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"cells": [
{
"cell_type": "markdown",
"id": "29283b5f73ff",
"metadata": {},
"source": [
"# Tutorial: Creating an AnnData Object from EmeraldBay Dataset\n",
"This notebook is intented for users who are familiar with the anndata format for single-cell data. We'll walk through how to parse records in the huggingface dataset format and convert between the two."
]
},
{
"cell_type": "markdown",
"id": "ba072a78e3a4",
"metadata": {},
"source": [
"## Install Required Libraries"
]
},
{
"cell_type": "code",
"id": "b9362f7078f7",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"!pip install datasets anndata scipy pandas pubchempy"
]
},
{
"cell_type": "markdown",
"id": "2bc895079924",
"metadata": {},
"source": [
"## Import Libraries"
]
},
{
"cell_type": "code",
"id": "e9ba03a3bd79",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"from scipy.sparse import csr_matrix\n",
"import anndata\n",
"import pandas as pd\n",
"import pubchempy as pcp"
]
},
{
"cell_type": "markdown",
"id": "c29b2fa43400",
"metadata": {},
"source": [
"## Mapping records to anndata\n",
"\n",
"This function takes in a generator that emits records from the EmeraldBay huggingface dataset and returns an anndata object. Use the `sample_size` argument to specify the number of records you need. You can also create a new generator using the `dataset.filter` function to only emit records that match a certain filter (eg: for a specific drug/sample/cell line).\n",
"\n",
"If you'd like to create a DataLoader for an ML training application, it's likely best to use the data in it's native format without interfacing with anndata."
]
},
{
"cell_type": "code",
"id": "687310bbaa90",
"metadata": {},
"execution_count": null,
"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": "2f9b054dd5d1",
"metadata": {},
"source": [
"## Load EmeraldBay Dataset"
]
},
{
"cell_type": "code",
"id": "00fb90b58885",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"emeraldbay_ds = load_dataset('tahoebio/EmeraldBay', streaming=True, split='train')"
]
},
{
"cell_type": "markdown",
"id": "eb4d67366482",
"metadata": {},
"source": [
"## Load Gene Metadata\n",
"\n",
"The gene metadata contains the mapping between the integer token IDs used in the dataset and standard identifiers for genes (ensembl IDs and HGNC gene symbols)"
]
},
{
"cell_type": "code",
"id": "f69fe5d5a0c0",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"gene_metadata = load_dataset(\"tahoebio/EmeraldBay\", name=\"gene_metadata\", split=\"train\")\n",
"gene_vocab = {entry[\"token_id\"]: entry[\"ensembl_id\"] for entry in gene_metadata}"
]
},
{
"cell_type": "markdown",
"id": "e2e508d19e5e",
"metadata": {},
"source": [
"## Create AnnData Object"
]
},
{
"cell_type": "code",
"id": "8adcb383b952",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"adata = create_anndata_from_generator(emeraldbay_ds, gene_vocab, sample_size=1000)\n",
"adata"
]
},
{
"cell_type": "markdown",
"id": "38b1fc1559fc",
"metadata": {},
"source": [
"## Inspect Metadata (`adata.obs`)"
]
},
{
"cell_type": "code",
"id": "2403918666e7",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"adata.obs.head()"
]
},
{
"cell_type": "markdown",
"id": "d7fcc00b9b45",
"metadata": {},
"source": "## Add Drug Metadata\n\nThe drug metadata contains additional information for the compounds used in EmeraldBay. See the dataset card for more information about how this information was generated."
},
{
"cell_type": "code",
"id": "188820081704",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": "drug_metadata = load_dataset(\"tahoebio/EmeraldBay\",\"drug_metadata\", split=\"train\").to_pandas()\nadata.obs = pd.merge(adata.obs, drug_metadata, on=\"drug\", how=\"left\")\nadata.obs.head()"
},
{
"cell_type": "markdown",
"id": "8ab3abb59bbf",
"metadata": {},
"source": [
"## Drug Info from PubChem\n",
"\n",
"We also provide the pubchem IDs for the compounds in EmeraldBay, this can be used to querry additional information as needed."
]
},
{
"cell_type": "code",
"id": "1550c8343527",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": "drug_name = adata.obs[\"drug\"].values[0]\ncid = int(float(adata.obs[\"pubchem-cid\"].values[0]))\ncompound = pcp.Compound.from_cid(cid)\n\nprint(f\"Name: {drug_name}\")\nprint(f\"Synonyms: {compound.synonyms[:10]}\")\nprint(f\"Formula: {compound.molecular_formula}\")\nprint(f\"SMILES: {compound.isomeric_smiles}\")\nprint(f\"Mass: {compound.exact_mass}\")"
},
{
"cell_type": "markdown",
"id": "6eb51c1c4b78",
"metadata": {},
"source": [
"## Load Cell Line Metadata\n",
"The cell-line metadata contains additional identifiers for the cell-lines used in EmeraldBay (eg: Depmap-IDs) as well as a curated list of driver mutations for each cell line. This information can be used for instance to train genotype aware models on the EmeraldBay data."
]
},
{
"cell_type": "code",
"id": "c102249f7cc9",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"cell_line_metadata = load_dataset(\"tahoebio/EmeraldBay\",\"cell_line_metadata\", split=\"train\").to_pandas()\n",
"cell_line_metadata.head()"
]
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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