1100 lines (1099 with data), 56.3 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to annotate cells\n",
    "def annotate_cells(data, method=\"custom_gene_list\", gene_list=None, annotation_map=None, external_tool=\"CIBERSORT\", database_results=None):\n",
    "    \"\"\"\n",
    "    Annotates cells based on single-cell gene expression data.\n",
    "\n",
    "    Parameters:\n",
    "    - data: AnnData object or DataFrame containing single-cell gene expression data.\n",
    "    - method: \"custom_gene_list\" or \"external_database\".\n",
    "    - gene_list: List of marker genes (required if method=\"custom_gene_list\").\n",
    "    - annotation_map: Dictionary mapping genes to cell types (required if method=\"custom_gene_list\").\n",
    "    - external_tool: Name of the external database/tool (e.g., \"CIBERSORT\", \"ImmuneCellAI\").\n",
    "    - database_results: Results from an external database (required if method=\"external_database\").\n",
    "\n",
    "    Returns:\n",
    "    - data: Annotated AnnData/DataFrame with a new column \"annotations\".\n",
    "    \"\"\"\n",
    "    if method == \"custom_gene_list\":\n",
    "        if gene_list is None or annotation_map is None:\n",
    "            raise ValueError(\"A gene list and annotation map must be provided for the 'custom_gene_list' method.\")\n",
    "        print(\"Annotating cells using custom gene list...\")\n",
    "\n",
    "        # Annotate cells by identifying the dominant cell type based on marker genes\n",
    "        annotations = []\n",
    "        for cell_idx in range(data.shape[0]):\n",
    "            cell_annotation = None\n",
    "            for gene in gene_list:\n",
    "                if gene in data.var_names:\n",
    "                    if data[cell_idx, gene].X > 1:  # Example threshold for gene expression\n",
    "                        cell_annotation = annotation_map.get(gene, \"Unknown\")\n",
    "                        break  # Assign the first matching annotation\n",
    "            annotations.append(cell_annotation if cell_annotation else \"Unannotated\")\n",
    "        data.obs[\"annotations\"] = annotations\n",
    "\n",
    "    elif method == \"external_database\":\n",
    "        if database_results is None:\n",
    "            raise ValueError(\"Database results must be provided for the 'external_database' method.\")\n",
    "        print(f\"Annotating cells using {external_tool} results...\")\n",
    "        data.obs[\"annotations\"] = database_results[\"cell_type\"]\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"Invalid method. Choose either 'custom_gene_list' or 'external_database'.\")\n",
    "\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UMAP Visualization Function\n",
    "def visualize_annotations_on_umap(data, annotations_column=\"annotations\", n_pcs=50):\n",
    "    \"\"\"\n",
    "    Visualizes annotations on UMAP.\n",
    "\n",
    "    Parameters:\n",
    "    - data: AnnData object or DataFrame containing UMAP coordinates and annotations.\n",
    "    - annotations_column: Name of the column containing cell annotations.\n",
    "    - n_pcs: Number of principal components to use for UMAP computation.\n",
    "\n",
    "    Returns:\n",
    "    - fig: Plotly figure with annotated UMAP.\n",
    "    \"\"\"\n",
    "    # Ensure UMAP coordinates exist\n",
    "    if \"X_umap\" not in data.obsm.keys():\n",
    "        print(\"UMAP coordinates not found. Computing UMAP...\")\n",
    "\n",
    "        # Reduce dimensions using PCA\n",
    "        print(f\"Reducing dimensionality to {n_pcs} principal components...\")\n",
    "        sc.pp.pca(data, n_comps=n_pcs)\n",
    "\n",
    "        # Compute neighbors and UMAP\n",
    "        sc.pp.neighbors(data, use_rep=\"X_pca\")  # Use PCA-reduced data\n",
    "        sc.tl.umap(data)\n",
    "\n",
    "    umap_df = pd.DataFrame(data.obsm[\"X_umap\"], columns=[\"UMAP1\", \"UMAP2\"])\n",
    "    umap_df[annotations_column] = data.obs[annotations_column].values\n",
    "\n",
    "    # Create Plotly UMAP visualization\n",
    "    fig = px.scatter(\n",
    "        umap_df,\n",
    "        x=\"UMAP1\",\n",
    "        y=\"UMAP2\",\n",
    "        color=annotations_column,\n",
    "        title=\"UMAP with Cell Annotations\",\n",
    "        labels={annotations_column: \"Cell Type\"},\n",
    "    )\n",
    "    fig.update_traces(marker=dict(size=5, opacity=0.8))\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotating cells using custom gene list...\n",
      "UMAP coordinates not found. Computing UMAP...\n",
      "Reducing dimensionality to 50 principal components...\n"
     ]
    },
    {
     "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"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Cell Type=T cells<br>UMAP1=%{x}<br>UMAP2=%{y}<extra></extra>",
         "legendgroup": "T cells",
         "marker": {
          "color": "#636efa",
          "opacity": 0.8,
          "size": 5,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "T cells",
         "showlegend": true,
         "type": "scattergl",
         "x": {
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          "dtype": "f4"
         },
         "yaxis": "y"
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        {
         "hovertemplate": "Cell Type=B cells<br>UMAP1=%{x}<br>UMAP2=%{y}<extra></extra>",
         "legendgroup": "B cells",
         "marker": {
          "color": "#EF553B",
          "opacity": 0.8,
          "size": 5,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "B cells",
         "showlegend": true,
         "type": "scattergl",
         "x": {
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         },
         "xaxis": "x",
         "y": {
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         "yaxis": "y"
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         "hovertemplate": "Cell Type=Monocytes<br>UMAP1=%{x}<br>UMAP2=%{y}<extra></extra>",
         "legendgroup": "Monocytes",
         "marker": {
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          "opacity": 0.8,
          "size": 5,
          "symbol": "circle"
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         "mode": "markers",
         "name": "Monocytes",
         "showlegend": true,
         "type": "scattergl",
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         "xaxis": "x",
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         "yaxis": "y"
        },
        {
         "hovertemplate": "Cell Type=Unannotated<br>UMAP1=%{x}<br>UMAP2=%{y}<extra></extra>",
         "legendgroup": "Unannotated",
         "marker": {
          "color": "#ab63fa",
          "opacity": 0.8,
          "size": 5,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "Unannotated",
         "showlegend": true,
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   "source": [
    "# Example Usage\n",
    "if __name__ == \"__main__\":\n",
    "    # Load example single-cell data (AnnData format)\n",
    "    adata = sc.datasets.pbmc3k()  # Example dataset\n",
    "    sc.pp.normalize_total(adata, target_sum=1e4)\n",
    "    sc.pp.log1p(adata)\n",
    "\n",
    "    # Example custom gene list and annotation map\n",
    "    example_gene_list = [\"CD3D\", \"CD79A\", \"LYZ\"]  # Example marker genes for T-cells, B-cells, Monocytes\n",
    "    annotation_map = {\n",
    "        \"CD3D\": \"T cells\",\n",
    "        \"CD79A\": \"B cells\",\n",
    "        \"LYZ\": \"Monocytes\"\n",
    "    }\n",
    "\n",
    "    # Annotate cells\n",
    "    adata = annotate_cells(adata, method=\"custom_gene_list\", gene_list=example_gene_list, annotation_map=annotation_map)\n",
    "\n",
    "    # Visualize annotations on UMAP\n",
    "    fig = visualize_annotations_on_umap(adata, annotations_column=\"annotations\", n_pcs=50)\n",
    "    fig.show()\n"
   ]
  }
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