n",
    "\n",
    "        multimodal_model.zero_grad()\n",
    "        encoder_model.zero_grad()\n",
    "        x1.grad.zero_()\n",
    "        x2.grad.zero_()\n",
    "\n",
    "    for key_ele in np.unique(positions[0][-15:]):\n",
    "\n",
    "        batch[\"X_norm\"] = {'rna':x1,'atac':x2}\n",
    "        z = encoder_model.encode(batch)\n",
    "        video_start = z['rna'].unsqueeze(1).to(dist_util.dev())\n",
    "        audio_start = z['atac'].unsqueeze(1).to(dist_util.dev())\n",
    "        model_kwargs[\"label\"] = torch.tensor(classes_all[::down_sample]).to(dist_util.dev())\n",
    "        noise ={\"video\":torch.randn_like(video_start),\\\n",
    "            \"audio\":torch.randn_like(audio_start)}\n",
    "        t = (torch.ones(video_start.shape[0], device=dist_util.dev())*time_step).to(dtype=torch.int)\n",
    "        video_t = multimodal_diffusion.q_sample(video_start, t, noise = noise[\"video\"])\n",
    "        audio_t = multimodal_diffusion.q_sample(audio_start, t, noise = noise[\"audio\"])\n",
    "        video_t = video_t[index]\n",
    "        audio_t = audio_t[index]\n",
    "        t = t[index]\n",
    "        labels = model_kwargs[\"label\"][index]\n",
    "\n",
    "        noise_pred_video, noise_pred_audio, att_vec = multimodal_model(video_t,audio_t,t,labels,return_attvec=True)\n",
    "        att_vec[5][:,:,key_ele].mean().backward(create_graph=True)\n",
    "        values2, indices2 = torch.topk(abs(x2.grad[index]).mean(0)*(x2[index].sum(0)>int(x2[index].shape[0]*0.1)), mdata['atac'].shape[1])#\n",
    "        top_peak = np.array([mdata['atac'].var_names[id] for id in np.array(indices2)])\n",
    "        \n",
    "        all_key_peaks += list(top_peak[:200])\n",
    "\n",
    "        multimodal_model.zero_grad()\n",
    "        encoder_model.zero_grad()\n",
    "        x1.grad.zero_()\n",
    "        x2.grad.zero_()\n",
    "\n",
    "    chromosome_segments = np.unique(all_key_peaks)\n",
    "    final_df['seqname'] = [name.split('_')[0] for name in final_df['seqname']]\n",
    "    \n",
    "    enhancers = []\n",
    "    for segment in chromosome_segments:\n",
    "        overlaps = find_enhancer_overlaps(bed_file[ii], segment)\n",
    "        if not overlaps.empty:\n",
    "            for i in range(overlaps.shape[0]):\n",
    "                enhancers.append('-'.join([overlaps['chrom'].values[i],str(overlaps['start'].values[i]),str(overlaps['end'].values[i])]))\n",
    "    overlap_gene = []\n",
    "    overlap_seg = []\n",
    "    # 查找每个染色体片段的最近基因\n",
    "    for segment in chromosome_segments:\n",
    "        gene_name, feature = find_nearest_gene(segment)\n",
    "        if gene_name is not None:\n",
    "            overlap_gene+=gene_name\n",
    "            overlap_seg.append(segment)\n",
    "    \n",
    "    results = []\n",
    "    for segment in chromosome_segments:\n",
    "        result = check_tss_overlap(final_df, segment)\n",
    "        if len(result)>0:\n",
    "            results.append(result)\n",
    "    \n",
    "    all_tss.append(np.unique(np.concatenate(results)[np.in1d(np.concatenate(results),np.unique(all_key_genes))]))\n",
    "    all_overlaps.append(np.array(overlap_gene)[np.in1d(overlap_gene,np.unique(all_key_genes))])\n",
    "    all_peaks.append(np.array(chromosome_segments))\n",
    "    num_genes.append(np.unique(all_key_genes).shape[0])\n",
    "    num_enhancers.append(len(enhancers))\n",
    "    num_overlap = np.array(overlap_gene)[np.in1d(overlap_gene,np.unique(all_key_genes))].shape[0]\n",
    "    num_tts = np.unique(np.concatenate(results)[np.in1d(np.concatenate(results),np.unique(all_key_genes))]).shape[0]\n",
    "    print(f'{type_list[k]}: {np.unique(all_key_genes).shape[0]} genes, {chromosome_segments.shape[0]} peaks, {len(enhancers)} enhancer, {num_overlap} overlap, {num_tts} tts,' )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, you can further obtain the top related peaks that overlaped with the TSS and the potential enhancer of the top related genes. Here we need the loop data between enhancers and TSS, these loop data can be found in HiChip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CD4+ T activated\n",
      "all overlap:  ['SELL' 'TRAF3IP3' 'TGFBR3' 'TGFBR3' 'GATA3' 'CD3D' 'CD5' 'DUSP16'\n",
      " 'DUSP16' 'TESPA1' 'KLRG1' 'FMN1' 'USP36' 'LPIN2' 'MALT1' 'CD28' 'EPHA4'\n",
      " 'GALM' 'NOL4L' 'SFMBT1' 'TCF7' 'RETREG1' 'PRDM1' 'SCML4' 'LINC-PINT'\n",
      " 'LINC00513' 'STK17A' 'CSGALNACT1' 'CSGALNACT1' 'TNFSF8' 'P2RY8']\n",
      "tss: \n",
      "CD28\n",
      "chr2-203703517-203706517\n",
      "overlaps_enhancer:  ['chr2-203706134-203707025']\n",
      "CD5\n",
      "chr11-61099489-61102489\n",
      "overlaps_enhancer:  ['chr11-61021436-61022320' 'chr11-61041942-61042854'\n",
      " 'chr11-61101933-61102835']\n",
      "DIPK1A\n",
      "chr1-92961522-92964522\n",
      "overlaps_enhancer:  ['chr1-92962295-92963080']\n",
      "FMN1\n",
      "chr15-33194714-33197714\n",
      "overlaps_enhancer:  []\n",
      "RETREG1\n",
      "chr5-16617101-16620101\n",
      "overlaps_enhancer:  ['chr5-16616577-16617478']\n",
      "TESPA1\n",
      "chr12-54984762-54987762\n",
      "overlaps_enhancer:  ['chr12-54984440-54985280']\n"
     ]
    }
   ],
   "source": [
    "def check_overlap(segment1, segment2):\n",
    "    \"\"\"check if two segments overlaped\"\"\"\n",
    "    chrom1, start1, end1 = parse_segment(segment1)\n",
    "    chrom2, start2, end2 = parse_segment(segment2)\n",
    "\n",
    "    return chrom1 == chrom2 and start1 <= end2 and end1 >= start2\n",
    "\n",
    "def find_overlapping_segments(list1, list2):\n",
    "    \"\"\"find overlaped segments in two segments list\"\"\"\n",
    "    overlapping = []\n",
    "    for seg1 in list1:\n",
    "        for seg2 in list2:\n",
    "            if check_overlap(seg1, seg2):\n",
    "                overlapping.append(seg1)\n",
    "    return overlapping\n",
    "\n",
    "type_index_loop = [14]\n",
    "bed_file_loop = []\n",
    "for n in type_index_loop:\n",
    "    if n in [13,14,15,16]:\n",
    "        bed_file_loop.append(pd.read_csv('hichip/loop_info_T_cell_hichipper.csv',))\n",
    "    elif n in [9,10,11]:\n",
    "        bed_file_loop.append(pd.read_csv('hichip/loop_info_B_cell_hichipper.csv',))\n",
    "\n",
    "for iid, item in enumerate([0]):  # 在type_index中的位置\n",
    "    print(type_list[type_index_loop[iid]])\n",
    "    print('all overlap: ',all_overlaps[item])\n",
    "    print('tss: ')\n",
    "    for tss in all_tss[item]:\n",
    "        print(tss)\n",
    "        chrom = final_df[(final_df['gene_name']==tss) & (final_df['feature']=='gene')][['seqname','start','end']].values[0][0]\n",
    "        start_id = str(final_df[(final_df['gene_name']==tss) & (final_df['feature']=='gene')][['seqname','start','end']].values[0][1])[:2]\n",
    "        \n",
    "        gene_seg = final_df[(final_df['gene_name']==tss) & (final_df['feature']=='gene')][['seqname','start','end','strand']].values[0]\n",
    "        if gene_seg[-1] == '+':\n",
    "            tss_seg = '-'.join([gene_seg[0],str(gene_seg[1]-3000),str(gene_seg[1])])\n",
    "        else:\n",
    "            tss_seg = '-'.join([gene_seg[0],str(gene_seg[2]),str(gene_seg[2]+3000)])\n",
    "        print(tss_seg)\n",
    "        overlaps = find_enhancer_overlaps_loop(bed_file_loop[iid], tss_seg)\n",
    "        if overlaps.empty:\n",
    "            continue\n",
    "\n",
    "        enhancers = []\n",
    "        for kkk in range(overlaps.shape[0]):\n",
    "            enhancers.append('-'.join([overlaps['chrom'].values[kkk],str(int(overlaps['start'].values[kkk])),str(int(overlaps['end'].values[kkk]))]))\n",
    "            enhancers.append('-'.join([overlaps['chrom'].values[kkk],str(int(overlaps['start2'].values[kkk])),str(int(overlaps['end2'].values[kkk]))]))\n",
    "        \n",
    "        selected_peak = [it for it in all_peaks[item] if it.startswith(chrom+'-'+start_id)]\n",
    "\n",
    "        overlaps_enhancer = find_overlapping_segments(selected_peak, enhancers)\n",
    "        print('overlaps_enhancer: ',np.unique(overlaps_enhancer))"
   ]
  }
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