SysgroRoiLength.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sysgro ROI Length\n",
    "This notebook loads the polygons which are linked to the images\n",
    "of idr0001-graml-sysgro and compares the length of cells labelled\n",
    "with a particular gene e.g. ASH2 versus the wild type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependencies\n",
    " * [Matplotlib](https://matplotlib.org/)\n",
    " * [NumPy](https://www.numpy.org/)\n",
    " * [Pandas](https://pandas.pydata.org/)\n",
    " * [Scikit-image](https://scikit-image.org/)\n",
    "\n",
    "The cell below will install dependencies if you choose to run the notebook in [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb#recent=true)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install idr-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from idr import connection\n",
    "from pandas import DataFrame\n",
    "import omero\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ttest_ind\n",
    "import skimage.measure as skmes\n",
    "import skimage.transform as sktrans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "screenId = 3  # sysgro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getBulkAnnotationAsDf(screenID, conn):\n",
    "    \"\"\"\n",
    "    Download the annotation file from a screen as a Pandas DataFrame\n",
    "    \"\"\"\n",
    "    screen = conn.getObject('Screen', screenID)\n",
    "    for ann in screen.listAnnotations():\n",
    "        if isinstance(ann, omero.gateway.FileAnnotationWrapper):\n",
    "            if (ann.getFile().getName() == 'bulk_annotations'):\n",
    "                if (ann.getFile().getSize() > 147625090):\n",
    "                    print(\"that's a big file...\")\n",
    "                    return None\n",
    "                ofId = ann.getFile().getId()\n",
    "                break\n",
    "    original_file = omero.model.OriginalFileI(ofId, False)\n",
    "    table = conn.c.sf.sharedResources().openTable(original_file)\n",
    "    try:\n",
    "        rowCount = table.getNumberOfRows()\n",
    "        column_names = [col.name for col in table.getHeaders()]\n",
    "\n",
    "        black_list = []\n",
    "        column_indices = []\n",
    "        for column_name in column_names:\n",
    "            if column_name in black_list:\n",
    "                continue\n",
    "            column_indices.append(column_names.index(column_name))\n",
    "\n",
    "        table_data = table.slice(column_indices, None)\n",
    "    finally:\n",
    "        table.close()\n",
    "\n",
    "    data = []\n",
    "    for index in range(rowCount):\n",
    "        row_values = [column.values[index] for column in table_data.columns]\n",
    "        data.append(row_values)\n",
    "\n",
    "    dfAnn = DataFrame(data)\n",
    "    dfAnn.columns = column_names\n",
    "    return dfAnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getLengthsFromStrain(astrain, lenlen=100, verbose=False):\n",
    "    lengths = []\n",
    "    params = omero.sys.ParametersI()\n",
    "    key = sysgroba[\"Characteristics [Strain]\"]\n",
    "    wellids = sysgroba[key == astrain].Well.values\n",
    "    params.addIds(wellids)\n",
    "    # Wells and images that contain a ROI\n",
    "    imageswithrois = queryService.projection(\n",
    "        'SELECT w.id, i.id FROM Image i JOIN i.wellSamples s JOIN s.well w '\n",
    "        'WHERE w.id in (:ids) AND EXISTS '\n",
    "        '(SELECT 1 FROM Roi AS r WHERE r.image = i) ORDER BY i.id', params)\n",
    "    wellswithrois = {}\n",
    "    for wid, iid in omero.rtypes.unwrap(imageswithrois):\n",
    "        try:\n",
    "            wellswithrois[wid].append(iid)\n",
    "        except KeyError:\n",
    "            wellswithrois[wid] = [iid]\n",
    "\n",
    "    for wid in sorted(wellswithrois.keys()):\n",
    "        for imId in wellswithrois[wid]:\n",
    "            result = roiService.findByImage(imId, None)\n",
    "            if verbose:\n",
    "                v = 'Well-id:%d Image-id:%d rois:[%d]'\n",
    "                print(v % (wid, imId, len(result.rois)))\n",
    "            for ii in range(len(result.rois)):\n",
    "\n",
    "                # get the coordinates of the outline of the ROI\n",
    "                s = result.rois[ii].copyShapes()[0]\n",
    "                pts = s.getPoints().getValue()\n",
    "                pts = [int(xx) for x in pts.split(' ') for xx in x.split(',')]\n",
    "                pts = np.reshape(pts, (len(pts) // 2, 2))\n",
    "\n",
    "                # from coordinates to mask image\n",
    "                M0, m0 = pts[:, 0].max(), pts[:, 0].min()\n",
    "                M1, m1 = pts[:, 1].max(), pts[:, 1].min()\n",
    "                imroi = np.zeros((M0 - m0 + 1, M1 - m1 + 1))\n",
    "                for i in range(pts.shape[0]):\n",
    "                    imroi[pts[i, 0] - m0, pts[i, 1] - m1] = 1\n",
    "\n",
    "                iml = skmes.label(1-imroi, connectivity=1)\n",
    "                imroi = 1 * (iml == iml[iml.shape[0] // 2,\n",
    "                                        iml.shape[1] // 2])\n",
    "\n",
    "                # length of cell as length of bounding box of rotated image\n",
    "                # (thanks to the particular shape of yeast cells)\n",
    "                # default orientation is changing in 0.16 so -pi/2 to\n",
    "                # make relative to x-axis\n",
    "                regions = skmes.regionprops(1 * imroi, coordinates='rc')[0]\n",
    "                ori = regions.orientation - np.pi // 2\n",
    "                imroi = sktrans.rotate(1. * imroi, - ori // np.pi * 180,\n",
    "                                       resize=True, order=0)\n",
    "                regions = skmes.regionprops(skmes.label(imroi),\n",
    "                                            coordinates='rc')[0]\n",
    "                bbox = regions.bbox\n",
    "                lengths.append(bbox[3] - bbox[1])\n",
    "        # to speed things up when there are a lot of images...\n",
    "        if len(lengths) > lenlen:\n",
    "            break\n",
    "    return lengths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to IDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to IDR ...\n"
     ]
    }
   ],
   "source": [
    "conn = connection('idr.openmicroscopy.org')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "roiService = conn.getRoiService()\n",
    "queryService = conn.getQueryService()\n",
    "sysgroba = getBulkAnnotationAsDf(screenId, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract length of cells as stored in ROIs in IDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "WTls = getLengthsFromStrain('MS1404', lenlen=1000)\n",
    "ash2ls = getLengthsFromStrain('ash2', lenlen=1000)\n",
    "pixsize = .11  # could be taken from IDR metadata\n",
    "ash2ls = [x * pixsize for x in ash2ls]\n",
    "WTls = [x * pixsize for x in WTls]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disconnect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display comparison of wild type to perturbed cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "h1 = plt.hist(ash2ls, bins=50, alpha=.5)\n",
    "h2 = plt.hist(WTls, bins=50, alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-13.398273380158818, pvalue=2.5160251562338347e-39)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(WTls, ash2ls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### License (BSD 2-Clause)¶\n",
    "\n",
    "Copyright (C) 2017-2021 University of Dundee. All Rights Reserved.\n",
    "\n",
    "Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n",
    "\n",
    "Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
   ]
  }
 ],
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