Tutorial_human_gastrula.ipynb

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   "cell_type": "markdown",
   "id": "693720c7-9085-4b6a-8b79-df47f9c55519",
   "metadata": {},
   "source": [
    "# Tutorial for human gastrula dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d992f70-34cb-4da3-821e-ff04ac50e919",
   "metadata": {},
   "source": [
    "## CIARA functions\n",
    "Install the ciara_python package over pip (can of course also be done in command line)"
   ]
  },
  {
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     "text": [
      "Requirement already satisfied: ciara_python in /Users/marco.stock/opt/anaconda3/envs/scanpy/lib/python3.9/site-packages (1.0.3)\n",
      "Requirement already satisfied: scanpy in /Users/marco.stock/opt/anaconda3/envs/scanpy/lib/python3.9/site-packages (from ciara_python) (1.8.2)\n",
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      "Requirement already satisfied: umap-learn>=0.3.10 in /Users/marco.stock/opt/anaconda3/envs/scanpy/lib/python3.9/site-packages (from scanpy->ciara_python) (0.5.2)\n",
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     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install --upgrade ciara_python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ab3a68-cd10-4583-8d82-65b032b489f6",
   "metadata": {},
   "source": [
    "Import the two important CIARA functions and other packages needed for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "981fe113-2aca-480d-b0f8-6ae0bcf86215",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "from ciara_python import get_background_full, ciara"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9ecfd6-d608-4405-94c9-757d3885cb75",
   "metadata": {},
   "source": [
    "## Import human gastrula dataset and KNN matrix\n",
    "\n",
    "Note that for Anndata object the count matrix is transposed (cells x genes) compared to the Seurat pipeline in R (genes x cells)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c755f9d0-b5b9-4209-a60e-9de9892e1174",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AnnData object with n_obs × n_vars = 1195 × 36570\n"
     ]
    }
   ],
   "source": [
    "human_gast_norm = sc.read_csv('../Data/norm_elmir_5_30_transposed.csv', delimiter=',')\n",
    "#change to your data path\n",
    "\n",
    "human_gast_norm = human_gast_norm.transpose()\n",
    "print(human_gast_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb75506-8b43-472d-8984-ecbbba23d257",
   "metadata": {},
   "source": [
    "Calculate the PCA and from this the knn matrix for the dataset using the integrated scanpy function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f604158b-dfbf-47b6-95ba-34134e0da8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marco.stock/opt/anaconda3/envs/scanpy/lib/python3.9/site-packages/tqdm/auto.py:22: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1195, 1195)\n"
     ]
    }
   ],
   "source": [
    "#sc.pp.highly_variable_genes(human_gast_norm, n_top_genes=2000)\n",
    "sc.tl.pca(human_gast_norm, n_comps=30)\n",
    "sc.pp.neighbors(human_gast_norm, n_neighbors=5, use_rep='X_pca')\n",
    "print(human_gast_norm.obsp[\"connectivities\"].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f43a68f-4955-45e5-a994-4e4f68f2eaa4",
   "metadata": {},
   "source": [
    "## CIARA algorithm\n",
    "\n",
    "### Step 1: Find background genes\n",
    "\n",
    "The background genes get calculated and added as boolean values to the gene metadata in the AnnData object (in column `human_gast_norm.var[\"CIARA_background\"]`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aea3f5d8-8fc7-4fbb-a02e-4a3cff24a342",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background genes: 5057\n",
      "Execution time: 0.08s\n"
     ]
    }
   ],
   "source": [
    "t = time.perf_counter()\n",
    "\n",
    "get_background_full(human_gast_norm, threshold=1, n_cells_low=3, n_cells_high=20)\n",
    "\n",
    "elapsed_time = time.perf_counter() - t\n",
    "print(\"Execution time: \" + str(np.round(elapsed_time, 2)) + \"s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa29784-d053-4891-ba2b-fb2c3edfeca9",
   "metadata": {},
   "source": [
    "### Step 2: Calculate entropy of mixing of background genes\n",
    "\n",
    "The p value for each background gene is added to the gene metadata in the AnnData object (in column `human_gast_norm.var[\"CIARA_p_value\"]`):\n",
    "\n",
    "**Runtime (4-core MacBook Pro) per size of genes (no approximation):**\n",
    "- 1 gene: **0.2s**\n",
    "- 10 genes: **0.5s**\n",
    "- 100 genes: **4s**\n",
    "- 1000 genes: **10s**\n",
    "- 5057 genes *(this dataset)*: **270s**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45b3908d-6e93-4245-8990-d74955671a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## Running on 4 cores with a chunksize of 317\n",
      "\n",
      "---- Finished sucessfully! ----\n",
      "\n",
      "Execution Time: 17.07s\n"
     ]
    }
   ],
   "source": [
    "t = time.perf_counter()\n",
    "\n",
    "ciara(human_gast_norm, n_cores=4, p_value=0.001, approximation=True, local_region=1)\n",
    "\n",
    "elapsed_time = time.perf_counter() - t\n",
    "print(\"\\nExecution Time: \" + str(np.round(elapsed_time, 2)) + \"s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e86d34f4-88f3-419c-be8e-6b75648b44db",
   "metadata": {},
   "source": [
    "## Ciara results\n",
    "\n",
    "We receive an extended AnnData object that contains the results in its gene metadata:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e4606c5-ce19-40ad-9d40-9e6deb3976d5",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CIARA_p_value</th>\n",
       "      <th>CIARA_background</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>A1BG</th>\n",
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       "      <th>A1CF</th>\n",
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       "<p>36570 rows × 2 columns</p>\n",
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      "text/plain": [
       "          CIARA_p_value  CIARA_background\n",
       "A1BG                NaN             False\n",
       "A1BG.AS1            NaN             False\n",
       "A1CF           0.000442              True\n",
       "A2M                 NaN             False\n",
       "A2M.AS1             NaN             False\n",
       "...                 ...               ...\n",
       "ZXDC           0.000488              True\n",
       "ZYG11A              NaN             False\n",
       "ZYG11B              NaN             False\n",
       "ZYX                 NaN             False\n",
       "ZZEF1          1.000000              True\n",
       "\n",
       "[36570 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "human_gast_norm.var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0face4-f330-48af-8e2c-cc3278398e21",
   "metadata": {},
   "source": [
    "We can now plot the top genes with lowest p-value on the UMAP of the dataset. For this we extract the top rare cell type marker genes identified by CIARA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9367e9ee-8e14-4190-b790-9842b408f2bf",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CIARA_p_value</th>\n",
       "      <th>CIARA_background</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>STAB2</th>\n",
       "      <td>6.390857e-11</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIMAP4</th>\n",
       "      <td>2.638277e-09</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>ECM1</th>\n",
       "      <td>2.164534e-08</td>\n",
       "      <td>True</td>\n",
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       "      <th>OIT3</th>\n",
       "      <td>2.859037e-08</td>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "        CIARA_p_value  CIARA_background\n",
       "STAB2    6.390857e-11              True\n",
       "GIMAP4   2.638277e-09              True\n",
       "ECM1     2.164534e-08              True\n",
       "OIT3     2.859037e-08              True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_markers = human_gast_norm.var.nsmallest(4, [\"CIARA_p_value\"],)\n",
    "top_markers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2e99d4-3dbc-4763-b4ce-ae4ce9ce9d8b",
   "metadata": {},
   "source": [
    "Now we plot the expression of the top markers in the umap to identify the rare cells. Of course, by defining a threshold on these top markers we can then also subset the scanpy dataset to extract the rare cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6c8f30b-b47b-40e7-afe2-73b0c33980ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1097.28x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import rc_context\n",
    "\n",
    "sc.tl.umap(human_gast_norm)\n",
    "\n",
    "with rc_context({'figure.figsize': (3, 3)}):\n",
    "    sc.pl.umap(human_gast_norm, color=top_markers.index.tolist())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "scanpy",
   "language": "python",
   "name": "scanpy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}