nuclei_segmentation_custom_binning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fed91344-d62d-4f3d-bf20-d71b171dd573",
   "metadata": {},
   "source": [
    "# Install"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6679718-1462-4988-8f0d-1c96941c753a",
   "metadata": {},
   "source": [
    "Get warning, 'restart runtime', this is fine. Do this before proceeding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b71cd2e-a884-404c-889e-997f3676fed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "    Found existing installation: scanpy 1.9.1\n",
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      "      Successfully uninstalled scanpy-1.9.1\n",
      "Successfully installed aiobotocore-2.5.4 aiohttp-3.9.3 aioitertools-0.11.0 aiosignal-1.3.1 anndata-0.10.5.post1 annotated-types-0.6.0 array-api-compat-1.4.1 asciitree-0.3.3 async-timeout-4.0.3 botocore-1.31.17 click-plugins-1.1.1 cligj-0.7.2 cloudpickle-3.0.0 colorcet-3.0.1 cramjam-2.8.1 csbdeep-0.7.4 dask-2024.1.1 dask-image-2023.8.1 datashader-0.16.0 distributed-2024.1.1 docrep-0.3.2 fasteners-0.19 fastparquet-2024.2.0 fiona-1.9.5 frozenlist-1.4.1 fsspec-2023.6.0 geopandas-0.14.3 get-annotations-0.1.2 igraph-0.11.3 imageio-2.33.1 inflect-7.0.0 jmespath-1.0.1 lazy_loader-0.3 leidenalg-0.10.2 locket-1.0.0 markdown-it-py-3.0.0 matplotlib-scalebar-0.8.1 mdurl-0.1.2 msgpack-1.0.7 multidict-6.0.5 multipledispatch-1.0.0 multiscale-spatial-image-0.11.2 numcodecs-0.12.1 ome-zarr-0.8.3 omnipath-1.0.8 pandas-2.2.0 param-2.0.2 partd-1.4.1 pims-0.6.1 pyarrow-15.0.0 pyct-0.5.0 pydantic-2.6.1 pydantic-core-2.16.2 pygeos-0.14 pyproj-3.6.1 rich-13.7.0 s3fs-2023.6.0 scanpy-1.9.8 scikit-image-0.22.0 seaborn-0.13.2 shapely-2.0.2 slicerator-1.1.0 sortedcontainers-2.4.0 spatial-image-0.3.0 spatialdata-0.0.15 squidpy-1.4.1 stardist-0.8.5 tblib-3.0.0 tifffile-2024.1.30 toolz-0.12.1 typing-extensions-4.9.0 tzdata-2023.4 urllib3-1.26.18 validators-0.22.0 wrapt-1.16.0 xarray-2023.12.0 xarray-dataclasses-1.7.0 xarray-datatree-0.0.14 xarray-schema-0.0.3 xarray-spatial-0.3.7 yarl-1.9.4 zarr-2.16.1 zict-3.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install stardist geopandas squidpy fastparquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74464f3a-c3b2-4289-a569-b3e5321f70cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hDownloading libclang-16.0.6-py2.py3-none-macosx_10_9_x86_64.whl (24.5 MB)\n",
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      "\u001b[?25hDownloading tensorflow_estimator-2.15.0-py2.py3-none-any.whl (441 kB)\n",
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      "\u001b[?25hDownloading tensorflow_io_gcs_filesystem-0.36.0-cp39-cp39-macosx_10_14_x86_64.whl (2.5 MB)\n",
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      "\u001b[?25hDownloading termcolor-2.4.0-py3-none-any.whl (7.7 kB)\n",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m186.8/186.8 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading google_auth_oauthlib-1.2.0-py2.py3-none-any.whl (24 kB)\n",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.9/103.9 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl (4.8 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0mm\n",
      "\u001b[?25hDownloading werkzeug-3.0.1-py3-none-any.whl (226 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m226.7/226.7 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading cachetools-5.3.2-py3-none-any.whl (9.3 kB)\n",
      "Downloading pyasn1-0.5.1-py2.py3-none-any.whl (84 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.9/84.9 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: libclang, flatbuffers, wrapt, werkzeug, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, pyasn1, protobuf, oauthlib, numpy, keras, grpcio, google-pasta, gast, cachetools, astunparse, absl-py, rsa, requests-oauthlib, pyasn1-modules, opt-einsum, ml-dtypes, markdown, google-auth, google-auth-oauthlib, tensorboard, tensorflow\n",
      "  Attempting uninstall: wrapt\n",
      "    Found existing installation: wrapt 1.16.0\n",
      "    Uninstalling wrapt-1.16.0:\n",
      "      Successfully uninstalled wrapt-1.16.0\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.23.0\n",
      "    Uninstalling numpy-1.23.0:\n",
      "      Successfully uninstalled numpy-1.23.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "numba 0.57.1 requires numpy<1.25,>=1.21, but you have numpy 1.26.4 which is incompatible.\n",
      "xarray-spatial 0.3.7 requires numpy<=1.23.4, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed absl-py-2.1.0 astunparse-1.6.3 cachetools-5.3.2 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.27.0 google-auth-oauthlib-1.2.0 google-pasta-0.2.0 grpcio-1.60.1 keras-2.15.0 libclang-16.0.6 markdown-3.5.2 ml-dtypes-0.2.0 numpy-1.26.4 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.25.2 pyasn1-0.5.1 pyasn1-modules-0.3.0 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.15.2 tensorboard-data-server-0.7.2 tensorflow-2.15.0 tensorflow-estimator-2.15.0 tensorflow-io-gcs-filesystem-0.36.0 termcolor-2.4.0 werkzeug-3.0.1 wrapt-1.14.1\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d847d8b1-c5a5-4952-880b-363ed5385570",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Numba needs NumPy 1.24 or less",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUSE_PYGEOS\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgeopandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgpd\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscanpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msc\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.9/site-packages/scanpy/__init__.py:16\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mModuleNotFoundError\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m     13\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscanpy is not correctly installed. Please install it, e.g. with pip.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     14\u001b[0m         )\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_versions\n\u001b[1;32m     18\u001b[0m check_versions()\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m check_versions\n",
      "File \u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.9/site-packages/scanpy/_utils/__init__.py:28\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_settings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m settings\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m logging \u001b[38;5;28;01mas\u001b[39;00m logg\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompute\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mis_constant\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_constant\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mEmpty\u001b[39;00m(Enum):\n\u001b[1;32m     32\u001b[0m     token \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.9/site-packages/scanpy/_utils/compute/is_constant.py:5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumbers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Integral\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m njit\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sparse\n\u001b[1;32m      9\u001b[0m \u001b[38;5;129m@singledispatch\u001b[39m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_constant\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n",
      "File \u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.9/site-packages/numba/__init__.py:55\u001b[0m\n\u001b[1;32m     50\u001b[0m             msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumba requires SciPy version 1.0 or greater. Got SciPy \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     51\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscipy\u001b[38;5;241m.\u001b[39m__version__\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     52\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[0;32m---> 55\u001b[0m \u001b[43m_ensure_critical_deps\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m# END DO NOT MOVE\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# ---------------------- WARNING WARNING WARNING ----------------------------\u001b[39;00m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_version\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_versions\n",
      "File \u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.9/site-packages/numba/__init__.py:42\u001b[0m, in \u001b[0;36m_ensure_critical_deps\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m numpy_version \u001b[38;5;241m>\u001b[39m (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m24\u001b[39m):\n\u001b[0;32m---> 42\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumba needs NumPy 1.24 or less\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     44\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n",
      "\u001b[0;31mImportError\u001b[0m: Numba needs NumPy 1.24 or less"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import anndata\n",
    "import os\n",
    "os.environ['USE_PYGEOS'] = '0'\n",
    "import geopandas as gpd\n",
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a944741e-b5a0-4e23-b2e3-79e987b7e01f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tifffile import imread, imwrite\n",
    "from csbdeep.utils import normalize\n",
    "from stardist.models import StarDist2D\n",
    "from shapely.geometry import Polygon, Point\n",
    "from scipy import sparse\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916681bd-cb46-4a45-822b-a595336d6c71",
   "metadata": {},
   "source": [
    "# Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5716616f-620b-4fb1-b148-30be437cbc0a",
   "metadata": {},
   "source": [
    "There are 5 helper functions below:\n",
    "* General image plotting functions (3)\n",
    "* Plotting function for nuclei area distribution\n",
    "* Total UMI distribution plotting function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb720e44-8882-4a40-8b89-62bc3e45ee8e",
   "metadata": {},
   "source": [
    "## 1. General image plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9ea6401-0907-4b6f-b007-6ad7e94d5482",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mask_and_save_image(title, gdf, img, cmap, output_name=None, bbox=None):\n",
    "    if bbox is not None:\n",
    "        # Crop the image to the bounding box\n",
    "        cropped_img = img[bbox[1]:bbox[3], bbox[0]:bbox[2]]\n",
    "    else:\n",
    "        cropped_img = img\n",
    "\n",
    "    # Plot options\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "    # Plot the cropped image\n",
    "    axes[0].imshow(cropped_img, cmap='gray', origin='lower')\n",
    "    axes[0].set_title(title)\n",
    "    axes[0].axis('off')\n",
    "\n",
    "    # Create filtering polygon\n",
    "    if bbox is not None:\n",
    "        bbox_polygon = Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])])\n",
    "        # Filter for polygons in the box\n",
    "        intersects_bbox = gdf['geometry'].intersects(bbox_polygon)\n",
    "        filtered_gdf = gdf[intersects_bbox]\n",
    "    else:\n",
    "        filtered_gdf=gdf\n",
    "\n",
    "    # Plot the filtered polygons on the second axis\n",
    "    filtered_gdf.plot(cmap=cmap, ax=axes[1])\n",
    "    axes[1].axis('off')\n",
    "    axes[1].legend(loc='upper left', bbox_to_anchor=(1.05, 1))  \n",
    "\n",
    "\n",
    "    # Save the plot if output_name is provided\n",
    "    if output_name is not None:\n",
    "        plt.savefig(output_name, bbox_inches='tight')  # Use bbox_inches='tight' to include the legend\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40830839-bdb7-4453-9ac5-5a3500d371ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_clusters_and_save_image(title, gdf,img, adata, bbox=None, color_by_obs=None, output_name=None):\n",
    "\n",
    "    if bbox is not None:\n",
    "        # Crop the image to the bounding box\n",
    "        cropped_img = img[bbox[1]:bbox[3], bbox[0]:bbox[2]]\n",
    "    else:\n",
    "        cropped_img = img\n",
    "\n",
    "    # Plot options\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "    # Plot the cropped image\n",
    "    axes[0].imshow(cropped_img, cmap='gray', origin='lower')\n",
    "    axes[0].set_title(title)\n",
    "    axes[0].axis('off')\n",
    "\n",
    "    # Create filtering polygon\n",
    "    if bbox is not None:\n",
    "        bbox_polygon = Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])])\n",
    "\n",
    "    unique_values = adata.obs[color_by_obs].astype('category').cat.categories\n",
    "    num_categories = len(unique_values)\n",
    "    tab20_colors = plt.cm.tab20.colors[:num_categories]\n",
    "\n",
    "    # Create a ListedColormap mapping tab20 colors to unique values\n",
    "    custom_cmap = ListedColormap(tab20_colors, name='custom_tab20_cmap')\n",
    "\n",
    "    # Label all polygons with a cluster\n",
    "    merged_gdf = gdf.merge(adata.obs[color_by_obs].astype('category'), left_on='id', right_index=True)\n",
    "\n",
    "    if bbox is not None:\n",
    "        # Filter for polygons in the box\n",
    "        intersects_bbox = merged_gdf['geometry'].intersects(bbox_polygon)\n",
    "        filtered_gdf = merged_gdf[intersects_bbox]\n",
    "    else:\n",
    "        filtered_gdf = merged_gdf\n",
    "\n",
    "    # Plot the filtered polygons on the second axis\n",
    "    filtered_gdf.plot(column=color_by_obs, cmap=custom_cmap,ax=axes[1],legend=True)\n",
    "    axes[1].set_title(color_by_obs)\n",
    "    legend = axes[1].get_legend()\n",
    "    legend.set_bbox_to_anchor((1.05, 1))\n",
    "    axes[1].axis('off')\n",
    "\n",
    "    # Save the plot if output_name is provided\n",
    "    if output_name is not None:\n",
    "        plt.savefig(output_name, bbox_inches='tight')  # Use bbox_inches='tight' to include the legend\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2065778f-f7a8-4699-af75-6afb4cabcec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_gene_and_save_image(title, gdf, gene, img, adata, bbox=None, output_name=None):\n",
    "\n",
    "    if bbox is not None:\n",
    "        # Crop the image to the bounding box\n",
    "        cropped_img = img[bbox[1]:bbox[3], bbox[0]:bbox[2]]\n",
    "    else:\n",
    "        cropped_img = img\n",
    "\n",
    "    # Plot options\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "    # Plot the cropped image\n",
    "    axes[0].imshow(cropped_img, cmap='gray', origin='lower')\n",
    "    axes[0].set_title(title)\n",
    "    axes[0].axis('off')\n",
    "\n",
    "    # Create filtering polygon\n",
    "    if bbox is not None:\n",
    "        bbox_polygon = Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])])\n",
    "\n",
    "\n",
    "    # Find a gene of interest and merge with the geodataframe\n",
    "    gene_expression = adata[:, gene].to_df()\n",
    "    gene_expression['id'] = gene_expression.index\n",
    "    merged_gdf = gdf.merge(gene_expression, left_on='id', right_on='id')\n",
    "\n",
    "    if bbox is not None:\n",
    "        # Filter for polygons in the box\n",
    "        intersects_bbox = merged_gdf['geometry'].intersects(bbox_polygon)\n",
    "        filtered_gdf = merged_gdf[intersects_bbox]\n",
    "    else:\n",
    "        filtered_gdf = merged_gdf\n",
    "\n",
    "    # Plot the filtered polygons on the second axis\n",
    "    filtered_gdf.plot(column=gene, cmap='inferno', legend=True, ax=axes[1])\n",
    "    axes[1].set_title(gene)\n",
    "    axes[1].axis('off')\n",
    "    axes[1].legend(loc='upper left', bbox_to_anchor=(1.05, 1))  \n",
    "\n",
    "    # Save the plot if output_name is provided\n",
    "    if output_name is not None:\n",
    "        plt.savefig(output_name, bbox_inches='tight')  # Use bbox_inches='tight' to include the legend\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146de7f3-2283-4b8d-a36d-087a7e5fb53a",
   "metadata": {},
   "source": [
    "## Plotting function for nuclei area distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df827a4b-b6e9-4265-9f9b-cc4382018126",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_nuclei_area(gdf,area_cut_off):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(15, 4))\n",
    "    # Plot the histograms\n",
    "    axs[0].hist(gdf['area'], bins=50, edgecolor='black')\n",
    "    axs[0].set_title('Nuclei Area')\n",
    "\n",
    "    axs[1].hist(gdf[gdf['area'] < area_cut_off]['area'], bins=50, edgecolor='black')\n",
    "    axs[1].set_title('Nuclei Area Filtered:'+str(area_cut_off))\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123dafdc-fe2f-40ef-bbe8-167cbf285b1c",
   "metadata": {},
   "source": [
    "## Total UMI distribution plotting function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0256e92c-3239-4081-a32a-35afbf83db6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_umi(adata_, cut_off):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "    # Box plot\n",
    "    axs[0].boxplot(adata_.obs[\"total_counts\"], vert=False, widths=0.7, patch_artist=True, boxprops=dict(facecolor='skyblue'))\n",
    "    axs[0].set_title('Total Counts')\n",
    "\n",
    "    # Box plot after filtering\n",
    "    axs[1].boxplot(adata_.obs[\"total_counts\"][adata_.obs[\"total_counts\"] > cut_off], vert=False, widths=0.7, patch_artist=True, boxprops=dict(facecolor='skyblue'))\n",
    "    axs[1].set_title('Total Counts > ' + str(cut_off))\n",
    "\n",
    "    # Remove y-axis ticks and labels\n",
    "    for ax in axs:\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558e7e02-0b14-4690-8f88-66d24325a9b6",
   "metadata": {},
   "source": [
    "# Nuclei Mask and GeoDataframe Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37df6e18-d309-4dfb-b89b-31c06e0a5714",
   "metadata": {},
   "source": [
    "In the following code block change the file path to the location of the high-resolution image. After importing the image, it is percentile normalized. Adjust the min and max percentile parameters as needed for other images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40672316-65b1-4f77-8bd2-01c25a273239",
   "metadata": {},
   "source": [
    "Load the image file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a0075a4-e42d-42e3-bc95-1124891c4591",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change dir_base as needed to the directory where the downloaded example data is stored\n",
    "dir_base = '/Users/lisa.johnson/Documents/analysis_guides_testing/nuclei_segmentation_custom_binning_input_files_temp/'\n",
    "filename = 'SJ0309_MsSI_slide08_01_20x_BF_01.btf'\n",
    "img = imread(dir_base + filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60151dd3-1c3d-41b8-b466-f50701f4e965",
   "metadata": {},
   "source": [
    "Load the pretrained model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c069757-d262-4fd4-87fa-6d98abee8cbd",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'StarDist2D' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mStarDist2D\u001b[49m\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2D_versatile_he\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'StarDist2D' is not defined"
     ]
    }
   ],
   "source": [
    "model = StarDist2D.from_pretrained('2D_versatile_he')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1cd3f42-35bb-4589-843e-b5a791a9e4d5",
   "metadata": {},
   "source": [
    "Percentile normalization of the image, adjust `min_percentile` and `max_percentile` as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd6f1a56-88d9-4d28-a644-d2ab8623064d",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_percentile = 5\n",
    "max_percentile = 95\n",
    "img = normalize(img, min_percentile, max_percentile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca18608-6903-4aa3-9a84-df855141b807",
   "metadata": {},
   "source": [
    "Execute the code block below to create the cell nuclei mask. Note this step has a long runtime and it will vary significantly depending on the chosen parameter settings. The nms threshold (nms_thresh) is set to a small number to reduce the probability of nuclei overlap. It may be necessary to adjust the probability threshold (prob_thresh) parameter for different images. Larger values will result in a reduction of segmented objects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a50f14e0-37b2-43f0-86d4-bbaec192c348",
   "metadata": {},
   "source": [
    "Predict cell nuclei using the normalized image. Adjust nms_thresh and prob_thresh as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68ee1d8a-a63d-4289-b6b1-afcc82dbd7d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels, polys = model.predict_instances_big(img, axes='YXC', block_size=4096, prob_thresh=0.01,nms_thresh=0.001, min_overlap=128, context=128, normalizer=None, n_tiles=(4,4,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0a3d91-e6a1-4352-8651-71fc7dd20a5d",
   "metadata": {},
   "source": [
    "In the next code block we convert the results from StarDist into a Geodataframe. We will use the Geodataframe for barcode binning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "509ea980-1910-4551-9d19-1fdbf6cec89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a list to store Polygon geometries\n",
    "geometries = []\n",
    "\n",
    "# Iterating through each nuclei in the 'polys' DataFrame\n",
    "for nuclei in range(len(polys['coord'])):\n",
    "\n",
    "    # Extracting coordinates for the current nuclei and converting them to (y, x) format\n",
    "    coords = [(y, x) for x, y in zip(polys['coord'][nuclei][0], polys['coord'][nuclei][1])]\n",
    "\n",
    "    # Creating a Polygon geometry from the coordinates\n",
    "    geometries.append(Polygon(coords))\n",
    "\n",
    "# Creating a GeoDataFrame using the Polygon geometries\n",
    "gdf = gpd.GeoDataFrame(geometry=geometries)\n",
    "gdf['id'] = [f\"ID_{i+1}\" for i, _ in enumerate(gdf.index)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384e673d-3fa1-4c17-a6c6-53862da4c7ca",
   "metadata": {},
   "source": [
    "The results for one region of interest from the nuclei segmentation are shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85726943-6e7d-4686-999c-63d19bcd9759",
   "metadata": {},
   "outputs": [],
   "source": [
    "Plot the nuclei segmentation\n",
    "# bbox=(x min,y min,x max,y max)\n",
    "\n",
    "# Define a single color cmap\n",
    "cmap=ListedColormap(['grey'])\n",
    "\n",
    "# Create Plot\n",
    "plot_mask_and_save_image(title=\"Region of Interest 1\",gdf=gdf,bbox=(10912,6656,11632,7632),cmap=cmap,img=img,output_name=dir_base+\"image_mask.ROI1.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1e0911-47f8-4d02-9265-4bcaa42a0437",
   "metadata": {},
   "source": [
    "# Binning Visium HD Gene Expression Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a5cf132-092a-46fa-bb04-7207be1fdf1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform a spatial join to check which coordinates are in a cell nucleus\n",
    "result_spatial_join = gpd.sjoin(gdf_coordinates, gdf, how='left', predicate='within')\n",
    "\n",
    "# Identify nuclei associated barcodes and find barcodes that are in more than one nucleus\n",
    "result_spatial_join['is_within_polygon'] = ~result_spatial_join['index_right'].isna()\n",
    "barcodes_in_overlaping_polygons = pd.unique(result_spatial_join[result_spatial_join.duplicated(subset=['index'])]['index'])\n",
    "result_spatial_join['is_not_in_an_polygon_overlap'] = ~result_spatial_join['index'].isin(barcodes_in_overlaping_polygons)\n",
    "\n",
    "# Remove barcodes in overlapping nuclei\n",
    "barcodes_in_one_polygon = result_spatial_join[result_spatial_join['is_within_polygon'] & result_spatial_join['is_not_in_an_polygon_overlap']]\n",
    "\n",
    "# The AnnData object is filtered to only contain the barcodes that are in non-overlapping polygon regions\n",
    "filtered_obs_mask = adata.obs_names.isin(barcodes_in_one_polygon['index'])\n",
    "filtered_adata = adata[filtered_obs_mask,:]\n",
    "\n",
    "# Add the results of the point spatial join to the Anndata object\n",
    "filtered_adata.obs =  pd.merge(filtered_adata.obs, barcodes_in_one_polygon[['index','geometry','id','is_within_polygon','is_not_in_an_polygon_overlap']], left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c459d62-c600-4a78-9e63-b395337ba141",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data by unique nucleous IDs\n",
    "groupby_object = filtered_adata.obs.groupby(['id'], observed=True)\n",
    "\n",
    "# Extract the gene expression counts from the AnnData object\n",
    "counts = filtered_adata.X\n",
    "\n",
    "# Obtain the number of unique nuclei and the number of genes in the expression data\n",
    "N_groups = groupby_object.ngroups\n",
    "N_genes = counts.shape[1]\n",
    "\n",
    "# Initialize a sparse matrix to store the summed gene counts for each nucleus\n",
    "summed_counts = sparse.lil_matrix((N_groups, N_genes))\n",
    "\n",
    "# Lists to store the IDs of polygons and the current row index\n",
    "polygon_id = []\n",
    "row = 0\n",
    "\n",
    "# Iterate over each unique polygon to calculate the sum of gene counts.\n",
    "for polygons, idx_ in groupby_object.indices.items():\n",
    "    summed_counts[row] = counts[idx_].sum(0)\n",
    "    row += 1\n",
    "    polygon_id.append(polygons)\n",
    "\n",
    "# Create and AnnData object from the summed count matrix\n",
    "summed_counts = summed_counts.tocsr()\n",
    "grouped_filtered_adata = anndata.AnnData(X=summed_counts,obs=pd.DataFrame(polygon_id,columns=['id'],index=polygon_id),var=filtered_adata.var)\n",
    "\n",
    "%store grouped_filtered_adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c6fe8ad-497b-469f-9174-7445fb1c1b19",
   "metadata": {},
   "source": [
    "# Nuclei Binning Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a37d51-7eda-4b13-8377-83e47512369c",
   "metadata": {},
   "source": [
    "In this demonstration, we filter the results to remove very large nuclei, which could be improperly segmented nuclei aggregates, and remove nuclei with too few UMI counts to make cluster interpretation and visualization easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fe5442a-18da-4ae0-bf86-0bdf000a1657",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the area of each nucleus in the GeoDataframe\n",
    "gdf['area'] = gdf['geometry'].area\n",
    "\n",
    "# Calculate quality control metrics for the original AnnData object\n",
    "sc.pp.calculate_qc_metrics(grouped_filtered_adata, inplace=True)\n",
    "\n",
    "# Plot the nuclei area distribution before and after filtering\n",
    "plot_nuclei_area(gdf=gdf,area_cut_off=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "392e2733-94ed-4f49-b8d8-960c73803a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot total UMI distribution\n",
    "total_umi(grouped_filtered_adata, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d26cfe-59c9-4d24-beae-9d5066665b90",
   "metadata": {},
   "source": [
    "Filter data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b16fda86-8509-4dc7-a37b-693fbfdd2265",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a mask based on the 'id' column for values present in 'gdf' with 'area' less than 500\n",
    "mask_area = grouped_filtered_adata.obs['id'].isin(gdf[gdf['area'] < 500].id)\n",
    "\n",
    "# Create a mask based on the 'total_counts' column for values greater than 100\n",
    "mask_count = grouped_filtered_adata.obs['total_counts'] > 100\n",
    "\n",
    "# Apply both masks to the original AnnData to create a new filtered AnnData object\n",
    "count_area_filtered_adata = grouped_filtered_adata[mask_area & mask_count, :]\n",
    "\n",
    "# Calculate quality control metrics for the filtered AnnData object\n",
    "sc.pp.calculate_qc_metrics(count_area_filtered_adata, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b981424-ca73-4fef-b521-0d3d564467de",
   "metadata": {},
   "source": [
    "Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e516958e-f3a3-4f83-a077-a3f321a0e7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize total counts for each cell in the AnnData object\n",
    "sc.pp.normalize_total(count_area_filtered_adata, inplace=True)\n",
    "\n",
    "# Logarithmize the values in the AnnData object after normalization\n",
    "sc.pp.log1p(count_area_filtered_adata)\n",
    "\n",
    "# Identify highly variable genes in the dataset using the Seurat method\n",
    "sc.pp.highly_variable_genes(count_area_filtered_adata, flavor=\"seurat\", n_top_genes=2000)\n",
    "\n",
    "# Perform Principal Component Analysis (PCA) on the AnnData object\n",
    "sc.pp.pca(count_area_filtered_adata)\n",
    "\n",
    "# Build a neighborhood graph based on PCA components\n",
    "sc.pp.neighbors(count_area_filtered_adata)\n",
    "\n",
    "# Perform Leiden clustering on the neighborhood graph and store the results in 'clusters' column\n",
    "\n",
    "# Adjust the resolution parameter as needed for different samples\n",
    "sc.tl.leiden(count_area_filtered_adata, resolution=0.85, key_added=\"clusters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "809074eb-8283-4a28-860c-c037450d6337",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot and save the clustering results\n",
    "plot_clusters_and_save_image(title=\"Region of interest 1\", gdf=gdf, img=img, adata=count_area_filtered_adata, bbox=(10912,6656,11632,7632), color_by_obs='clusters', output_name=dir_base+\"image_clustering.ROI1.tiff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58c4aa73-723f-4575-9d6e-e644928bcb2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot Lyz1 gene expression\n",
    "plot_gene_and_save_image(title=\"Region of interest 1\", gdf=gdf, gene='Lyz1', img=img, adata=count_area_filtered_adata, bbox=(10912,6656,11632,7632),output_name=dir_base+\"image_Lyz1.ROI1.tiff\", bbox=None, output_name=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "154a2c07-7056-474f-b142-0770d6b93435",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot Muc2 gene expression\n",
    "plot_gene_and_save_image(title=\"Region of interest 1\", gdf=gdf, gene='Muc2', img=img, adata=count_area_filtered_adata, bbox=(10912,6656,11632,7632),output_name=dir_base+\"image_Muc2.ROI1.tiff\", bbox=None, output_name=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79a1f0a7-8c01-46e4-aca3-db99a4004f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot Fabp2 expression\n",
    "plot_gene_and_save_image(title=\"Region of interest 1\", gdf=gdf, gene='Fabp2', img=img, adata=count_area_filtered_adata, bbox=(10912,6656,11632,7632),output_name=dir_base+\"image_Fabp2.ROI1.tiff\", bbox=None, output_name=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c97adcf0-08b9-4eba-8e72-1356f5f9414b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot Jchain expression\n",
    "plot_gene_and_save_image(title=\"Region of interest 1\", gdf=gdf, gene='Jchain', img=img, adata=count_area_filtered_adata, bbox=(10912,6656,11632,7632),output_name=dir_base+\"image_Jchain.ROI1.tiff\", bbox=None, output_name=None)"
   ]
  }
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