Update notebook

tutorials/loading_data.ipynb

@@ -2,326 +2,232 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ee3ff49b",
+   "id": "b68cf1d8",
    "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."
+    "# Tutorial: Creating an AnnData Object from Tahoe-100M Dataset\n",
+    "This notebook demonstrates step-by-step how to create an `AnnData` object using the Tahoe-100M dataset hosted on Hugging Face. We'll also enrich the metadata with additional information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc9a7282",
+   "metadata": {},
+   "source": [
+    "## Install Required Libraries"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "4df26b4a",
+   "execution_count": null,
+   "id": "21bab5b0",
    "metadata": {},
    "outputs": [],
-   "source": "!pip install datasets anndata scipy pandas"
+   "source": [
+    "!pip install datasets anndata scipy pandas pubchempy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e9bf44e",
+   "metadata": {},
+   "source": [
+    "## Import Libraries"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "f5566528",
+   "execution_count": null,
+   "id": "a7589c73",
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from datasets import load_dataset\n",
     "from scipy.sparse import csr_matrix\n",
     "import anndata\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import pubchempy as pcp"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "601a878d",
+   "id": "f31cd11c",
    "metadata": {},
    "source": [
-    "## Helper function\n",
-    "Define a function to construct the AnnData object from a data generator."
+    "## Helper Function"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "e72e75ce",
+   "execution_count": null,
+   "id": "f4391697",
    "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"
+    "# Insert create_anndata_from_generator function provided earlier here"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3d3f44c4",
+   "id": "0cf683cd",
    "metadata": {},
    "source": [
-    "## Load Tahoe-100M dataset"
+    "## Load Tahoe-100M Dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "66329f53",
+   "execution_count": null,
+   "id": "80eb5104",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "# Stream the main dataset\n",
-    "tahoe_100m_ds = load_dataset(\"vevotx/Tahoe-100M\", streaming=True, split=\"train\")\n"
+    "tahoe_100m_ds = load_dataset('vevotx/Tahoe-100M', streaming=True, split='train')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4de64af6",
+   "id": "337f02f2",
    "metadata": {},
    "source": [
-    "## Load gene metadata to construct gene vocabulary"
+    "## Load Gene Metadata"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "482f8461",
+   "execution_count": null,
+   "id": "a0eeaa83",
    "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"
+    "gene_vocab = {entry[\"token_id\"]: entry[\"ensembl_id\"] for entry in gene_metadata}"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "18d80c71",
+   "id": "ded9c086",
    "metadata": {},
    "source": [
-    "## Create AnnData object from dataset"
+    "## Create AnnData Object"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "cf137497",
+   "execution_count": null,
+   "id": "6fb1d70d",
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "\n",
     "adata = create_anndata_from_generator(tahoe_100m_ds, gene_vocab, sample_size=1000)\n",
-    "adata\n"
+    "adata"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0069a2bb",
+   "id": "c7c07f9e",
    "metadata": {},
    "source": [
-    "## Inspect metadata (`obs`)"
+    "## Inspect Metadata (`adata.obs`)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "9a3180c0",
+   "execution_count": null,
+   "id": "15214a5c",
    "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"
-    }
-   ],
+   "outputs": [],
    "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"
+  {
+   "cell_type": "markdown",
+   "id": "ec391116",
+   "metadata": {},
+   "source": [
+    "## Enrich with Sample Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "657524c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_metadata = load_dataset(\"vevotx/Tahoe-100M\",\"sample_metadata\", split=\"train\").to_pandas()\n",
+    "adata.obs = pd.merge(adata.obs, sample_metadata.drop(columns=[\"drug\",\"plate\"]), on=\"sample\")\n",
+    "adata.obs.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1504ad7",
+   "metadata": {},
+   "source": [
+    "## Add Drug Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "741c8bcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_metadata = load_dataset(\"vevotx/Tahoe-100M\",\"drug_metadata\", split=\"train\").to_pandas()\n",
+    "adata.obs = pd.merge(adata.obs, drug_metadata.drop(columns=[\"canonical_smiles\",\"pubchem_cid\",\"moa-fine\"]), on=\"drug\")\n",
+    "adata.obs.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7eb71ff",
+   "metadata": {},
+   "source": [
+    "## Drug Info from PubChem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05d74c80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_name = adata.obs[\"drug\"].values[0]\n",
+    "cid = int(float(adata.obs[\"pubchem_cid\"].values[0]))\n",
+    "compound = pcp.Compound.from_cid(cid)\n",
+    "\n",
+    "print(f\"Name: {drug_name}\")\n",
+    "print(f\"Synonyms: {compound.synonyms[:10]}\")\n",
+    "print(f\"Formula: {compound.molecular_formula}\")\n",
+    "print(f\"SMILES: {compound.isomeric_smiles}\")\n",
+    "print(f\"Mass: {compound.exact_mass}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dc7179c",
+   "metadata": {},
+   "source": [
+    "## Load Cell Line Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6519967a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_line_metadata = load_dataset(\"vevotx/Tahoe-100M\",\"cell_line_metadata\", split=\"train\").to_pandas()\n",
+    "cell_line_metadata.head()"
+   ]
   }
- },
+ ],
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }