CellProfiler/statistics_fruit_fly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Images stored on the OMERO server using Cell Profiler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to retrieve Images stored in OMERO and\n",
    "process them using [CellProfiler](http://cellprofiler.org/).\n",
    "The output is saved back to OMERO as CSV attachments.\n",
    "For this example, we use\n",
    "[FruitFlyCells](http://cellprofiler.org/examples/#FruitFlyCells)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Workflow Summary\n",
    "![Overview](images/fruit_fly_workflow.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %tb\n",
    "\n",
    "# Import Cell Profiler Dependencies\n",
    "import cellprofiler.preferences as cpprefs\n",
    "import cellprofiler.pipeline as cpp\n",
    "\n",
    "# Inject Image module used to inject OMERO image planes\n",
    "# into Cell Profiler Pipeline\n",
    "from cellprofiler.modules.injectimage import InjectImage\n",
    "\n",
    "# Import OMERO Python BlitzGateway\n",
    "from omero.gateway import BlitzGateway\n",
    "\n",
    "# Import Python System Packages\n",
    "import glob\n",
    "import os\n",
    "from os.path import expanduser\n",
    "import pandas\n",
    "import tempfile\n",
    "import warnings\n",
    "\n",
    "from getpass import getpass\n",
    "cpprefs.set_headless()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Cell Output Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_output_directory = os.path.normcase(tempfile.mkdtemp())\n",
    "cpprefs.set_default_output_directory(new_output_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OMERO Credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HOST = 'workshop.openmicroscopy.org'\n",
    "PORT = 4064\n",
    "conn = BlitzGateway(input(\"Username: \"),\n",
    "                    getpass(\"OMERO Password: \"),\n",
    "                    host=HOST, port=PORT)\n",
    "conn.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch OMERO Dataset that contains the Images to be analysed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be modified\n",
    "# Please fill in the datasetId for the dataset named 'CellProfiler images'\n",
    "datasetid = 602\n",
    "dataset = conn.getObject(\"Dataset\", datasetid)\n",
    "print('Dataset Name: ', dataset.getName())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pipeline and inspect modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "home = expanduser(\"~\")\n",
    "pipeline = cpp.Pipeline()\n",
    "file = \"/notebooks/CellProfiler/pipelines/ExampleFly.cppipe\"\n",
    "pipeline.load(home+file)\n",
    "\n",
    "# Remove first 4 modules: Images, Metadata, NamesAndTypes, Groups...\n",
    "# (replaced by InjectImage module below)\n",
    "for i in range(4):\n",
    "    print('Remove module: ', pipeline.modules()[0].module_name)\n",
    "    pipeline.remove_module(1)\n",
    "print('Pipeline modules:')\n",
    "for module in pipeline.modules():\n",
    "    print(module.module_num, module.module_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Cell Profiler Pipeline on the OMERO Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "Nuclei = pandas.DataFrame()\n",
    "Cells = pandas.DataFrame()\n",
    "Cytoplasm = pandas.DataFrame()\n",
    "Image = pandas.DataFrame()\n",
    "\n",
    "for image in dataset.listChildren():\n",
    "\n",
    "    pixels = image.getPrimaryPixels()\n",
    "    size_c = image.getSizeC()\n",
    "    # For each Image in OMERO, we copy pipeline and inject image modules\n",
    "    pipeline_copy = pipeline.copy()\n",
    "    for c in range(0, size_c):\n",
    "\n",
    "        plane = pixels.getPlane(0, c, 0)\n",
    "        image_name = image.getName()\n",
    "\n",
    "        # Name of the channel expected in the pipeline\n",
    "        if c == 0:\n",
    "            Image_Name = 'OrigBlue'\n",
    "        if c == 1:\n",
    "            Image_Name = 'OrigGreen'\n",
    "        if c == 2:\n",
    "            Image_Name = 'OrigRed'\n",
    "\n",
    "        inject_image_module = InjectImage(Image_Name, plane)\n",
    "        inject_image_module.set_module_num(1)\n",
    "        pipeline_copy.add_module(inject_image_module)\n",
    "\n",
    "    m = pipeline_copy.run()\n",
    "\n",
    "    # Results obtained as CSV from Cell Profiler\n",
    "    nuc_csv = pandas.read_csv(new_output_directory + '/Nuclei.csv')\n",
    "    nuc_csv.ImageNumber = image.getName()\n",
    "    Nuclei = Nuclei.append(nuc_csv)\n",
    "\n",
    "    cel_csv = pandas.read_csv(new_output_directory + '/Cells.csv')\n",
    "    cel_csv.ImageNumber = image.getName()\n",
    "    Cells = Cells.append(cel_csv)\n",
    "\n",
    "    cyto_csv = pandas.read_csv(new_output_directory + '/Cytoplasm.csv')\n",
    "    cyto_csv.ImageNumber = image.getName()\n",
    "    Cytoplasm = Cytoplasm.append(cyto_csv)\n",
    "\n",
    "    image_csv = pandas.read_csv(new_output_directory + '/Image.csv')\n",
    "    image_csv.ImageNumber = image.getName()\n",
    "    Image = Image.append(image_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results obtained as CSV from Cell Profiler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measurements (Nuclei)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nuclei.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measurements (Cells)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cells.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measurements (Cytoplasm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cytoplasm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Images (Summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Push Results back to OMERO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file_to_upload in glob.glob(new_output_directory + '/*.csv'):\n",
    "    if file_to_upload.endswith('Nuclei.csv'):\n",
    "        Nuclei.to_csv(file_to_upload)\n",
    "    elif file_to_upload.endswith('Cells.csv'):\n",
    "        Cells.to_csv(file_to_upload)\n",
    "    elif file_to_upload.endswith('Cytoplasm.csv'):\n",
    "        Cytoplasm.to_csv(file_to_upload)\n",
    "    elif file_to_upload.endswith('Image.csv'):\n",
    "        Image.to_csv(file_to_upload)\n",
    "    # create the original file and file annotation (uploads the file etc.)\n",
    "    namespace = \"training.meeting.demo\"\n",
    "    print(\"\\nCreating an OriginalFile and FileAnnotation\")\n",
    "    file_ann = conn.createFileAnnfromLocalFile(file_to_upload,\n",
    "                                               mimetype=\"text/plain\",\n",
    "                                               ns=namespace, desc=None)\n",
    "    print(\"Attaching FileAnnotation to Dataset: \",\n",
    "          \"File ID:\", file_ann.getId(),\n",
    "          \",\", file_ann.getFile().getName(),\n",
    "          \"Size:\", file_ann.getFile().getSize())\n",
    "    dataset.linkAnnotation(file_ann)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Close the connection to the OMERO server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### License\n",
    "Copyright (C) 2019 University of Dundee. All Rights Reserved.\n",
    "This program is free software; you can redistribute it and/or modify it\n",
    "under the terms of the GNU General Public License as published by the\n",
    "Free Software Foundation; either version 2 of the License, or\n",
    "(at your option) any later version.\n",
    "This program is distributed in the hope that it will be useful, but\n",
    "WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY\n",
    "or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for\n",
    "more details. You should have received a copy of the GNU General\n",
    "Public License along with this program; if not, write to the\n",
    "Free Software Foundation,\n",
    "Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA."
   ]
  }
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