{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee900772-45e5-4d5e-902c-eaec0214e11f",
   "metadata": {},
   "source": [
    "### Seqlet Calling and Downstream Analyses\n",
    "\n",
    "A key set of functionality implemented in tangermeme relates to the identification and usage of <i>seqlets</i>. Seqlets are short contiguous regions of the genome that have been identified as driving model predictions, usually through the use of an attribution method (e.g., DeepLIFT/SHAP). They also usually correspond biologically to a transcription factor (TF) binding site or some other important <i>cis-</i>regulatory element. The term \"seqlet\" was introduced in as the first step in the TF-MoDISco algorithm, with the subsequent steps involving processing and clustering these seqlets to find repeated patterns.\n",
    "\n",
    "In the pipeline of automatically extracting insights from sequence-based machine learning methods, seqlet calling comes directly after attributions and is, in many cases, the step where we transition from contiguous-based analyses to discrete logic-based ones. For instance, having an attribution track is nice as a visual and may help explain what is happening at an individual locus, but requires annotation, \"there is an AP-1 binding site here\". This annotation is done through seqlet calling on the attributions, followed by seqlet annotation using a motif database.\n",
    "\n",
    "Here, we will show how seqlets can be helpful when making concrete statements about what a model is focusing on at individual regions, and then use them to more globally characterize the logic that has been learned by a model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa1eeee-ff4b-4c74-83c2-431c271b3001",
   "metadata": {},
   "source": [
    "#### Loading Models\n",
    "\n",
    "First, we will load five models that make predictions for overlapping forms of biochemical activity. Specifically, we will load \n",
    "\n",
    "1. a BPNet model that predicts E2F3 binding\n",
    "2. a BPNet model that predicts MYC binding\n",
    "3. Beluga using its MYC binding predictions\n",
    "4. a ChromBPNet model that predicts accessibility\n",
    "5. a ProCapNet model that predicts transcription initiation\n",
    "\n",
    "All models are making predictions in the K562 cell line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3ad74fa-b635-4c43-88e5-c168832c30c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn; seaborn.set_style('whitegrid')\n",
    "\n",
    "import sys\n",
    "import torch\n",
    "\n",
    "from bpnetlite import BPNet\n",
    "from bpnetlite.bpnet import ControlWrapper\n",
    "from bpnetlite.bpnet import CountWrapper\n",
    "\n",
    "\n",
    "e2f3_bpnet = torch.load(\"../../../../models/bpnet/E2F3.torch\", weights_only=False)\n",
    "e2f3_bpnet = CountWrapper(ControlWrapper(e2f3_bpnet))\n",
    "\n",
    "myc_bpnet = torch.load(\"../../../../models/bpnet/MYC.torch\", weights_only=False)\n",
    "myc_bpnet = CountWrapper(ControlWrapper(myc_bpnet))\n",
    "\n",
    "sys.path.append(\"/users/jacob.schreiber/models/deepsea\")\n",
    "from beluga import Beluga\n",
    "\n",
    "class BelugaWrapper(torch.nn.Module):\n",
    "    def __init__(self, model, target):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.target = target\n",
    "    \n",
    "    def forward(self, X):\n",
    "        return self.model(X[:, :, 57:-57])[:, self.target:self.target+1]\n",
    "\n",
    "beluga = Beluga()\n",
    "beluga.load_state_dict(torch.load(\"../../../../models/deepsea/deepsea.beluga.pth\", weights_only=False))\n",
    "beluga = BelugaWrapper(beluga, 614)\n",
    "\n",
    "chrombpnet = BPNet.from_chrombpnet(\"../../../../models/chrombpnet/fold_0/model.chrombpnet_nobias.fold_0.ENCSR868FGK.h5\")\n",
    "chrombpnet = CountWrapper(chrombpnet)\n",
    "\n",
    "procapnet = BPNet(512, n_outputs=2, n_control_tracks=0)\n",
    "procapnet.load_state_dict(torch.load(\"../../../../models/procapnet/procapnet_model.fold0.torch\", weights_only=False))\n",
    "procapnet = CountWrapper(procapnet)\n",
    "\n",
    "models = e2f3_bpnet, myc_bpnet, beluga, chrombpnet, procapnet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49269a0c-9a09-44a2-a722-001c74d905e3",
   "metadata": {},
   "source": [
    "#### Calling seqlets at a single locus\n",
    "\n",
    "Now, we will load up the promoter of PLD6 as well as a variety of other MYC binding peaks across the genome to use as a background distribution for our seqlet calling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2cf6d4dc-71d5-44af-9c7e-2cd05db1422e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([100, 4, 2114])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "from tangermeme.io import extract_loci\n",
    "\n",
    "# PLD6\n",
    "df = pandas.DataFrame({\n",
    "    'chrom': ['chr1'],\n",
    "    'start': [11_060_020 - 2114 // 2],\n",
    "    'end': [11_060_020 + 2114 // 2]\n",
    "})\n",
    "\n",
    "X = extract_loci([df, \"ENCFF114VAI.bed.gz\"], \"/users/jacob.schreiber/common/hg38.fa\", n_loci=100).float()\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d6e268-f006-428f-abdf-3de142104103",
   "metadata": {},
   "source": [
    "Then, we can calculate attributions for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d572f511-cc62-4171-9ead-b66d868a2e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:08<00:00, 235.58it/s]\n",
      "100%|██████████| 2000/2000 [00:07<00:00, 256.50it/s]\n",
      "100%|██████████| 2000/2000 [00:21<00:00, 93.22it/s] \n",
      "100%|██████████| 2000/2000 [01:05<00:00, 30.41it/s]\n",
      "100%|██████████| 2000/2000 [00:57<00:00, 34.89it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 4, 2114])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tangermeme.deep_lift_shap import deep_lift_shap\n",
    "\n",
    "X_attr = torch.stack([\n",
    "    deep_lift_shap(model, X, batch_size=4, verbose=True, warning_threshold=0.01, random_state=0)[0] for model in models\n",
    "])\n",
    "\n",
    "X_attr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba67d36c-818d-4dfe-b888-5ecd3042f295",
   "metadata": {},
   "source": [
    "With these attributions, we can now call seqlets using the built-in `recursive_seqlets` function. For more details on how that algorithm works, see the seqlet calling tutorial. As a brief description, we are empirically finding spans of characters whose attribution is above what one would expect by chance, and we are then post-processing these spans to additionally have 2bp on either side because the seqlets are usually a bit conservative in their width. The \"core\" part of these seqlets cannot overlap, but the additional flanking bp are allowed to overlap each other and also overlap the \"core\" part of another seqlet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0afcc403-c273-4c97-88ed-bc50e532dcb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>example_idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>attribution</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1243</td>\n",
       "      <td>1259</td>\n",
       "      <td>0.388328</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>595</td>\n",
       "      <td>608</td>\n",
       "      <td>0.135827</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1025</td>\n",
       "      <td>1033</td>\n",
       "      <td>0.091028</td>\n",
       "      <td>0.008905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   example_idx  start   end  attribution   p-value\n",
       "0            0   1243  1259     0.388328  0.000046\n",
       "1            0    595   608     0.135827  0.003955\n",
       "2            0   1025  1033     0.091028  0.008905"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tangermeme.seqlet import recursive_seqlets\n",
    "\n",
    "X_seqlet = [\n",
    "    recursive_seqlets(X_attr[i:i+1].sum(dim=1), additional_flanks=2) for i in range(len(models))\n",
    "]\n",
    "\n",
    "X_seqlet[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31176471-d7e7-4d34-b2dc-df002161f6d5",
   "metadata": {},
   "source": [
    "Finally, we can visualize the results. We will be showing the attributions from each of the models at the TSS of PLD6 as well as bars under the attributions indicating where the called seqlets are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09389abf-50e5-4662-a29b-e611da3b5687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tangermeme.plot import plot_logo\n",
    "\n",
    "s, e = 1075, 1270\n",
    "\n",
    "names = \"BPNet (E2F3)\", \"BPNet (MYC)\", \"Beluga (MYC)\", \"ChromBPNet\", \"ProCapNet\"\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "\n",
    "for i in range(len(models)):\n",
    "    plt.subplot(len(models), 1, i+1)\n",
    "    plt.title(names[i], fontsize=6)\n",
    "    plot_logo(X_attr[i], annotations=X_seqlet[i], start=s, end=e, show_score=False, score_key='attribution')\n",
    "    plt.yticks(fontsize=8)\n",
    "    plt.ylim(-0.01, plt.gca().get_ylim()[1])\n",
    "    plt.xticks([], [])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ddf66d-b1e0-4b63-a65a-c509a1252760",
   "metadata": {},
   "source": [
    "It looks like the algorithm is calling seqlets on many of the contiguous spans that one might do by eye. Unfortunately, seqlet calling is a somewhat subjective task with no clear definitions as to when something definitely is or is not a seqlet. Based on my prior experience, this is a fairly clean segmentation of the attribution scores into seqlets, particularly when considering some of the more complex and close-together sequence features.\n",
    "\n",
    "Remember, the attribution calculations and seqlet calling are being done independently on each of the models. It is somewhat remarkable to see how different models -- trained to predict different readouts -- can have such similar attributions. One could view this stability as a sign of the robustness of such an interpretation approach.\n",
    "\n",
    "An interesting result here is that the Beluga (MYC) model appears to highlight almost a union of all the motifs found by the models. Basically, it seems to identify that the MYC motif is driving MYC binding, but it is also picking up that all of the motifs associated with chromatin accessibility are also associated with MYC binding. This is not necessarily incorrect, in the sense that an accessible region may have stronger binding of non-pioneer factors than an inaccessible region, but it does mean that the model is less specific to the sequence factors driving MYC binding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76de8884-aaa5-4700-b5fa-727da7145a58",
   "metadata": {},
   "source": [
    "#### Annotating seqlets at a single locus\n",
    "\n",
    "Now that we have our seqlets, we can map the discrete sequence within them to a motif database using Tomtom to automatically determine what motifs are being picked up by our models. Note that the choice of motif database is VERY important in this step. If you use one with ambiguous names or full of redundancy you may end up with fuzzier mappings than if you use one that is less redundant and with cleaner names. Here, we will use all motifs from JASPAR2024 that are derived from human ChIP-seq experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51f118eb-8509-4228-8375-a30d2373eada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>example_idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>attribution</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MYCN</td>\n",
       "      <td>1243</td>\n",
       "      <td>1259</td>\n",
       "      <td>0.388328</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MYC</td>\n",
       "      <td>595</td>\n",
       "      <td>608</td>\n",
       "      <td>0.135827</td>\n",
       "      <td>0.003955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZNF610</td>\n",
       "      <td>1025</td>\n",
       "      <td>1033</td>\n",
       "      <td>0.091028</td>\n",
       "      <td>0.008905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  example_idx  start   end  attribution   p-value\n",
       "0        MYCN   1243  1259     0.388328  0.000046\n",
       "1         MYC    595   608     0.135827  0.003955\n",
       "2      ZNF610   1025  1033     0.091028  0.008905"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "from tangermeme.io import read_meme\n",
    "from tangermeme.annotate import annotate_seqlets\n",
    "\n",
    "motifs = read_meme(\"JASPAR2024_homo_sapiens_chipseq.meme\")\n",
    "motif_names = numpy.array([name.split('.')[-1] for name in motifs.keys()]) # Remove the MA... from the name\n",
    "\n",
    "for i in range(5):\n",
    "    idxs, pvals = annotate_seqlets(X, X_seqlet[i], motifs)\n",
    "    X_seqlet[i]['example_idx'] = motif_names[idxs[:, 0].numpy()]\n",
    "    \n",
    "X_seqlet[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7519bc3-bce4-4531-b39a-2f386548ada7",
   "metadata": {},
   "source": [
    "Looks like our MYC BPNet model is picking up two MYC motifs and a zinc-finger motif in this region.\n",
    "\n",
    "Now we can plot the attributions and seqlet calls again but, instead of having an uninformative \"0\" under each seqlet, we can have the name of the motif that maps there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10b58e3a-cae9-414d-bd14-7df9f010f345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s, e = 1075, 1270\n",
    "\n",
    "names = \"BPNet (E2F3)\", \"BPNet (MYC)\", \"Beluga (MYC)\", \"ChromBPNet\", \"ProCapNet\"\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "for i in range(len(models)):\n",
    "    plt.subplot(len(models), 1, i+1)\n",
    "    plt.title(names[i], fontsize=6)\n",
    "    plot_logo(X_attr[i], annotations=X_seqlet[i], start=s, end=e, show_score=False, score_key='attribution')\n",
    "    plt.yticks(fontsize=8)\n",
    "    plt.ylim(-0.01, plt.gca().get_ylim()[1])\n",
    "    plt.xticks([], [])\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"tangermeme-fig2-attr.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee844774-c082-45b2-9db7-522742da14f2",
   "metadata": {},
   "source": [
    "Looking here, we can see that the story is cleaner to explain in words when using the annotated seqlets than when using only the attributions: the BPNet model predicting MYC picks up on the MYC model, the ChromBPNet model picks up on several motifs including SP-1 and NFYB, which are known to be active in K562, and the ProCapNet model picks up on the same motifs as the ChromBPNet model. The E2F3 model is picking up on an extended form of the motif that seems to be overlapping with the NFYB site here.\n",
    "\n",
    "A key point to take away from this procedure is that, although it is automatic (perhaps <i>because</i> it is automatic), user verification may be necessary, particularly in some of the more complicated cases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b4a535-4909-46f3-8206-78bc0ac40b74",
   "metadata": {},
   "source": [
    "#### Large-scale analysis using seqlets\n",
    "\n",
    "Being able to describe what is happening at an individual locus through the lens of annotated seqlets can be invaluable, but this can be scaled up to describe the global patterns of what is driving model predictions across the entire genome. Here, we will essentially repeat the same process that we employed for the individual locus, except do so across a large number of MYC binding sites across the genome. We will focus on comparing the MYC BPNet model and the MYC beluga model because they seemed to learn such different forms of activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be3f55c2-439d-442a-9422-f041da3adc26",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 199160/199160 [10:06<00:00, 328.36it/s]\n",
      " 57%|█████▋    | 113088/199160 [10:20<07:50, 182.83it/s]/users/jacob.schreiber/github/tangermeme/tangermeme/deep_lift_shap.py:460: RuntimeWarning: Convergence deltas too high: tensor([9.5367e-07, 9.5367e-07, 9.5367e-07, 7.1526e-07, 1.1921e-07, 3.5763e-06,\n",
      "        9.5367e-07, 5.9605e-07, 1.4305e-06, 4.7684e-07, 2.9206e-06, 7.1526e-07,\n",
      "        2.8610e-06, 4.7684e-07, 3.3379e-06, 4.7684e-07, 3.3379e-06, 4.7684e-07,\n",
      "        1.4305e-06, 1.1921e-06, 0.0000e+00, 4.2915e-06, 9.5367e-07, 9.5367e-07,\n",
      "        4.7684e-07, 1.2118e-02, 1.9073e-06, 9.5367e-07, 1.6689e-06, 1.9073e-06,\n",
      "        1.1921e-06, 2.3842e-06], device='cuda:0', grad_fn=<AbsBackward0>)\n",
      "  warnings.warn(\"Convergence deltas too high: \" +\n",
      "100%|██████████| 199160/199160 [18:15<00:00, 181.72it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 9958, 4, 2114])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = extract_loci(\"ENCFF114VAI.bed.gz\", \"/users/jacob.schreiber/common/hg38.fa\").float()\n",
    "X = X[X.sum(dim=(-1, -2)) == X.shape[-1]]\n",
    "\n",
    "X_attr = torch.stack([\n",
    "    deep_lift_shap(model, X, verbose=True, warning_threshold=0.01, random_state=0) for model in models[1:3]\n",
    "])\n",
    "\n",
    "X_attr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41aa15ee-1e78-4cad-94f6-b0df2b2a04a1",
   "metadata": {},
   "source": [
    "Then, we will call seqlets again for each of them. Using the additional flanking positions here can be especially helpful because sometimes an overly conservative seqlet call can exclude flanking nucleotides that are useful for distinguishing between several similar motifs in a redundant datbase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36609006-9a04-49c8-b815-d047d0d0e3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>example_idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>attribution</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3520</td>\n",
       "      <td>1047</td>\n",
       "      <td>1066</td>\n",
       "      <td>0.838357</td>\n",
       "      <td>4.305052e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3229</td>\n",
       "      <td>1009</td>\n",
       "      <td>1025</td>\n",
       "      <td>0.613310</td>\n",
       "      <td>7.472241e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1766</td>\n",
       "      <td>1004</td>\n",
       "      <td>1022</td>\n",
       "      <td>0.713385</td>\n",
       "      <td>7.688517e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2650</td>\n",
       "      <td>1088</td>\n",
       "      <td>1106</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>8.623045e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>456</td>\n",
       "      <td>1013</td>\n",
       "      <td>1032</td>\n",
       "      <td>0.717326</td>\n",
       "      <td>2.219866e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   example_idx  start   end  attribution       p-value\n",
       "0         3520   1047  1066     0.838357  4.305052e-08\n",
       "1         3229   1009  1025     0.613310  7.472241e-08\n",
       "2         1766   1004  1022     0.713385  7.688517e-08\n",
       "3         2650   1088  1106     0.727878  8.623045e-08\n",
       "4          456   1013  1032     0.717326  2.219866e-07"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_seqlet = [\n",
    "    recursive_seqlets(X_attr[i].sum(dim=1), threshold=0.001, additional_flanks=2) for i in range(X_attr.shape[0])\n",
    "]\n",
    "\n",
    "X_seqlet[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97963aaf-463a-4edd-a06b-93c9d69043b5",
   "metadata": {},
   "source": [
    "To get a more objective view of how well this seqlet calling algorithm works, let's just scan through 20 regions and see what the attributions and the seqlet calls look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "013f3d77-e311-4151-8943-ba9356cbffb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABjUAAACMCAYAAAA0liuOAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAFfJJREFUeJzt3X+QFuWBJ/DvOzMMPwaQMDA6DKKRgNHDrAZM0ISNERF1r4wBz4iJ5rKne7EsK/GSKrWoqGWyVSEmlUpcd02skK07T0v0EtfEOpEYT9YkanYr2YjkoshCZvgtgsAwwMy87/3R8uuUH4M/sOHzqZqa6emne57u6ffp7vf7PP1WarVaLQAAAAAAAO9xdYe7AgAAAAAAAAdDqAEAAAAAAJSCUAMAAAAAACgFoQYAAAAAAFAKQg0AAAAAAKAUhBoAAAAAAEApCDUAAAAAAIBSEGoAAAAAAAClINQAAAAAAABKQagBAAAAAACUglADAAAAAAAoBaEGAAAAAABQCkINAAAAAACgFIQaAAAAAABAKfQ51Fi2bFkuv/zyTJ8+PZdeemmWLFnyhjIdHR258sorM3HixMyYMeMN85988slccMEFmTZtWq6//vp0dnYeWu0BAAAAAICjRp9DjVtuuSWXXXZZ5s+fn6uvvjqzZ89+Q5nBgwfnS1/6Ur7zne+8YV5nZ2dmz56du+66KwsWLMjIkSNz9913H1rtAQAAAACAo0afQo3169dn8eLFufjii5Mk06dPT0dHRzo6OvYqN2zYsEyaNCkDBw58wzoWLlyYCRMmZOzYsUmSK664Io8++uih1h8AAAAAADhKNPSl8KpVq9LS0pKGhmKxSqWS1tbWrFq1KqNHjz7odYwaNWrXdFtbW9asWZNqtZq6uoPLWKrVamq1Wl+qDgAAAAAAvIfV19cfsEyfQo2kCDL2dCjhwv+/jr5atGhRuru739I6AAAAAACA946JEycesEyfQo3W1tasXr06PT09aWhoSK1Wy+rVq9Pa2tqndTzzzDO7plesWJFjjz32oEdpJMmECROM1AAAAAAAgKNMn0KN5ubmnHrqqXnkkUcyY8aMzJ8/P21tbQf96KkkmTJlSm6//fa8/PLLGTt2bO67775cdNFFfap0XwIQAAAAAADgyFCp9XHIw9KlS3PzzTdn48aNaWpqypw5czJu3LjMnj075557bqZOnZodO3bkvPPOy44dO7Jly5YMHz48n/rUp/KVr3wlSfLEE0/kjjvuSG9vb8aPH585c+Zk8ODB78gGAgAAAAAAR4Y+hxoAAAAAAACHg+c4AQAAAAAApSDUAAAAAAAASkGoAQAAAAAAlIJQAwAAAAAAKAWhBgAAAAAAUApCDQAAAAAAoBSEGgAAAAAAQCkINQAAAAAAgFIQagAAAAAAAKUg1AAAAAAAAEpBqAEAAAAAAJSCUAMAAAAAACgFoQYAAAAAAFAKQg0AAAAAAKAUhBoAAAAAAEApCDUAAAAAAIBSEGoAAAAAAAClINQAAAAAAABKQagBAAAAAACUglADAAAAAAAoBaEGAAAAAABQCkINAAAAAACgFIQaAAAAAABAKQg1AAAAAACAUhBqAAAAAAAApSDUAAAAAAAASkGoAQAAAAAAlIJQAwAAAAAAKAWhBgAAAAAAUApCDQAAAAAAoBSEGgAAAAAAQCkINQAAAAAAgFIQagAAAAAAAKXQ0NcFli1blptuuikbNmzIkCFD8s1vfjMf+MAH3lDuwQcfzD333JNqtZqzzjort956axoaGtLR0ZHzzz8/48aN21X2zjvvzJgxY97algAAAAAAAEe0Sq1Wq/VlgauuuiqXXHJJZsyYkcceeyw//vGP88ADD+xVpr29PbNmzcrDDz+c5ubmXHvttTnnnHNy+eWXp6OjIzNnzsyzzz77tm4IAAAAAABwZOvT46fWr1+fxYsX5+KLL06STJ8+PR0dHeno6Nir3Pz58zNt2rSMGDEilUols2bNyqOPPvr21RoAAAAAADjq9OnxU6tWrUpLS0saGorFKpVKWltbs2rVqowePXqvcqNGjdo13dbWlpUrV+6a7uzszMyZM1OtVjN16tRce+21qa+vP+h6VKvV9HGACQAAAAAA8B52MDlBnz9To1Kp7DW9r3Bhz3J7lmlpaclTTz2V5ubmbNy4MTfccEPmzp2ba6655qDrsGjRonR3d/ex5gAAAAAAwHvVxIkTD1imT6FGa2trVq9enZ6enjQ0NKRWq2X16tVpbW19Q7kVK1bsml65cuWukRuNjY1pbm5OkgwbNiwzZ87Mz3/+8z6FGhMmTDBSAwAAAAAAjjJ9CjWam5tz6qmn5pFHHsmMGTMyf/78tLW17fXoqaT4rI1Zs2bluuuuS3Nzc+6///5cdNFFSYrP5Rg6dGj69euXHTt25PHHH88pp5zSp0rX1fXpo0AAAAAAAIAjQKXWxyEPS5cuzc0335yNGzemqakpc+bMybhx4zJ79uyce+65mTp1apJk3rx5ueeee1KtVjN58uTcdttt6devXx5//PF8//vfT11dXXp7ezN58uTceOONaWxsfEc2EAAAAAAAODL0OdQAAAAAAAA4HDzHCQAAAAAAKAWhBgAAAAAAUApCDQAAAAAAoBSEGgAAAAAAQCkINQAAAAAAgFIQagAAAAAAAKUg1AAAAAAAAEpBqAEAAAAAAJSCUAMAAAAAACgFoQYAAAAAAFAKQg0AAAAAAKAUhBoAAAAAAEApCDUAAAAAAIBSEGoAAAAAAAClINQAAAAAAABKQagBAAAAAACUglADAAAAAAAoBaEGAAAAAABQCkINAAAAAACgFIQaAAAAAABAKQg1AAAAAACAUhBqAAAAAAAApSDUAAAAAAAASkGoAQAAAAAAlIJQAwAAAAAAKAWhBgAAAAAAUApCDQAAAAAAoBSEGgAAAAAAQCkINQAAAAAAgFIQagAAAAAAAKUg1AAAAAAAAEpBqAEAAAAAAJSCUAMAAAAAACgFoQYAAAAAAFAKfQ41li1blssvvzzTp0/PpZdemiVLlrxpuQcffDDnn39+zjvvvHzta19LT0/PrnlPPvlkLrjggkybNi3XX399Ojs7D30LAAAAAACAo0KfQ41bbrkll112WebPn5+rr746s2fPfkOZ9vb2fO9738t9992XBQsWZN26dXnooYeSJJ2dnZk9e3buuuuuLFiwICNHjszdd9/91rcEAADgKPLn1/6cC//nhZn2P6blW7/61uGuDgAAvCv6FGqsX78+ixcvzsUXX5wkmT59ejo6OtLR0bFXufnz52fatGkZMWJEKpVKZs2alUcffTRJsnDhwkyYMCFjx45NklxxxRW75gEAAHBgm7Zvyl/d91dZ27k2nzvtc7nxFzfmH3//j4e7WgBwxNm2Lfnzn5MXX0xeeKH4vmrV4a7VO69WK7Z906Zk8+aku/tw1wh2a+hL4VWrVqWlpSUNDcVilUolra2tWbVqVUaPHr1XuVGjRu2abmtry8qVK/c5b82aNalWq6mrO7iMpVqtplar9aXqb5vF6xbn3j/cmyGNQ7KtZ1vqKnX53Ic+l7HDx77ldddqtazZsiavbX8tSTJswLC0NLWkUqkccNllG5flO7/+Tn657JeZMmZKnlr+VP7mw3+TayZek6Z+TflN+2/y3We/m1qtlv71/bNpx6bcMPmGfPLET+69/mOOKb6/9tquX63ctDJ3/vbO/GLpLzJlzJQsXL4wn/+Lz+cLZ3whgxsHv+XthreqVkt6e4sTbLVaTFcqSX19MmDA4a7dO6R7S7J9ffFz/YCk8nr7WaslDYOTFQ8n29clI/8yaRiUdC5PeruSSkPSOj2p23/zv65zXRYuX5jmQc1Zv3V9Wge3ZlLbpDTWN+4u9CbtxdGqc0dn7l90fx5Y9EDOPv7s/KbjNznv/eflr8/464xoGnHA5XurvXlx/Yvp6u5KT7UnwwcNz0nvOyl1FR999WZqtaSrK9m+vXjN73n5MGhQ0r//4avb/mzalKxdm/T0JEOGJA0NRVtVqyVNTclgp1Q46mzevjnPrXgu9XX16eruyvCBw3NG6xl7n2/fRE+1J1f95Kq82vlqLhp3UV5Y80LOGXNOvvy/v5wTh56YKSdMyR/X/TEPLHogwwcNz+btm9O/oX8+M+EzOX7o8W+pzrVa8cbGtm3FdMMelxT9+hXt22HR05ms+T9Jz+Zk2GlJ3YBka3sxr35A0vzRotHdh02bkvXri2vKpqbiOnJnGz1kwKYM/MM1SVd7Mv6GZNCoZOMfktdeSCr1aR/x7Tz3L/2yfXvyoQ8V56K6uuIc1b9/0tb27uyCd8L2nu1ZtHZRKqmks7szo4aMyknvO+mg7lFhp45NHfnn5f+ctqFtWbFpRU4cdmImjZqUfvX9DnfVeJvUasl3v5v8278lp5+efPzjxTlh57yxY5OhQw993Xffnfzud8kppyRTpuy97pNO2v+6Fy9Onn66+PmjH00GDtzdvg8enPzkJ8mvfpV88IPJ9Om77yV2zv/854t1fOMbyQUXFLfBO3YkL71U/H7OnOL6/rrrkmOPLdr/nctOmFCsa8uOLenuLRKBYwYcs/s+r9qb9G5Nqq8/sn9nxer6Jf32f3Pws58lzz2XjBqVnHVW0ti4u97NzcX/Yu3aZPz4ZMSI3fdM1WpxjvvVr4p7qjPP3H3eqtWK7Z83L5k7NznhhOTLX05aWl7fji1J23Fdef/665MtS5IPXJc0HZ9sfL6YrvUkp92epZvX5Ee/+9Gu9xLaN7XnC6d/IWcff/Z+zx89PclDDyXt7clppyVjxuw+H1eryfvfX9wDbt1a1LWxcY9dVpe873373WWUSH19/QHLVGp9SAcWLVqUG2+8ca+RFTNnzsxNN92UM888c9fvvv71r6e1tTVXX311kuSll17KF7/4xTzxxBOZO3du2tvbc+uttyZJurq6MmnSpDz//PMHHWr8y29+mUrjMQdbbQAAAAAA4D1u4sSJByzTp5Eara2tWb16dXp6etLQ0JBarZbVq1entbX1DeVWrFixa3rlypW7Rme0trbmmWee2TVvxYoVOfbYYw860EiSD394Umpda5LezqRSX/RQrlWTxuHJwOP6sknvmlqtGJ7W3r47Nd1zk08//bBV7e21MyN7D/XcqVaLtLe3t9jnO5PcQ1WrJT/9afL880UiPnbs7l4Cvb3JRz5SpNz0wR/vSF74RvL+q5LTv528tjhZ9t+TpXOTEz6bnPn3h7zqb30r+du/Tc44I1mwYPf/ak87enfkZ3/6Wbb3bM/Wnq258AMXpm3o613qatVkx8akZ8vrvSdqxWiHfkOS/sMPuV4cBmueLHqQDDo+GdDy+rmjVnwfMXm/i3Z3J0uXJq+8UvRIHThwdw/M5uZ3tgfmiy8W7U1vbzJ6dHEM7+ypcvLJuwfMvJnly4vjfsOGopfQkCG76z1yZLH8Pm1dmfz2vyZdK5IPfjUZcGyy7umiJ1HdwHSfclteeilZs6ao0+DBu9d9zDFFL5okqdaq6e7tTl2lru+98Xq6kt5tSa23mK7rlzQ0HXCk0buibCOVarVkx4ake1NS7U7RltUn9QOLHsfvUdVq0cPstdeKc3ld3e5z+IknHsbReCt+nvzfbycD25LRM5LeLcmml4pr09YLk9bz97lorVb0NFy2rOgJN3Jk0QMt2X0dsWFD0d5s3bp3D7TBg5Pj30on+w2/T5b8sHgttXyyeG1tW118H/7hZOTH38LKD2B/jdWG9cV5f9OfkvedmQxoLs69tWpxjLZ8Itnwr0nX2mTQccXvUinmDzguaRpd9HTs2Zz0bi+2p1KX1DWm1m9YHv6nujz/fNLauvu6rVIp9vfO3on71P7TZPn9yZBxRU//HeuT7a8Uf+/4S5KhH9z3sp3tyZIfJD2bkpZzkrrGZNuaot5NJyat0w5lT75ruruLY3DndXR9fXE8HraRGEeI7u5ifybFsXgQHRH37/c3Jy//IDn5vyVjZiabliSb/1S8tk/4bDL8jH0vu/yh5Lf/JTnuguTs+5KtHcny+4pj/pj/kHzs/n0v29OVPHJCcTz/xxeL6+Ll9ycbfpds35B86BvJceP3vfxbOH8uXVr0mF6yJPnsZ4t7sp37sbExOe+84vrjkT89kjVb1mRr99Z8pO0j+diYj/XtD+3rXF+rJt17tjeV4vXdb2iy8FPJK08nZ/4oGXlWsv7ZZMu/J9tWJid8Lnn5h8m2tcnxM5L6puJ/VetNBrYmJ33hkPdJV1fy8MNFz/Fx4/beJ9Vq8pd/ecirPqDf/jb5h39INm5MLrmk6BFeX7/7fY89+r++Ue+25OUfJVteTprPLo6j7a8kqRajz4eelqz4X0nvjuI8lbriWrRWTYaOT445dd/r3vZK8uj4pGFIcuHzSePQ5MW/S1Y/UbTlU/4p6X/o3bq3bUtWrCgOj4aG4qtWK75OPvnN7zv3tHZt8uqrRY//+vriq1otevo3N+9nwXVPJ0//p+SYU5JJP0jq+yfrFiablxbH4Ae/fMjbtJc3O/57uopjdvurSeOw4pq8VktSSwafVPx94N3VtTZ56e+KJ3W0TE36NRX38LVacQ8/+uI9rpP3uLeu9CvawLrDO2JuXee6zF8yP431jenf0D8XjrvwgCOWd+rTSI0kufLKK/PpT386M2bMyGOPPZa5c+dm3rx5e5Vpb2/PrFmz8vDDD6e5uTnXXnttPvGJT2TWrFnZsmVLpk2blnvvvTdjx47N7bffnkGDBuWrX/1qX6oBHEn+/d7k2f+cHHtu8hffTPqPLBrcrR1J05j9v2FwAK++mvzwh8mvf50cd1wxfLKhobhgHDcuufTSt28z4IjV01k8vmznG4wNg99T4TUcVtWe4gahUv/64wC9NoB3We/2ZMvS14Pr7UUHnMZhyZDx++8I0LUq+dcvJa8+l4z5TDJ4bNGWbV+XDPtQMuqi/f/dDX9IOn5avAndNKb4u6kV1wrjrn07t7Bctq0t3qTv3pJUdxSBR//hyfAD9zrlbfbincmf5yUDRyVDTi4CgGp30r85GX/94a7doevpSjYtLl7DvV0pXndNRRg5+MTDXTuAd0WfQ42lS5fm5ptvzsaNG9PU1JQ5c+Zk3LhxmT17ds4999xMnTo1STJv3rzcc889qVarmTx5cm677bb0ez2qfuKJJ3LHHXekt7c348ePz5w5czLYg6Th6Na1Kln7VPHZDz2bi5uiAS1J26eSQSV+EDEAALyX1WrJjleL0QeVStJvWOJxzxyJatXdn0UIQKn1OdQAAAAAAAA4HETUAAAAAABAKQg1AAAAAACAUhBqAAAAAAAApSDUAAAAAAAASkGoAQAAAAAAlIJQAwAAAAAAKAWhBgAAAAAAUApCDQAAAAAAoBSEGgAAAAAAQCkINQAAAAAAgFIQagAAAAAAAKUg1AAAAAAAAEpBqAEAAAAAAJSCUAMAAAAAACgFoQYAAAAAAFAKQg0AAAAAAKAUhBoAAAAAAEApCDUAAAAAAIBSEGoAAAAAAAClINQAAAAAAABKQagBAAAAAACUglADAAAAAAAoBaEGAAAAAABQCkINAAAAAACgFIQaAAAAAABAKQg1AAAAAACAUhBqAAAAAAAApSDUAAAAAAAASkGoAQAAAAAAlIJQAwAAAAAAKAWhBgAAAAAAUAr/D4pb5no31qRRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s, e = 1050, 1250\n",
    "\n",
    "for eidx in range(10):\n",
    "    plt.figure(figsize=(16, 1.5))\n",
    "\n",
    "    ann_ = X_seqlet[0]\n",
    "    ann_ = ann_[ann_['example_idx'] == eidx]\n",
    "\n",
    "    plot_logo(X_attr[0][eidx], start=s, end=e, annotations=ann_, show_score=False, score_key='attribution')\n",
    "\n",
    "    plt.yticks(fontsize=8)\n",
    "    plt.ylim(-0.03, 0.11)\n",
    "    plt.xticks([], [])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dd48d6-d63b-4d0f-84e2-5f84284eca71",
   "metadata": {},
   "source": [
    "We can now go through and annotate each of these seqlet calls and then count the number of annotations in each example using the `count_annotations` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cdab42cd-475a-4afd-9a75-a73f91601a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 9958, 251])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tangermeme.annotate import count_annotations\n",
    "\n",
    "counts = []\n",
    "\n",
    "for i in range(2):\n",
    "    idxs, pvals = annotate_seqlets(X, X_seqlet[i], motifs)\n",
    "    seqlets = (X_seqlet[i]['example_idx'], idxs[:, 0])\n",
    "    \n",
    "    counts_ = count_annotations(seqlets, shape=(len(X), len(motif_names))).float()\n",
    "    counts.append(counts_)\n",
    "    \n",
    "counts = torch.stack(counts)\n",
    "counts.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c914b3-34b8-4cff-a373-572674987022",
   "metadata": {},
   "source": [
    "We can see how many seqlets are being called per instance with the two models on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10fcad99-74aa-422d-aa8d-e169b9f9a251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4251857995986938, 1.6104639768600464)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts[0].sum(dim=-1).mean().item(), counts[1].sum(dim=-1).mean().item()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03c1193-0187-428f-874b-fb4e993701df",
   "metadata": {},
   "source": [
    "Looks like the Beluga model is calling slightly more seqlets on average than the BPNet model is.\n",
    "\n",
    "We can then look at what motifs are being used to annotate seqlets for each of the two models to see whether there seem to be broad differences in the sorts of motifs that are being picked up on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64295257-e9b9-4212-aabd-a66bb0f18093",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MYC            \t2436.0\t976.0\n",
      "MYCN           \t1622.0\t405.0\n",
      "MXI1           \t1619.0\t383.0\n",
      "ZBTB11         \t653.0\t827.0\n",
      "HIF1A          \t913.0\t443.0\n",
      "GATA3          \t160.0\t678.0\n",
      "NRF1           \t276.0\t537.0\n",
      "GATA2          \t199.0\t537.0\n",
      "YY2            \t280.0\t399.0\n",
      "YY1            \t 43.0\t630.0\n",
      "IKZF2          \t142.0\t508.0\n",
      "GATA4          \t162.0\t469.0\n",
      "ATF4           \t427.0\t202.0\n",
      "KLF15          \t143.0\t458.0\n",
      "CTCFL          \t308.0\t281.0\n"
     ]
    }
   ],
   "source": [
    "motif_counts = counts.sum(dim=1).numpy()\n",
    "motif_idxs = motif_counts.sum(axis=0).argsort()[::-1]\n",
    "\n",
    "\n",
    "for idx in motif_idxs[:15]:\n",
    "    print(\"{:15}\\t{:5.5}\\t{:5.5}\".format(motif_names[idx], motif_counts[0][idx], motif_counts[1][idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1aa2ea17-138a-4e0d-a6b1-ed7b3edd0ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 2.5))\n",
    "plt.title(\"Annotated Seqlet Count\", fontsize=10)\n",
    "plt.bar(numpy.arange(15)-0.2, motif_counts[0][motif_idxs[:15]], width=0.4, facecolor=\"#e5507a\", label=\"BPNet\")\n",
    "plt.bar(numpy.arange(15)+0.2, motif_counts[1][motif_idxs[:15]], width=0.4, facecolor=\"#50dee5\", label=\"Beluga\")\n",
    "plt.xticks(range(15), motif_names[motif_idxs[:15]], rotation=90)\n",
    "\n",
    "plt.legend(loc=(1.01, 0.5))\n",
    "seaborn.despine(left=True)\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"tangermeme-fig2-seqlet-count.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166b9e69-d211-436b-8448-4de6da68e96f",
   "metadata": {},
   "source": [
    "Looks like there are clear differences between the two. The MYC BPNet model mostly yields seqlets that are annotated with MYC-related motifs, whereas the Beluga model seems to focus on YY1 ones and actually seems underrepresented for the MYC ones!\n",
    "\n",
    "Next, we can consider more sophisticated aspects of the logic in the genome. First, we can consider what sorts of pairs of seqlets appear together in the same examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60a42076-e1b0-4bc8-bbb1-ec6e4c8ab36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 251, 251])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tangermeme.annotate import pairwise_annotations\n",
    "\n",
    "pairwise_counts = []\n",
    "\n",
    "for i in range(2):\n",
    "    idxs, pvals = annotate_seqlets(X, X_seqlet[i], motifs)\n",
    "    seqlets = (X_seqlet[i]['example_idx'], idxs[:, 0])\n",
    "    \n",
    "    counts_ = pairwise_annotations(seqlets, shape=len(motif_names)).float()\n",
    "    pairwise_counts.append(counts_)\n",
    "    \n",
    "pairwise_counts = torch.stack(pairwise_counts)\n",
    "pairwise_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6dafe6e-3819-4676-9849-4cc85a2e54e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "cmap0 = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"white\", \"#e5507a\"])\n",
    "cmap1 = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"white\", \"#50dee5\"])\n",
    "\n",
    "idx0 = pairwise_counts[0].sum(dim=0).argsort().numpy()[::-1].copy()[:15]\n",
    "idx1 = pairwise_counts[1].sum(dim=0).argsort().numpy()[::-1].copy()[:15]\n",
    "\n",
    "plt.figure(figsize=(7, 3))\n",
    "plt.subplot(121)\n",
    "plt.title(\"BPNet (MYC)\", fontsize=10)\n",
    "plt.imshow(pairwise_counts[0][idx0][:, idx0], cmap=cmap0, aspect='auto')\n",
    "plt.yticks(range(len(idx0)), motif_names[idx0], fontsize=7)\n",
    "plt.xticks(range(len(idx0)), motif_names[idx0], rotation=90, fontsize=7)\n",
    "plt.colorbar(label=\"Count\")\n",
    "plt.grid(False)\n",
    "seaborn.despine(bottom=True, left=True)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"Beluga (MYC)\", fontsize=10)\n",
    "plt.imshow(pairwise_counts[1][idx1][:, idx1], cmap=cmap1, aspect='auto')\n",
    "plt.yticks(range(len(idx0)), motif_names[idx1], fontsize=7)\n",
    "plt.xticks(range(len(idx0)), motif_names[idx1], rotation=90, fontsize=7)\n",
    "plt.grid(False)\n",
    "seaborn.despine(bottom=True, left=True)\n",
    "\n",
    "plt.colorbar(label=\"Count\")\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"tangermeme-fig2-pairwise.pdf\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}