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Cross-Tissue Macrophage Submanifold Analysis
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "id": "0dfca7be", | |
| "metadata": {}, | |
| "source": [ | |
| "# Cross-Tissue Macrophage Submanifold Analysis\n", | |
| "\n", | |
| "## Background\n", | |
| "\n", | |
| "This notebook demonstrates how to automate the preprocessing of cell type \"submanifolds\" across multiple tissues using TileDB-SOMA, RAPIDS, and Databricks.\n", | |
| "\n", | |
| "The typical workflow for refining cell type labels involves extracting specific cell types from multiple datasets and re-processing them before the re-annotation can even begin.\n", | |
| "\n", | |
| "This can require juggling many different h5ad files and running lots of jobs one at a time.\n", | |
| "\n", | |
| "Here we'll show how to automate the preprocessing steps to deliver submanifolds results ready for re-annotation.\n", | |
| "\n", | |
| "## Introduction\n", | |
| "\n", | |
| "For this example, we'll focus on macrophages across multiple tissue-specific datasets generated by the Tabula Sapiens consortium. Macrophages are interesting because they exhibit strong tissue-specific behaviors.\n", | |
| "\n", | |
| "These datasets are all available in CZI's CELLxGENE Census, which is powered by TileDB-SOMA, allowing us to efficiently query only the macrophage cells we care about.\n", | |
| "\n", | |
| "### The Stack\n", | |
| "\n", | |
| "- **TileDB-SOMA + CELLxGENE Census**: Selective query (macrophages only, 70-90% data reduction).\n", | |
| "- **RAPIDS**: GPU-accelerated compute.\n", | |
| "- **Databricks**: Parallelize across tissues simultaneously." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "21f7d4c1", | |
| "metadata": {}, | |
| "source": [ | |
| "## Setup and Configuration" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "9f35b1e4", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import time\n", | |
| "\n", | |
| "import anndata as ad\n", | |
| "import cellxgene_census\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import pandas as pd\n", | |
| "import scanpy as sc\n", | |
| "import seaborn as sns\n", | |
| "import tiledbsoma as soma" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "49f46ba0", | |
| "metadata": {}, | |
| "source": [ | |
| "CELLxGENE Census is hosted on AWS S3 in `us-west-2`. We configure TileDB-SOMA to access this public dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "d04ff053", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "context = soma.SOMATileDBContext(\n", | |
| " tiledb_config={\n", | |
| " \"vfs.s3.region\": \"us-west-2\",\n", | |
| " \"vfs.s3.no_sign_request\": \"true\",\n", | |
| " }\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "6d21ecb5", | |
| "metadata": {}, | |
| "source": [ | |
| "Retrieve macrophage cells from the *Tabula Sapiens* datasets present in the CELLxGENE Discover Census." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "76b68246", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "DATASET_TITLE = \"Tabula Sapiens - All Cells\"\n", | |
| "ORGANISM = \"homo_sapiens\"\n", | |
| "CELL_TYPE = \"macrophage\"" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "2ced66a7", | |
| "metadata": {}, | |
| "source": [ | |
| "And analyze macrophage across 6 tissues:\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "34ab47b8", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "TISSUES = [\n", | |
| " \"blood\", # Circulating monocytes (macrophage precursors)\n", | |
| " \"bone marrow\", # Monocyte production site\n", | |
| " \"liver\", # Kupffer cells\n", | |
| " \"lung\", # Alveolar macrophages\n", | |
| " \"small intestine\", # Lamina propria macrophages\n", | |
| " \"spleen\", # Red pulp macrophages\n", | |
| "]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "5e543e03", | |
| "metadata": {}, | |
| "source": [ | |
| "Each tissue represents a distinct macrophage niche." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "e65cf65f", | |
| "metadata": {}, | |
| "source": [ | |
| "## Retrieve *Tabula Sapiens* Dataset ID\n", | |
| "\n", | |
| "All Tabula Sapiens cells share a single `dataset_id` in CELLxGENE Census. We'll use this to filter our tissue-specific queries." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "a2a9ccd4", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Querying CELLxGENE Census for Tabula Sapiens dataset...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "The \"stable\" release is currently 2025-01-30. Specify 'census_version=\"2025-01-30\"' in future calls to open_soma() to ensure data consistency.\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
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| " }\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>dataset_id</th>\n", | |
| " <th>dataset_title</th>\n", | |
| " <th>dataset_total_cell_count</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1569</td>\n", | |
| " <td>53d208b0-2cfd-4366-9866-c3c6114081bc</td>\n", | |
| " <td>Tabula Sapiens - All Cells</td>\n", | |
| " <td>1136218</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " soma_joinid dataset_id dataset_title dataset_total_cell_count\n", | |
| "0 1569 53d208b0-2cfd-4366-9866-c3c6114081bc Tabula Sapiens - All Cells 1136218" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "print(\"Querying CELLxGENE Census for Tabula Sapiens dataset...\")\n", | |
| "\n", | |
| "with cellxgene_census.open_soma(context=context) as census:\n", | |
| " census_datasets = census[\"census_info\"][\"datasets\"]\n", | |
| " datasets = (\n", | |
| " census_datasets.read(\n", | |
| " value_filter=f\"dataset_title == '{DATASET_TITLE}'\",\n", | |
| " column_names=[\n", | |
| " \"soma_joinid\",\n", | |
| " \"dataset_id\",\n", | |
| " \"dataset_title\",\n", | |
| " \"dataset_total_cell_count\",\n", | |
| " ],\n", | |
| " )\n", | |
| " .concat()\n", | |
| " .to_pandas()\n", | |
| " )\n", | |
| "\n", | |
| "datasets" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "7ace4dd2", | |
| "metadata": {}, | |
| "source": [ | |
| "Isolate the dataset ID for use in the tissue-specific queries below." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "16ccb35c", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'53d208b0-2cfd-4366-9866-c3c6114081bc'" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "dataset_id = datasets.dataset_id.tolist()[0]\n", | |
| "dataset_id" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "3706a017", | |
| "metadata": {}, | |
| "source": [ | |
| "## Query Tissue-Specific Macrophages\n", | |
| "\n", | |
| "This function leverages **TileDB-SOMA's selective query** to retrieve only macrophages from a specific tissue and returns an `AnnData` object—no need to download the entire dataset and filter locally." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "5fc72663", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def retrieve_anndata(\n", | |
| " tissue: str, dataset_id: str, context: soma.SOMATileDBContext\n", | |
| ") -> ad.AnnData:\n", | |
| " \"\"\"\n", | |
| " Retrieve tissue-specific data from the Census dataset.\n", | |
| "\n", | |
| " Args:\n", | |
| " tissue: Tissue name (must match Census tissue values)\n", | |
| " dataset_id: Tabula Sapiens dataset ID\n", | |
| " context: SOMA context for Census access\n", | |
| "\n", | |
| " Returns:\n", | |
| " AnnData object containing the tissue-specific data.\n", | |
| " \"\"\"\n", | |
| " obs_filter = (\n", | |
| " f\"dataset_id == '{dataset_id}' and \"\n", | |
| " f\"tissue == '{tissue}' and \"\n", | |
| " f\"cell_type == '{CELL_TYPE}'\"\n", | |
| " )\n", | |
| "\n", | |
| " column_names = soma.AxisColumnNames(\n", | |
| " obs=[\n", | |
| " \"soma_joinid\",\n", | |
| " \"cell_type\",\n", | |
| " \"cell_type_ontology_term_id\",\n", | |
| " \"tissue\",\n", | |
| " \"tissue_general_ontology_term_id\",\n", | |
| " \"assay\",\n", | |
| " \"n_measured_vars\",\n", | |
| " ],\n", | |
| " var=[\n", | |
| " \"soma_joinid\",\n", | |
| " \"feature_id\",\n", | |
| " \"feature_name\",\n", | |
| " \"n_measured_obs\",\n", | |
| " ],\n", | |
| " )\n", | |
| "\n", | |
| " with cellxgene_census.open_soma(context=context) as census:\n", | |
| " print(f\"Opening the '{ORGANISM}' SOMA Experiment...\")\n", | |
| " with census[\"census_data\"][ORGANISM].axis_query(\n", | |
| " measurement_name=\"RNA\",\n", | |
| " obs_query=soma.AxisQuery(value_filter=obs_filter),\n", | |
| " ) as query:\n", | |
| " query: soma.ExperimentAxisQuery\n", | |
| " print(f\"...retrieved {query.n_obs:,} cells from '{tissue}' tissue\")\n", | |
| " print(\"...exporting to AnnData\")\n", | |
| " adata = query.to_anndata(\n", | |
| " X_name=\"raw\",\n", | |
| " column_names=column_names,\n", | |
| " )\n", | |
| " return adata" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "8dc7da91", | |
| "metadata": {}, | |
| "source": [ | |
| "Execute query for the first tissue." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "3e2af6a7", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "The \"stable\" release is currently 2025-01-30. Specify 'census_version=\"2025-01-30\"' in future calls to open_soma() to ensure data consistency.\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Opening the 'homo_sapiens' SOMA Experiment...\n", | |
| "...retrieved 1,228 cells from 'blood' tissue\n", | |
| "...exporting to AnnData\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "AnnData object with n_obs × n_vars = 1228 × 61888\n", | |
| " obs: 'soma_joinid', 'cell_type', 'cell_type_ontology_term_id', 'tissue', 'tissue_general_ontology_term_id', 'assay', 'n_measured_vars', 'dataset_id'\n", | |
| " var: 'soma_joinid', 'feature_id', 'feature_name', 'n_measured_obs'" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "adata = retrieve_anndata(\n", | |
| " tissue=TISSUES[0],\n", | |
| " dataset_id=dataset_id,\n", | |
| " context=context,\n", | |
| ")\n", | |
| "\n", | |
| "adata" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "098dae7f", | |
| "metadata": {}, | |
| "source": [ | |
| "## Submanifold Processing Pipeline\n", | |
| "\n", | |
| "This function implements the submanifold preprocessing workflow and produces a simple table containing only the processed data needed for inspecting and refining cell type annotations." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "0995ea4c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def process_submanifold(\n", | |
| " adata: ad.AnnData,\n", | |
| " n_top_genes: int = 5_000,\n", | |
| " n_pcs: int = 10,\n", | |
| " min_genes: int = 200,\n", | |
| " inplace: bool = False,\n", | |
| ") -> pd.DataFrame:\n", | |
| " \"\"\"\n", | |
| " Process submanifold.\n", | |
| "\n", | |
| "\n", | |
| " This automates the preprocessing steps that computational biologists\n", | |
| " manually perform before refining cell type annotations:\n", | |
| "\n", | |
| " 1. Quality control filtering (remove low-quality cells)\n", | |
| " 2. Normalization (standard log-normalization)\n", | |
| " 3. Recompute HVGs\n", | |
| " 4. Scale expression values\n", | |
| " 5. Recompute PCA on HVGs\n", | |
| " 6. Build neighborhood graph\n", | |
| " 7. Compute UMAP\n", | |
| " 8. Leiden clustering (for initital groupings)\n", | |
| "\n", | |
| " Args:\n", | |
| " adata: AnnData object containing tissue-specific data.\n", | |
| " n_top_genes: Number of highly variable genes to select.\n", | |
| " n_pcs: Number of principal components to compute.\n", | |
| " min_genes: Minimum number of genes expressed to retain a cell.\n", | |
| " inplace: Whether to modify the AnnData object in place.\n", | |
| "\n", | |
| " Returns:\n", | |
| " PyArrow Table with columns:\n", | |
| " - cell_id: Unique cell identifier (links back to Census)\n", | |
| " - umap_x, umap_y: UMAP coordinates for visualization\n", | |
| " - cluster: Leiden cluster assignment\n", | |
| " - n_genes_expressed: Number of genes expressed per cell (for QC)\n", | |
| " \"\"\"\n", | |
| " if not inplace:\n", | |
| " adata = adata.copy()\n", | |
| "\n", | |
| " start_time = time.time()\n", | |
| "\n", | |
| " print(\"Step 1: Calculating QC metrics...\")\n", | |
| " sc.pp.calculate_qc_metrics(adata, inplace=True)\n", | |
| "\n", | |
| " print(\"Step 2: Applying QC filters...\")\n", | |
| " n_before = adata.n_obs\n", | |
| " sc.pp.filter_cells(adata, min_genes=min_genes, inplace=True)\n", | |
| "\n", | |
| " n_after = adata.n_obs\n", | |
| " n_filtered = n_before - n_after\n", | |
| " pct_filtered = 100 * n_filtered / n_before\n", | |
| " print(f\" Filtered {n_filtered:,} cells ({pct_filtered:.1f}%)\")\n", | |
| " print(f\" Retained {n_after:,} high-quality cells\")\n", | |
| "\n", | |
| " print(\"Step 3: Normalization...\")\n", | |
| " sc.pp.normalize_total(adata, target_sum=1e4)\n", | |
| " sc.pp.log1p(adata)\n", | |
| "\n", | |
| " print(\"Step 4: Recomputing highly variable genes (T-cell specific)...\")\n", | |
| " sc.pp.highly_variable_genes(adata, n_top_genes=n_top_genes)\n", | |
| "\n", | |
| " print(\"Step 5: Scaling expression values...\")\n", | |
| " sc.pp.scale(adata)\n", | |
| "\n", | |
| " print(\"Step 7: Computing PCA (T-cell HVG space)...\")\n", | |
| " sc.pp.pca(adata, n_comps=n_pcs, mask_var=\"highly_variable\")\n", | |
| "\n", | |
| " print(\"Step 8: Computing neighborhood graph...\")\n", | |
| " sc.pp.neighbors(adata, n_pcs=n_pcs)\n", | |
| "\n", | |
| " print(\"Step 9: Computing UMAP...\")\n", | |
| " sc.tl.umap(adata)\n", | |
| "\n", | |
| " print(\"Step 10: Clustering cells...\")\n", | |
| " sc.tl.leiden(adata, flavor=\"igraph\", n_iterations=2)\n", | |
| " n_clusters = adata.obs[\"leiden\"].nunique()\n", | |
| " print(f\" Identified {n_clusters} clusters\")\n", | |
| "\n", | |
| " print(\"Step 11: Extracting results to PyArrow Table...\")\n", | |
| " umap_coords = adata.obsm[\"X_umap\"]\n", | |
| "\n", | |
| " processing_time = time.time() - start_time\n", | |
| " print(f\"\\n✓ Processing complete\")\n", | |
| " print(f\" Total time: {processing_time:.1f}s\")\n", | |
| " print(f\" Cells analyzed: {len(adata.obs):,}\")\n", | |
| " print(f\" Clusters identified: {n_clusters}\")\n", | |
| "\n", | |
| " return pd.DataFrame(\n", | |
| " {\n", | |
| " \"cell_id\": adata.obs.index,\n", | |
| " \"umap_x\": umap_coords[:, 0],\n", | |
| " \"umap_y\": umap_coords[:, 1],\n", | |
| " \"cluster\": adata.obs[\"leiden\"].values,\n", | |
| " \"n_genes_expressed\": adata.obs[\"n_genes_by_counts\"].values,\n", | |
| " }\n", | |
| " )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "c9014aec", | |
| "metadata": {}, | |
| "source": [ | |
| "Run the submanifold processing pipeline on the queried macrophage cells." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "d811b27a", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Step 1: Calculating QC metrics...\n", | |
| "Step 2: Applying QC filters...\n", | |
| " Filtered 0 cells (0.0%)\n", | |
| " Retained 1,228 high-quality cells\n", | |
| "Step 3: Normalization...\n", | |
| "Step 4: Recomputing highly variable genes (T-cell specific)...\n", | |
| "Step 5: Scaling expression values...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/Users/aaronwolen/.local/share/uv/python/cpython-3.10.16-macos-aarch64-none/lib/python3.10/functools.py:889: UserWarning: zero-centering a sparse array/matrix densifies it.\n", | |
| " return dispatch(args[0].__class__)(*args, **kw)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Step 7: Computing PCA (T-cell HVG space)...\n", | |
| "Step 8: Computing neighborhood graph...\n", | |
| "Step 9: Computing UMAP...\n", | |
| "Step 10: Clustering cells...\n", | |
| " Identified 11 clusters\n", | |
| "Step 11: Extracting results to PyArrow Table...\n", | |
| "\n", | |
| "✓ Processing complete\n", | |
| " Total time: 1.7s\n", | |
| " Cells analyzed: 1,228\n", | |
| " Clusters identified: 11\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
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| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
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| " text-align: right;\n", | |
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| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>cell_id</th>\n", | |
| " <th>umap_x</th>\n", | |
| " <th>umap_y</th>\n", | |
| " <th>cluster</th>\n", | |
| " <th>n_genes_expressed</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>7.265996</td>\n", | |
| " <td>2.743016</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2759</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-3.393944</td>\n", | |
| " <td>-2.726480</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2604</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2</td>\n", | |
| " <td>-2.219077</td>\n", | |
| " <td>-1.750018</td>\n", | |
| " <td>4</td>\n", | |
| " <td>2761</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>3</td>\n", | |
| " <td>-0.980119</td>\n", | |
| " <td>-4.708682</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1441</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>4</td>\n", | |
| " <td>-1.748296</td>\n", | |
| " <td>-4.496931</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1869</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1223</th>\n", | |
| " <td>1223</td>\n", | |
| " <td>-1.194059</td>\n", | |
| " <td>-4.366943</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2438</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1224</th>\n", | |
| " <td>1224</td>\n", | |
| " <td>-3.069425</td>\n", | |
| " <td>-1.080847</td>\n", | |
| " <td>4</td>\n", | |
| " <td>2286</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1225</th>\n", | |
| " <td>1225</td>\n", | |
| " <td>5.716567</td>\n", | |
| " <td>2.224270</td>\n", | |
| " <td>3</td>\n", | |
| " <td>4974</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1226</th>\n", | |
| " <td>1226</td>\n", | |
| " <td>-2.934721</td>\n", | |
| " <td>-2.726521</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2997</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1227</th>\n", | |
| " <td>1227</td>\n", | |
| " <td>-1.467677</td>\n", | |
| " <td>-4.884536</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2692</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1228 rows × 5 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " cell_id umap_x umap_y cluster n_genes_expressed\n", | |
| "0 0 7.265996 2.743016 0 2759\n", | |
| "1 1 -3.393944 -2.726480 1 2604\n", | |
| "2 2 -2.219077 -1.750018 4 2761\n", | |
| "3 3 -0.980119 -4.708682 1 1441\n", | |
| "4 4 -1.748296 -4.496931 1 1869\n", | |
| "... ... ... ... ... ...\n", | |
| "1223 1223 -1.194059 -4.366943 1 2438\n", | |
| "1224 1224 -3.069425 -1.080847 4 2286\n", | |
| "1225 1225 5.716567 2.224270 3 4974\n", | |
| "1226 1226 -2.934721 -2.726521 1 2997\n", | |
| "1227 1227 -1.467677 -4.884536 1 2692\n", | |
| "\n", | |
| "[1228 rows x 5 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "submanifold_df = process_submanifold(adata)\n", | |
| "submanifold_df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "3500898b", | |
| "metadata": {}, | |
| "source": [ | |
| "This table contains everything computational biologists need for annotation refinement. In production this could land in a Delta Lake." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "e78c1ecc", | |
| "metadata": {}, | |
| "source": [ | |
| "## Plot UMAP Clusters\n", | |
| "\n", | |
| "Now that we have the processed macrophage submanifold, let's visualize the UMAP embedding colored by initial Leiden clusters." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "0008991c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_umap_clusters(\n", | |
| " df: pd.DataFrame,\n", | |
| " title: str = \"Leiden Clusters\",\n", | |
| " figsize: tuple = (8, 6),\n", | |
| " palette: str = \"tab10\",\n", | |
| ") -> None:\n", | |
| " \"\"\"\n", | |
| " Generate a UMAP scatter plot colored by Leiden clusters using seaborn.\n", | |
| "\n", | |
| " Args:\n", | |
| " df: DataFrame from process_submanifold() containing umap_x,\n", | |
| " umap_y, and cluster columns.\n", | |
| " title: Plot title.\n", | |
| " figsize: Figure size as (width, height) tuple.\n", | |
| " palette: Seaborn color palette name.\n", | |
| "\n", | |
| " Returns:\n", | |
| " matplotlib Figure object.\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " # Create figure and axis\n", | |
| " fig, ax = plt.subplots(figsize=figsize)\n", | |
| "\n", | |
| " # Create scatter plot with seaborn\n", | |
| " sns.scatterplot(\n", | |
| " data=df,\n", | |
| " x=\"umap_x\",\n", | |
| " y=\"umap_y\",\n", | |
| " hue=\"cluster\",\n", | |
| " palette=palette,\n", | |
| " s=20,\n", | |
| " alpha=0.7,\n", | |
| " linewidth=0,\n", | |
| " ax=ax,\n", | |
| " )\n", | |
| "\n", | |
| " ax.set_xlabel(\"UMAP 1\")\n", | |
| " ax.set_ylabel(\"UMAP 2\")\n", | |
| " ax.set_title(title, fontweight=\"bold\")\n", | |
| "\n", | |
| " ax.legend(\n", | |
| " title=\"Cluster\",\n", | |
| " bbox_to_anchor=(1.05, 1),\n", | |
| " loc=\"upper left\",\n", | |
| " frameon=True,\n", | |
| " fontsize=10,\n", | |
| " )\n", | |
| "\n", | |
| " plt.tight_layout()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "613e3917", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 800x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plot_umap_clusters(submanifold_df, title=\"Macrophage Leiden Clusters\")" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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