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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.csv filter=lfs diff=lfs merge=lfs -text

1_data.ipynb

@@ -0,0 +1,624 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6b984a20-40eb-4c08-8494-ebb607e91b94",
+   "metadata": {},
+   "source": [
+    "# Process data for chromHMM multiclass classification model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df5cf6ce-dd2c-445f-a5b3-a13e9fd07d17",
+   "metadata": {},
+   "source": [
+    "## Set up wandb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "35c13d53-f8f7-45c9-a7f0-c0e92d7654c9",
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "import wandb\n",
+    "import anndata\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "wandb.login(host=\"https://api.wandb.ai\")\n",
+    "project_name = 'human-chromhmm-fullstack'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "37ecd399-3fd9-4687-9d7d-95638f529dc1",
+   "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/chromhmm/wandb/run-20250306_045811-8fux0bft</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-chromhmm-fullstack/runs/8fux0bft' target=\"_blank\">prep</a></strong> to <a href='https://wandb.ai/grelu/human-chromhmm-fullstack' 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-chromhmm-fullstack' target=\"_blank\">https://wandb.ai/grelu/human-chromhmm-fullstack</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-chromhmm-fullstack/runs/8fux0bft' target=\"_blank\">https://wandb.ai/grelu/human-chromhmm-fullstack/runs/8fux0bft</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/chromhmm/1_data.ipynb')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4b88d95-a65a-4e75-a309-032faf846c03",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1527622f-94c0-4377-b125-407a0eef4bec",
+   "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>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10400</td>\n",
+       "      <td>2_GapArtf2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10400</td>\n",
+       "      <td>10600</td>\n",
+       "      <td>27_Acet1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10600</td>\n",
+       "      <td>10800</td>\n",
+       "      <td>38_EnhWk4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10800</td>\n",
+       "      <td>12800</td>\n",
+       "      <td>1_GapArtf1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>12800</td>\n",
+       "      <td>13000</td>\n",
+       "      <td>38_EnhWk4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chrom  start    end       state\n",
+       "0  chr1  10000  10400  2_GapArtf2\n",
+       "1  chr1  10400  10600    27_Acet1\n",
+       "2  chr1  10600  10800   38_EnhWk4\n",
+       "3  chr1  10800  12800  1_GapArtf1\n",
+       "4  chr1  12800  13000   38_EnhWk4"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chromhmm = pd.read_table('https://public.hoffman2.idre.ucla.edu/ernst/2K9RS//full_stack/full_stack_annotation_public_release/hg38/hg38_genome_100_segments.bed.gz', header=None)\n",
+    "chromhmm.columns = ['chrom', 'start', 'end', 'state']\n",
+    "chromhmm.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91bec5b4-201c-4c66-992f-6b54ba5bc71e",
+   "metadata": {},
+   "source": [
+    "## Process data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "394d7f20-121f-4da7-886e-7b4184d2128d",
+   "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keeping 5845850 intervals\n",
+      "Keeping 5809104 intervals\n"
+     ]
+    }
+   ],
+   "source": [
+    "from grelu.data.preprocess import filter_chromosomes, filter_blacklist\n",
+    "from grelu.sequence.utils import resize\n",
+    "\n",
+    "chromhmm = filter_chromosomes(chromhmm, include='autosomes')\n",
+    "chromhmm = resize(chromhmm, 1024)\n",
+    "chromhmm = filter_blacklist(chromhmm, 'hg38')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a93565f-d819-44d4-911c-d8d1f5d7a052",
+   "metadata": {},
+   "source": [
+    "## Get coarse-grained state labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "44474b8b-7f33-4608-95cb-b77e87fb4840",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Quies      1485576\n",
+       "Acet        639669\n",
+       "EnhA        613794\n",
+       "ReprPC      610147\n",
+       "Tx          561526\n",
+       "EnhWk       543113\n",
+       "HET         521161\n",
+       "TxWk        254518\n",
+       "TxEnh       190465\n",
+       "TxEx        121833\n",
+       "PromF        88429\n",
+       "GapArtf      51474\n",
+       "BivProm      48242\n",
+       "znf          34146\n",
+       "TSS          24402\n",
+       "DNase        20609\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chromhmm['state'] = [\n",
+    "    x.split('_')[1][:-1] for x in chromhmm.state\n",
+    "]\n",
+    "chromhmm.loc[chromhmm.state.isin(['EnhA1', 'EnhA2']), 'state'] = 'EnhA'\n",
+    "\n",
+    "chromhmm['state'] = chromhmm['state'].astype('category')\n",
+    "chromhmm.state.value_counts()  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3eb00f57-972a-4b07-badd-d8edf20e9d9e",
+   "metadata": {},
+   "source": [
+    "## Load Enformer splits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "89717895-524f-4ea5-bacc-4914e8893096",
+   "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": 6,
+     "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": "e19fd34a-5797-49f3-a838-c3e2d0342be0",
+   "metadata": {},
+   "source": [
+    "## Split regions based on their overlap with Enformer split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "86d33170-ccb8-4245-9e7e-c12f1ca9fb2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chromhmm = chromhmm.reset_index(drop=True)\n",
+    "chromhmm['interval_idx'] = range(len(chromhmm))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6d7628c5-3e70-4835-8c04-b6d193490419",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "split_\n",
+       "train         4963283\n",
+       "test           402392\n",
+       "valid          363619\n",
+       "None            76215\n",
+       "testtrain        1606\n",
+       "trainvalid       1221\n",
+       "testvalid         768\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import bioframe as bf\n",
+    "overlaps = bf.overlap(chromhmm, enformer_intervals, how='left')\n",
+    "overlaps.split_ = overlaps.split_.fillna('None')\n",
+    "\n",
+    "overlaps = overlaps.groupby('interval_idx').split_.apply(lambda x: ''.join(list(np.unique(x))))\n",
+    "overlaps.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "562e9b6e-aaf3-4c11-82fb-41f8b69d94cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert np.all(overlaps.index == chromhmm.interval_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a43a1227-154f-47f7-90fd-a7fd5f59ce3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train    5042325\n",
+       "test      402392\n",
+       "valid     364387\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "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": 11,
+   "id": "04c0bede-0f36-4d07-bd81-7988ca856c09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chromhmm['enformer_split'] = overlaps\n",
+    "chromhmm['split'] = new_splits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dbf9d8a-aa8f-45b0-a572-e7e3a6abb607",
+   "metadata": {},
+   "source": [
+    "## Save dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "801efe31-dfbc-4052-81bf-9b1de399c8f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chromhmm.to_csv('chromhmm.csv.gz', index=False) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "be914f0c-da00-4b1a-820b-8d61d6725b70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Artifact dataset>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "artifact = wandb.Artifact('dataset', type='dataset')\n",
+    "artifact.add_file(local_path='chromhmm.csv.gz', name='data.csv.gz')\n",
+    "run.log_artifact(artifact)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7afe340f-d1cd-43d1-a6ce-0db10c2e2e63",
+   "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-chromhmm-fullstack/runs/8fux0bft' target=\"_blank\">https://wandb.ai/grelu/human-chromhmm-fullstack/runs/8fux0bft</a><br> View project at: <a href='https://wandb.ai/grelu/human-chromhmm-fullstack' target=\"_blank\">https://wandb.ai/grelu/human-chromhmm-fullstack</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_045811-8fux0bft/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run.finish()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46094f9a-324c-45ef-acf1-051c7d7ebe3d",
+   "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 +1,46 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- tabular-classification
+tags:
+- biology
+- genomics
+pretty_name: "ChromHMM fullstack annotation of the human genome"
+size_categories:
+- 1M<n<10M
+---
+
+# human-chromhmm-fullstack
+
+## Dataset Summary
+This dataset provides a multi-class annotation of genomic regions across the hg38 genome. It is derived from the ChromHMM fullstack annotation (Vu & Ernst, 2022; https://doi.org/10.1186/s13059-021-02572-z). Genomic regions are classified into 16 states. The data is derived from https://public.hoffman2.idre.ucla.edu/ernst/2K9RS//full_stack/full_stack_annotation_public_release/hg38/hg38_genome_100_segments.bed.gz.
+
+## Repository Content
+1. `data.csv`: The main dataset stored in comma-separated tabular format.
+2. `1_data.ipynb`: Jupyter notebook containing the preprocessing steps used to generate the `.csv` file.
+
+## Dataset Structure
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| chrom | string | Chromosome name (e.g., chr1) |
+| start | int | Start coordinate of the genomic interval |
+| end | int | End coordinate of the genomic interval |
+| state | string | Chromatin state annotation (e.g., EnhWk, Quies) |
+| interval_idx | int | Unique numerical index for the specific genomic interval |
+| enformer_split | string | Overlap with the data splits used for training the Enformer model |
+| split | string |Splits used for downstream modeling (training/validation/test) |
+
+## Usage
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+file_path = hf_hub_download(
+    repo_id="Genentech/human-chromhmm-fullstack-data", 
+    filename="data.csv"
+)
+
+df = pd.read_csv(file_path)
+```

data.csv

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