1283 lines (1282 with data), 139.3 kB

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discover_new_annotations(data, clustering_resolution=0.8, n_pcs=50):\n",
    "    \"\"\"\n",
    "    Discovers new cell annotations using clustering algorithms and UMAP visualization.\n",
    "\n",
    "    Parameters:\n",
    "    - data: AnnData object containing single-cell gene expression data.\n",
    "    - clustering_resolution: Resolution for Leiden clustering (higher values create more clusters).\n",
    "    - n_pcs: Number of principal components for dimensionality reduction.\n",
    "\n",
    "    Returns:\n",
    "    - data: Annotated AnnData object with new clusters in `data.obs[\"discovered_clusters\"]`.\n",
    "    \"\"\"\n",
    "    # Preprocess data\n",
    "    sc.pp.pca(data, n_comps=n_pcs)\n",
    "    sc.pp.neighbors(data, use_rep=\"X_pca\")\n",
    "\n",
    "    # Perform clustering\n",
    "    sc.tl.leiden(data, resolution=clustering_resolution)  # Leiden clustering algorithm\n",
    "    data.obs[\"discovered_clusters\"] = data.obs[\"leiden\"]  # Save clusters to new column\n",
    "\n",
    "    # Compute UMAP\n",
    "    sc.tl.umap(data)\n",
    "\n",
    "    print(\"Clusters discovered and annotated.\")\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize clusters using UMAP\n",
    "def visualize_clusters_on_umap(data, cluster_column=\"discovered_clusters\"):\n",
    "    \"\"\"\n",
    "    Visualizes discovered clusters on a UMAP plot.\n",
    "\n",
    "    Parameters:\n",
    "    - data: AnnData object containing UMAP coordinates and clusters.\n",
    "    - cluster_column: Column name containing cluster information.\n",
    "\n",
    "    Returns:\n",
    "    - fig: Plotly UMAP visualization.\n",
    "    \"\"\"\n",
    "    umap_df = pd.DataFrame(data.obsm[\"X_umap\"], columns=[\"UMAP1\", \"UMAP2\"])\n",
    "    umap_df[cluster_column] = data.obs[cluster_column].values\n",
    "\n",
    "    fig = px.scatter(\n",
    "        umap_df,\n",
    "        x=\"UMAP1\",\n",
    "        y=\"UMAP2\",\n",
    "        color=cluster_column,\n",
    "        title=\"UMAP with Discovered Clusters\",\n",
    "        labels={cluster_column: \"Cluster\"},\n",
    "    )\n",
    "    fig.update_traces(marker=dict(size=5, opacity=0.8))\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize gene expression in clusters using a bubble plot\n",
    "def plot_bubble_matrix(adata, cluster_column=\"discovered_clusters\", top_n_genes=10):\n",
    "    # Get the cluster labels\n",
    "    clusters = adata.obs[cluster_column].unique()\n",
    "\n",
    "    # Extract the marker genes (use the most variable genes or any list you prefer)\n",
    "    marker_genes = adata.var_names[:top_n_genes]\n",
    "\n",
    "    # Collect the gene expression for each cluster\n",
    "    cluster_expression = []\n",
    "    for cluster in clusters:\n",
    "        # Get the subset of cells belonging to the current cluster\n",
    "        cluster_cells = adata[adata.obs[cluster_column] == cluster]\n",
    "\n",
    "        # Get the average expression for each marker gene\n",
    "        mean_expression = cluster_cells[:, marker_genes].X.mean(axis=0)  # Make sure it's 2D\n",
    "        cluster_expression.append(mean_expression.A1 if hasattr(mean_expression, \"A1\") else mean_expression)\n",
    "\n",
    "    # Convert cluster_expression to a 2D numpy array\n",
    "    cluster_expression = np.array(cluster_expression)\n",
    "\n",
    "    # Create a DataFrame for the cluster expression matrix\n",
    "    bubble_df = pd.DataFrame(\n",
    "        cluster_expression,\n",
    "        columns=marker_genes,\n",
    "        index=adata.obs[cluster_column].unique(),\n",
    "    )\n",
    "\n",
    "    bubble_df.index.name = \"Cluster\"\n",
    "    bubble_df = bubble_df.reset_index().melt(id_vars=\"Cluster\", var_name=\"Gene\", value_name=\"Expression\")\n",
    "\n",
    "    # Plotting the bubble plot\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    scatter = sns.scatterplot(\n",
    "        data=bubble_df,\n",
    "        x=\"Gene\",\n",
    "        y=\"Cluster\",\n",
    "        size=\"Expression\",\n",
    "        hue=\"Expression\",\n",
    "        palette=\"viridis\",\n",
    "        sizes=(50, 500),\n",
    "        marker=\"o\",\n",
    "    )\n",
    "\n",
    "    # Adjust the legend position to the right side\n",
    "    plt.legend(title=\"Expression\", bbox_to_anchor=(1.05, 0.5), loc=\"center left\")\n",
    "\n",
    "    # Title and label adjustments\n",
    "    plt.title(\"Gene Expression in Clusters\")\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.tight_layout()  # Ensure the plot fits within the figure space\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jaydeepbhat/Documents/Hackathon/2024_AI_Agent/scripts/talk2cells/lib/python3.11/site-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",
      "/var/folders/vn/z_dtm8w13b92pwg7wrgkkfx00000gn/T/ipykernel_56284/3840506039.py:26: FutureWarning: In the future, the default backend for leiden will be igraph instead of leidenalg.\n",
      "\n",
      " To achieve the future defaults please pass: flavor=\"igraph\" and n_iterations=2.  directed must also be False to work with igraph's implementation.\n",
      "  sc.tl.leiden(data, resolution=clustering_resolution)  # Leiden clustering algorithm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters discovered and annotated.\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example Usage\n",
    "if __name__ == \"__main__\":\n",
    "    # Load example single-cell data\n",
    "    adata = sc.datasets.pbmc3k()  # Example PBMC dataset\n",
    "    sc.pp.normalize_total(adata, target_sum=1e4)\n",
    "    sc.pp.log1p(adata)\n",
    "\n",
    "    # Discover new clusters\n",
    "    adata = discover_new_annotations(adata, clustering_resolution=0.8, n_pcs=50)\n",
    "\n",
    "    # Visualize discovered clusters on UMAP\n",
    "    fig = visualize_clusters_on_umap(adata, cluster_column=\"discovered_clusters\")\n",
    "    fig.show()\n",
    "\n",
    "    # Visualize gene expression in clusters using a bubble plot\n",
    "    plot_bubble_matrix(adata, cluster_column=\"discovered_clusters\", top_n_genes=10)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "talk2cells",
   "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": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}