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@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data.h5ad filter=lfs diff=lfs merge=lfs -text

1_data.ipynb

@@ -0,0 +1,740 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9904be0b-9bdb-4a28-850a-8aec7786fab4",
+   "metadata": {},
+   "source": [
+    "# Process data for training CATLAS binary ATAC-seq model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83fd567a-5263-458a-97f9-6b9f2d3db6c1",
+   "metadata": {},
+   "source": [
+    "## Set up W&B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f6cc91ab-89b9-448b-a2ec-6d43dafe5e86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import wandb\n",
+    "import os\n",
+    "import anndata\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import bioframe as bf\n",
+    "from grelu.sequence.utils import resize\n",
+    "from grelu.data.preprocess import filter_blacklist, filter_chromosomes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4f87b690-04e0-4099-8536-15ebec7c5102",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mavantikalal\u001b[0m (\u001b[33mgrelu\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wandb.login(host=\"https://api.wandb.ai\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d4cfb6b2-2f49-4c0f-8c0d-9f402852d150",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_name='human-atac-catlas'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "52c22759-6d2c-4b41-bc1d-d2a3233d7c73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.19.7"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/code/github/gReLU-applications/catlas/wandb/run-20250306_000043-vphymp69</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/grelu/human-atac-catlas/runs/vphymp69' target=\"_blank\">prep</a></strong> to <a href='https://wandb.ai/grelu/human-atac-catlas' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/grelu/human-atac-catlas' target=\"_blank\">https://wandb.ai/grelu/human-atac-catlas</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/grelu/human-atac-catlas/runs/vphymp69' target=\"_blank\">https://wandb.ai/grelu/human-atac-catlas/runs/vphymp69</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run = wandb.init(\n",
+    "    entity='grelu', project=project_name, job_type='preprocessing', name='prep',\n",
+    "    settings=wandb.Settings(\n",
+    "        program_relpath='1_data.ipynb',\n",
+    "        program_abspath='/code/github/gReLU-applications/catlas/1_data.ipynb')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b3caead-af54-4e7a-88af-ac9e283f0750",
+   "metadata": {},
+   "source": [
+    "## Load CATLAS snATAC-seq matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0d70c98f-f45f-45a9-98ca-946c507c36c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!wget http://catlas.org/catlas_downloads/humantissues/cCRE_by_cell_type/matrix.tsv.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b909e398-8de7-41e2-83b7-3c32021cef5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.11/site-packages/anndata/utils.py:429: FutureWarning: Importing read_mtx from `anndata` is deprecated. Import anndata.io.read_mtx instead.\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(222, 1154611)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ad = anndata.read_mtx('matrix.tsv.gz').T\n",
+    "\n",
+    "# Prepare ad.obs\n",
+    "ad.obs = pd.read_table('http://catlas.org/catlas_downloads/humantissues/cCRE_by_cell_type/celltypes.txt.gz', header=None, names=['cell type'])\n",
+    "ad.obs_names = ad.obs['cell type']\n",
+    "\n",
+    "# Prepare ad.var\n",
+    "var = pd.read_table('http://catlas.org/catlas_downloads/humantissues/cCRE_hg38.tsv.gz')\n",
+    "var.columns = ['chrom', 'start', 'end', 'cre_class', 'in_fetal', 'in_adult', 'cre_module']\n",
+    "var[\"width\"] = (var.end - var.start).astype(int)\n",
+    "var.index = var.index.astype(str)\n",
+    "ad.var = var\n",
+    "\n",
+    "print(ad.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bd2463e1-8fa8-40ee-a1a2-b5b60516976d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  chrom   start     end          cre_class in_fetal in_adult  cre_module  \\\n",
+      "0  chr1    9955   10355  Promoter Proximal      yes      yes         146   \n",
+      "1  chr1   29163   29563           Promoter      yes      yes          37   \n",
+      "2  chr1   79215   79615             Distal       no      yes          75   \n",
+      "3  chr1  102755  103155             Distal       no      yes          51   \n",
+      "4  chr1  115530  115930             Distal      yes       no          36   \n",
+      "\n",
+      "   width  \n",
+      "0    400  \n",
+      "1    400  \n",
+      "2    400  \n",
+      "3    400  \n",
+      "4    400  \n",
+      "                                   cell type\n",
+      "cell type                                   \n",
+      "Follicular                        Follicular\n",
+      "Fibro General                  Fibro General\n",
+      "Acinar                                Acinar\n",
+      "T Lymphocyte 1 (CD8+)  T Lymphocyte 1 (CD8+)\n",
+      "T lymphocyte 2 (CD4+)  T lymphocyte 2 (CD4+)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ad.var.head())\n",
+    "print(ad.obs.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84f93545-eaf0-4d39-b16d-2e843367d626",
+   "metadata": {},
+   "source": [
+    "## Filter peaks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b5181175-02ce-4614-81a3-962d4705e4f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keeping 1121319 intervals\n"
+     ]
+    }
+   ],
+   "source": [
+    "ad = filter_chromosomes(ad, 'autosomes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "db96deff-fdec-4cc3-8c39-ad2541394a70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keeping 1121319 intervals\n"
+     ]
+    }
+   ],
+   "source": [
+    "ad = filter_blacklist(ad, genome='hg38')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "517b43dc-8e4f-4f44-a6de-e4b459dca5af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(222, 1121319)\n",
+      "(204, 1121319)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Drop peaks that are only accessible in few cell types\n",
+    "print(ad.shape)\n",
+    "ad = ad[ad.X.mean(axis=1) > .03, :]\n",
+    "print(ad.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c53c564-17ed-42e8-bd62-2e4ee182a082",
+   "metadata": {},
+   "source": [
+    "## Resize peaks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "493f5905-02bb-477b-ae5f-abde1572962d",
+   "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>chrom</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>cre_class</th>\n",
+       "      <th>in_fetal</th>\n",
+       "      <th>in_adult</th>\n",
+       "      <th>cre_module</th>\n",
+       "      <th>width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10055</td>\n",
+       "      <td>10255</td>\n",
+       "      <td>Promoter Proximal</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>146</td>\n",
+       "      <td>400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>29263</td>\n",
+       "      <td>29463</td>\n",
+       "      <td>Promoter</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>37</td>\n",
+       "      <td>400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>79315</td>\n",
+       "      <td>79515</td>\n",
+       "      <td>Distal</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>75</td>\n",
+       "      <td>400</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chrom  start    end          cre_class in_fetal in_adult  cre_module  width\n",
+       "0  chr1  10055  10255  Promoter Proximal      yes      yes         146    400\n",
+       "1  chr1  29263  29463           Promoter      yes      yes          37    400\n",
+       "2  chr1  79315  79515             Distal       no      yes          75    400"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "seq_len = 200\n",
+    "ad.var = resize(ad.var, seq_len)\n",
+    "ad.var.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26745890-cb7f-4f8c-9801-133c66a0ef96",
+   "metadata": {},
+   "source": [
+    "## Load enformer splits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "85bc5e37-ba54-4e34-8826-96e266c7ec87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
+     ]
+    },
+    {
+     "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>chrom</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>split</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr18</td>\n",
+       "      <td>895618</td>\n",
+       "      <td>1092226</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>113598179</td>\n",
+       "      <td>113794787</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>18394952</td>\n",
+       "      <td>18591560</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chrom      start        end  split\n",
+       "0  chr18     895618    1092226  train\n",
+       "1   chr4  113598179  113794787  train\n",
+       "2  chr11   18394952   18591560  train"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "artifact = run.use_artifact('enformer/human_intervals:latest')\n",
+    "dir = artifact.download()\n",
+    "enformer_intervals = pd.read_table(os.path.join(dir, \"data.tsv\"))\n",
+    "enformer_intervals.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "842a7aa6-e8ae-4969-8bba-72bb532575c9",
+   "metadata": {},
+   "source": [
+    "## Split peaks based on their overlap with enformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d3c8c95a-9386-4c6e-9f21-d4370dac178c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ad.var = ad.var.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "57727d0b-6187-4640-9947-d247d4225166",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ad.var['cre_idx'] = range(len(ad.var))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "04f3e704-cd4d-44ea-bcc1-d4c9dc1db1db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "split_\n",
+       "train         966953\n",
+       "test           72507\n",
+       "valid          71703\n",
+       "None            9670\n",
+       "testtrain        222\n",
+       "trainvalid       169\n",
+       "testvalid         95\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "overlaps = bf.overlap(ad.var, enformer_intervals, how='left')\n",
+    "overlaps.split_ = overlaps.split_.fillna('None')\n",
+    "\n",
+    "overlaps = overlaps.groupby('cre_idx').split_.apply(lambda x: ''.join(list(np.unique(x))))\n",
+    "overlaps.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "72775c43-2996-456d-879a-e077b11ba736",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert np.all(overlaps.index == ad.var.cre_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a317db18-c0e9-425d-86da-98cd6bdac3e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train    977014\n",
+       "test      72507\n",
+       "valid     71798\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_splits = np.array(['train'] * len(overlaps))\n",
+    "new_splits[[(('valid' in x) and ('train' not in x)) for x in overlaps]] = 'valid'\n",
+    "new_splits[[(('test' in x) and ('train' not in x) and ('valid' not in x)) for x in overlaps]] = 'test'\n",
+    "pd.Series(new_splits).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3c0f944d-ee42-45a9-ad3b-41e4a53aba10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ad.var['enformer_split'] = overlaps\n",
+    "ad.var['split'] = new_splits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04ff9b90-956d-4150-bbf8-f5c29826f6fb",
+   "metadata": {},
+   "source": [
+    "## Save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e947777a-3a1f-4e07-995f-c0e85e9dedf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ad.write_h5ad('preprocessed.h5ad')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e39fb051-0a80-4832-a962-ab62e6995e95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Artifact dataset>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "artifact = wandb.Artifact('dataset', type='dataset')\n",
+    "artifact.add_file(local_path='preprocessed.h5ad', name='data.h5ad')\n",
+    "run.log_artifact(artifact)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "573db082-362c-4caa-a7d7-53e1dff271e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">prep</strong> at: <a href='https://wandb.ai/grelu/human-atac-catlas/runs/vphymp69' target=\"_blank\">https://wandb.ai/grelu/human-atac-catlas/runs/vphymp69</a><br> View project at: <a href='https://wandb.ai/grelu/human-atac-catlas' target=\"_blank\">https://wandb.ai/grelu/human-atac-catlas</a><br>Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20250306_000043-vphymp69/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run.finish()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e408e2f-babd-491b-8287-f131b6a82c7e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

README.md

@@ -1,3 +1,52 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- tabular-classification
+tags:
+- biology
+- genomics
+pretty_name: "CATlas Human Enhancer Binary Matrix"
+size_categories:
+- 1M<n<10M
+---
+
+# human-catlas-atac-data
+
+## Dataset Summary
+This dataset provides a binary accessibility matrix of candidate Cis-Regulatory Elements (cCREs) across human cell types. It is derived from the CATlas project (https://decoder-genetics.wustl.edu/catlasv1/catlas_humanenhancer/#!/). The matrix identifies which genomic regions are accessible (1) or inaccessible (0) across 204 distinct cell types. The data is derived from https://decoder-genetics.wustl.edu/catlasv1/humanenhancer/data/cCRE_by_cell_type/. The hg38 genome build was used.
+
+## Repository Content
+1. `data.h5ad`: The main dataset stored in AnnData format.
+2. `1_data.ipynb`: Jupyter notebook containing the preprocessing steps used to generate the `.h5ad` file.
+
+## Dataset Structure
+
+### AnnData Object Dimensions
+- **n_obs (Cell Types):** 204
+- **n_vars (cCREs):** 1,121,319
+
+### Data Fields
+- **`.X`**: A sparse binary matrix where rows are cell types and columns are genomic regions (cCREs).
+- **`.obs` (Cell Type Metadata):**
+  - `cell type`: The descriptive name of the human cell type/cluster.
+- **`.var` (Genomic Feature Metadata):**
+  - `chrom`, `start`, `end`: Genomic coordinates (hg38).
+  - `cre_class`: Classification of the regulatory element.
+  - `in_fetal` / `in_adult`: indicators of activity in developmental stages.
+  - `cre_module`: Associated regulatory module.
+  - `enformer_split`: Overlap with the data splits used for training the Enformer model.
+  - `split`: Splits used for downstream modeling (training/validation/test).
+
+## Usage
+
+```python
+import anndata as ad
+from huggingface_hub import hf_hub_download
+
+file_path = hf_hub_download(
+    repo_id="Genentech/human-atac-catlas-data", 
+    filename="data.h5ad"
+)
+
+ad = anndata.read_h5ad(file_path)
+```

data.h5ad

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50059ed67b84d0237d385a6dc89e95bdaeb8b877bd75b269c25df777de8d19a5
+size 187877866