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_sources/notebooks/api_demo/census_citation_generation.ipynb.txt
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "88812eae-6b46-48b4-a1e4-c468657d8480", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Generating citations for Census slices\n", | ||
| "\n", | ||
| "This notebook demonstrates how to generate a citation string for all datasets contained in a Census slice.\n", | ||
| "\n", | ||
| "**Contents**\n", | ||
| "\n", | ||
| "1. Requirements\n", | ||
| "1. Generating citation strings\n", | ||
| " 1. Via cell metadata query\n", | ||
| " 1. Via an AnnData query \n", | ||
| "\n", | ||
| "⚠️ Note that the Census RNA data includes duplicate cells present across multiple datasets. Duplicate cells can be filtered in or out using the cell metadata variable `is_primary_data` which is described in the [Census schema](https://github.com/chanzuckerberg/cellxgene-census/blob/main/docs/cellxgene_census_schema.md#repeated-data).\n", | ||
| "\n", | ||
| "## Requirements\n", | ||
| "\n", | ||
| "This notebook requires:\n", | ||
| "\n", | ||
| "- `cellxgene_census` Python package.\n", | ||
| "- Census data release with [schema version](https://github.com/chanzuckerberg/cellxgene-census/blob/main/docs/cellxgene_census_schema.md) 1.3.0 or greater.\n", | ||
| "\n", | ||
| "## Generating citation strings\n", | ||
| "\n", | ||
| "First we open a handle to the Census data. To ensure we open a data release with schema version 1.3.0 or greater, we use `census_version=\"latest\"`" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "9a5a5a92-3d78-4542-95a5-e6889f245491", | ||
| "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>soma_joinid</th>\n", | ||
| " <th>label</th>\n", | ||
| " <th>value</th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <th>0</th>\n", | ||
| " <td>0</td>\n", | ||
| " <td>census_schema_version</td>\n", | ||
| " <td>1.3.0</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>1</th>\n", | ||
| " <td>1</td>\n", | ||
| " <td>census_build_date</td>\n", | ||
| " <td>2024-01-01</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>2</th>\n", | ||
| " <td>2</td>\n", | ||
| " <td>dataset_schema_version</td>\n", | ||
| " <td>4.0.0</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>3</th>\n", | ||
| " <td>3</td>\n", | ||
| " <td>total_cell_count</td>\n", | ||
| " <td>75694072</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>4</th>\n", | ||
| " <td>4</td>\n", | ||
| " <td>unique_cell_count</td>\n", | ||
| " <td>45846761</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>5</th>\n", | ||
| " <td>5</td>\n", | ||
| " <td>number_donors_homo_sapiens</td>\n", | ||
| " <td>16292</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>6</th>\n", | ||
| " <td>6</td>\n", | ||
| " <td>number_donors_mus_musculus</td>\n", | ||
| " <td>2153</td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "</div>" | ||
| ], | ||
| "text/plain": [ | ||
| " soma_joinid label value\n", | ||
| "0 0 census_schema_version 1.3.0\n", | ||
| "1 1 census_build_date 2024-01-01\n", | ||
| "2 2 dataset_schema_version 4.0.0\n", | ||
| "3 3 total_cell_count 75694072\n", | ||
| "4 4 unique_cell_count 45846761\n", | ||
| "5 5 number_donors_homo_sapiens 16292\n", | ||
| "6 6 number_donors_mus_musculus 2153" | ||
| ] | ||
| }, | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import cellxgene_census\n", | ||
| "\n", | ||
| "census = cellxgene_census.open_soma(census_version=\"latest\")\n", | ||
| "census[\"census_info\"][\"summary\"].read().concat().to_pandas()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "23174644-7804-4723-a4ab-c5cf75bdd954", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Then we load the dataset table which contains a column `\"citation\"` for each dataset included in Census. " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "d47b636a-d653-4e3b-b139-14b6ca697ce8", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "0 Dataset Version: https://datasets.cellxgene.cz...\n", | ||
| "1 Dataset Version: https://datasets.cellxgene.cz...\n", | ||
| "2 Dataset Version: https://datasets.cellxgene.cz...\n", | ||
| "3 Dataset Version: https://datasets.cellxgene.cz...\n", | ||
| "4 Publication: https://doi.org/10.1002/ctm2.1356...\n", | ||
| " ... \n", | ||
| "695 Publication: https://doi.org/10.1038/s41586-02...\n", | ||
| "696 Publication: https://doi.org/10.1038/s41586-02...\n", | ||
| "697 Publication: https://doi.org/10.1016/j.isci.20...\n", | ||
| "698 Publication: https://doi.org/10.1371/journal.p...\n", | ||
| "699 Publication: https://doi.org/10.1016/j.isci.20...\n", | ||
| "Name: citation, Length: 700, dtype: object" | ||
| ] | ||
| }, | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "datasets = census[\"census_info\"][\"datasets\"].read().concat().to_pandas()\n", | ||
| "datasets[\"citation\"]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "06adfa4a-3656-4f26-9adf-ba28eb2f691e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "And now we can use the column `\"dataset_id\"` present in both the dataset table and the Census cell metadata to create citation strings for any Census slice.\n", | ||
| "\n", | ||
| "### Via cell metadata query" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "f7edf4a7-8394-4df2-9dde-b24efcd6dbe0", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/4866a804-37eb-436f-8c87-9cd585260061.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/bfd80f12-725c-4482-ad7f-1ed2b4909b0d.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/e6df8a57-f54f-413a-9d4d-dee03294d778.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/8d599205-5c51-4b50-9d48-3dec31238587.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/f6065c51-bd26-4aa5-a05d-2805aeea48d9.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/8cdbf790-4d29-4f46-9aef-21adfb2e21da.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Query cell metadata\n", | ||
| "cell_metadata = census[\"census_data\"][\"homo_sapiens\"].obs.read(\n", | ||
| " value_filter=\"tissue == 'cardiac atrium'\", column_names=[\"dataset_id\", \"cell_type\"]\n", | ||
| ")\n", | ||
| "cell_metadata = cell_metadata.concat().to_pandas()\n", | ||
| "\n", | ||
| "# Get a citation string for the slice\n", | ||
| "slice_datasets = datasets[datasets[\"dataset_id\"].isin(cell_metadata[\"dataset_id\"])]\n", | ||
| "print(*slice_datasets[\"citation\"], sep=\"\\n\\n\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "941c6f2d-6bc2-46c3-963e-e74335fe93f6", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Via AnnData query" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "id": "2d9b2a11-2f48-43a5-8955-759019ce6bed", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/4866a804-37eb-436f-8c87-9cd585260061.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/bfd80f12-725c-4482-ad7f-1ed2b4909b0d.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/e6df8a57-f54f-413a-9d4d-dee03294d778.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/8d599205-5c51-4b50-9d48-3dec31238587.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/f6065c51-bd26-4aa5-a05d-2805aeea48d9.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n", | ||
| "\n", | ||
| "Publication: https://doi.org/10.1126/science.abl4896 Dataset Version: https://datasets.cellxgene.cziscience.com/8cdbf790-4d29-4f46-9aef-21adfb2e21da.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Fetch an AnnData object\n", | ||
| "adata = cellxgene_census.get_anndata(\n", | ||
| " census=census,\n", | ||
| " organism=\"homo_sapiens\",\n", | ||
| " measurement_name=\"RNA\",\n", | ||
| " obs_value_filter=\"tissue == 'cardiac atrium'\",\n", | ||
| " var_value_filter=\"feature_name == 'MYBPC3'\",\n", | ||
| " column_names={\"obs\": [\"dataset_id\", \"cell_type\"]},\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Get a citation string for the slice\n", | ||
| "slice_datasets = datasets[datasets[\"dataset_id\"].isin(adata.obs[\"dataset_id\"])]\n", | ||
| "print(*slice_datasets[\"citation\"], sep=\"\\n\\n\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "f6988186-5294-43f9-bfe5-2ac255aa0b26", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "And don't forget to close the Census handle" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "id": "f96b1c3b-4a2a-469a-9ded-1c5ff98b84aa", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "census.close()" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.11.4" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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