Add notebook for loading data

tutorials/loading_data.ipynb

@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ee3ff49b",
+   "metadata": {},
+   "source": [
+    "# Tutorial: Creating an AnnData object from Tahoe-100M dataset\n",
+    "This notebook demonstrates how to create an `AnnData` object using the Tahoe-100M dataset on Hugging Face."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4df26b4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install datasets anndata scipy pandas duckdb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f5566528",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3/dist-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": [
+    "from datasets import load_dataset\n",
+    "from scipy.sparse import csr_matrix\n",
+    "import anndata\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601a878d",
+   "metadata": {},
+   "source": [
+    "## Helper function\n",
+    "Define a function to construct the AnnData object from a data generator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e72e75ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def create_anndata_from_generator(generator, gene_vocab, sample_size=None):\n",
+    "    sorted_vocab_items = sorted(gene_vocab.items())\n",
+    "    token_ids, gene_names = zip(*sorted_vocab_items)\n",
+    "    token_id_to_col_idx = {token_id: idx for idx, token_id in enumerate(token_ids)}\n",
+    "\n",
+    "    data, indices, indptr = [], [], [0]\n",
+    "    obs_data = []\n",
+    "\n",
+    "    for i, cell in enumerate(generator):\n",
+    "        if sample_size is not None and i >= sample_size:\n",
+    "            break\n",
+    "        genes = cell['genes']\n",
+    "        expressions = cell['expressions']\n",
+    "        if expressions[0] < 0: \n",
+    "            genes = genes[1:]\n",
+    "            expressions = expressions[1:]\n",
+    "\n",
+    "        col_indices = [token_id_to_col_idx[gene] for gene in genes if gene in token_id_to_col_idx]\n",
+    "        valid_expressions = [expr for gene, expr in zip(genes, expressions) if gene in token_id_to_col_idx]\n",
+    "\n",
+    "        data.extend(valid_expressions)\n",
+    "        indices.extend(col_indices)\n",
+    "        indptr.append(len(data))\n",
+    "\n",
+    "        obs_entry = {k: v for k, v in cell.items() if k not in ['genes', 'expressions']}\n",
+    "        obs_data.append(obs_entry)\n",
+    "\n",
+    "    expr_matrix = csr_matrix((data, indices, indptr), shape=(len(indptr) - 1, len(gene_names)))\n",
+    "    obs_df = pd.DataFrame(obs_data)\n",
+    "\n",
+    "    adata = anndata.AnnData(X=expr_matrix, obs=obs_df)\n",
+    "    adata.var.index = pd.Index(gene_names, name='ensembl_id')\n",
+    "\n",
+    "    return adata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d3f44c4",
+   "metadata": {},
+   "source": [
+    "## Load Tahoe-100M dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "66329f53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Stream the main dataset\n",
+    "tahoe_100m_ds = load_dataset(\"vevotx/Tahoe-100M\", streaming=True, split=\"train\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4de64af6",
+   "metadata": {},
+   "source": [
+    "## Load gene metadata to construct gene vocabulary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "482f8461",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "gene_metadata = load_dataset(\"vevotx/Tahoe-100M\", name=\"gene_metadata\", split=\"train\")\n",
+    "gene_vocab = {entry[\"token_id\"]: entry[\"ensembl_id\"] for entry in gene_metadata}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18d80c71",
+   "metadata": {},
+   "source": [
+    "## Create AnnData object from dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cf137497",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3/dist-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 1000 × 62710\n",
+       "    obs: 'drug', 'sample', 'BARCODE_SUB_LIB_ID', 'cell_line_id', 'moa-fine', 'canonical_smiles', 'pubchem_cid', 'plate'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "adata = create_anndata_from_generator(tahoe_100m_ds, gene_vocab, sample_size=1000)\n",
+    "adata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0069a2bb",
+   "metadata": {},
+   "source": [
+    "## Inspect metadata (`obs`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9a3180c0",
+   "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>drug</th>\n",
+       "      <th>sample</th>\n",
+       "      <th>BARCODE_SUB_LIB_ID</th>\n",
+       "      <th>cell_line_id</th>\n",
+       "      <th>moa-fine</th>\n",
+       "      <th>canonical_smiles</th>\n",
+       "      <th>pubchem_cid</th>\n",
+       "      <th>plate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8-Hydroxyquinoline</td>\n",
+       "      <td>smp_1783</td>\n",
+       "      <td>01_001_052-lib_1105</td>\n",
+       "      <td>CVCL_0480</td>\n",
+       "      <td>unclear</td>\n",
+       "      <td>C1=CC2=C(C(=C1)O)N=CC=C2</td>\n",
+       "      <td>1923.0</td>\n",
+       "      <td>plate4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8-Hydroxyquinoline</td>\n",
+       "      <td>smp_1783</td>\n",
+       "      <td>01_001_105-lib_1105</td>\n",
+       "      <td>CVCL_0546</td>\n",
+       "      <td>unclear</td>\n",
+       "      <td>C1=CC2=C(C(=C1)O)N=CC=C2</td>\n",
+       "      <td>1923.0</td>\n",
+       "      <td>plate4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8-Hydroxyquinoline</td>\n",
+       "      <td>smp_1783</td>\n",
+       "      <td>01_001_165-lib_1105</td>\n",
+       "      <td>CVCL_1717</td>\n",
+       "      <td>unclear</td>\n",
+       "      <td>C1=CC2=C(C(=C1)O)N=CC=C2</td>\n",
+       "      <td>1923.0</td>\n",
+       "      <td>plate4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8-Hydroxyquinoline</td>\n",
+       "      <td>smp_1783</td>\n",
+       "      <td>01_003_094-lib_1105</td>\n",
+       "      <td>CVCL_1717</td>\n",
+       "      <td>unclear</td>\n",
+       "      <td>C1=CC2=C(C(=C1)O)N=CC=C2</td>\n",
+       "      <td>1923.0</td>\n",
+       "      <td>plate4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8-Hydroxyquinoline</td>\n",
+       "      <td>smp_1783</td>\n",
+       "      <td>01_003_164-lib_1105</td>\n",
+       "      <td>CVCL_1056</td>\n",
+       "      <td>unclear</td>\n",
+       "      <td>C1=CC2=C(C(=C1)O)N=CC=C2</td>\n",
+       "      <td>1923.0</td>\n",
+       "      <td>plate4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 drug    sample   BARCODE_SUB_LIB_ID cell_line_id moa-fine  \\\n",
+       "0  8-Hydroxyquinoline  smp_1783  01_001_052-lib_1105    CVCL_0480  unclear   \n",
+       "1  8-Hydroxyquinoline  smp_1783  01_001_105-lib_1105    CVCL_0546  unclear   \n",
+       "2  8-Hydroxyquinoline  smp_1783  01_001_165-lib_1105    CVCL_1717  unclear   \n",
+       "3  8-Hydroxyquinoline  smp_1783  01_003_094-lib_1105    CVCL_1717  unclear   \n",
+       "4  8-Hydroxyquinoline  smp_1783  01_003_164-lib_1105    CVCL_1056  unclear   \n",
+       "\n",
+       "           canonical_smiles pubchem_cid   plate  \n",
+       "0  C1=CC2=C(C(=C1)O)N=CC=C2      1923.0  plate4  \n",
+       "1  C1=CC2=C(C(=C1)O)N=CC=C2      1923.0  plate4  \n",
+       "2  C1=CC2=C(C(=C1)O)N=CC=C2      1923.0  plate4  \n",
+       "3  C1=CC2=C(C(=C1)O)N=CC=C2      1923.0  plate4  \n",
+       "4  C1=CC2=C(C(=C1)O)N=CC=C2      1923.0  plate4  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs.head()"
+   ]
+  }
+ ],
+ "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": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}