Figure_1_Sampling_of_Phenotypes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Reproducing Fig. 1\n",
    "This notebook exemplifies how to reproduce Figure 1 of the article.\n",
    "The annotations from all screens will be downloaded and parsed to\n",
    "build statistics on phenotypes, which will be displayed using Bokeh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependencies\n",
    " * [Matplotlib](https://matplotlib.org/)\n",
    " * [NumPy](https://www.numpy.org/)\n",
    " * [Pandas](https://pandas.pydata.org/)\n",
    " * [Bokeh](https://bokeh.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "    root._bokeh_onload_callbacks = [];\n",
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       "\n",
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       "      delete Bokeh.index[id];\n",
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       "\n",
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       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
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       "      cell.notebook.kernel.execute(cmd);\n",
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       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
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       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
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       "\n",
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       "      Bokeh.set_log_level(\"info\");\n",
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       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n",
       "    }\n",
       "  ];\n",
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       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
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       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
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       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"8e1fdc26-93d2-44b0-b0d6-663404a24403\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"8e1fdc26-93d2-44b0-b0d6-663404a24403\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8e1fdc26-93d2-44b0-b0d6-663404a24403' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.11.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"8e1fdc26-93d2-44b0-b0d6-663404a24403\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython import get_ipython\n",
    "import omero\n",
    "from idr import connection\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from pandas import DataFrame\n",
    "from pandas import read_csv\n",
    "\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.plotting import output_notebook\n",
    "from bokeh.plotting import show\n",
    "from bokeh.models import HoverTool\n",
    "import bokeh.palettes as bpal\n",
    "\n",
    "output_notebook()\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getBulkAnnotationAsDf(screenID, conn):\n",
    "    ofId = None\n",
    "    sc = conn.getObject('Screen', screenID)\n",
    "    for ann in sc.listAnnotations():\n",
    "        if isinstance(ann, omero.gateway.FileAnnotationWrapper):\n",
    "            if (ann.getFile().getName() == 'bulk_annotations'):\n",
    "                if (ann.getFile().getSize() > 1476250900):\n",
    "                    print(\"that's a big file...\")\n",
    "                    return None\n",
    "                ofId = ann.getFile().getId()\n",
    "                break\n",
    "\n",
    "    if ofId is None:\n",
    "        return None\n",
    "\n",
    "    original_file = omero.model.OriginalFileI(ofId, False)\n",
    "\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",
    "        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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def appendPhInfo(phall, screen, df):\n",
    "    \"\"\"\n",
    "    extract all phenotypes information from given bulk annotation file and\n",
    "    append it to the phall\n",
    "    \"\"\"\n",
    "    phcol = df.columns[[('Phenotype' in s) and ('Term Accession' in s)\n",
    "                        for s in df.columns]]\n",
    "    for s in phcol:\n",
    "        ph = df[s].unique()\n",
    "        if ph[0] != '':\n",
    "            ph = ph[0]\n",
    "            desc = df[s.replace('Accession', 'Name')].unique()[0]\n",
    "        else:\n",
    "            ph = ph[1]\n",
    "            desc = df[s.replace('Accession', 'Name')].unique()[1]\n",
    "        dfph = df[df[s] != '']\n",
    "        try:\n",
    "            phall[ph]['n'] = phall[ph]['n']+len(dfph)\n",
    "            if not (screen in phall[ph]['screens']):\n",
    "                phall[ph]['screens'].append(screen)\n",
    "        except Exception:\n",
    "            phcur = {'n': len(dfph), 'screens': [screen], 'desc': desc}\n",
    "            phall[ph] = phcur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the IDR server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to IDR...\n"
     ]
    }
   ],
   "source": [
    "conn = connection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and display figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n",
      "[============================================================] 100.0% ...Iterating through screens\r"
     ]
    }
   ],
   "source": [
    "screens = list(conn.getObjects(\"Screen\"))\n",
    "screen_count = len(screens)\n",
    "print(screen_count)\n",
    "\n",
    "phall = {}\n",
    "# List of screens used for paper\n",
    "screen_ids = [3, 102, 51, 202, 597, 253, 201, 154, 751, 206,\n",
    "              251, 803, 1351, 1501, 1551, 1601, 1602, 1603, 1202, 1101, 1302,\n",
    "              1201, 1251, 1151, 1203, 1204, 1651, 1652, 1653, 1654]\n",
    "print(\"Iterating through screens...\")\n",
    "for sc in screens:\n",
    "    sc_id = sc.getId()\n",
    "    print('loading ' + str(sc_id))\n",
    "    if sc_id in screen_ids:\n",
    "        df = getBulkAnnotationAsDf(sc_id, conn)\n",
    "        if df is not None:\n",
    "            appendPhInfo(phall, sc.getName(), df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disconnect when done loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load grouping of phenotypes for ordering and coloring them\n",
    "Categories were chosen manually offline\n",
    "(see [paper](https://www.nature.com/articles/nmeth.4326))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfColor = read_csv('./includes/CMPOAccessionToPhenotypeCategories.csv')\n",
    "colors = {}\n",
    "for i, grp in enumerate(dfColor.CmpoPhenotypeCategory.unique()):\n",
    "    colors[grp] = bpal.Set3_10[i % 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pass:CMPO_0000458\n",
      "pass:CMPO_0000390\n",
      "pass:CMPO_0000018\n",
      "pass:CMPO_0000446\n",
      "pass:CMPO_0000447\n",
      "pass:CMPO_0000444\n",
      "pass:CMPO_0000445\n",
      "pass:CMPO_0000442\n",
      "pass:CMPO_0000443\n",
      "pass:CMPO_0000440\n",
      "pass:CMPO_0000441\n",
      "pass:CMPO_0000450\n",
      "pass:CMPO_0000453\n",
      "pass:CMPO_0000454\n",
      "pass:CMPO_0000457\n",
      "pass:CMPO_0000113\n",
      "pass:CMPO_0000422\n",
      "pass:CMPO_0000023\n"
     ]
    }
   ],
   "source": [
    "# add the information to the data and sort it\n",
    "for ph in phall.keys():\n",
    "    try:\n",
    "        v = dfColor['CmpoAcc'] == ph\n",
    "        phall[ph]['group'] = dfColor[v]['CmpoPhenotypeCategory'].values[0]\n",
    "        phall[ph]['groupColor'] = colors[phall[ph]['group']]\n",
    "        phall[ph]['FigureCmpoName'] = dfColor[v]['FigureCmpoName'].values[0]\n",
    "    except Exception:\n",
    "        print('pass:'+ph)\n",
    "        del phall[ph]\n",
    "\n",
    "phalls = sorted(phall.values(), key=lambda x: x['group'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"ec713a69-00a7-4861-bac1-cb73f5c46939\"></div>\n",
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       "    var docs_json = 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1\"},\"id\":\"2ccaa313-9129-40b2-8100-d73f9a8432f7\",\"type\":\"Title\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"fab25230-ab40-4d68-b039-e6ea8809b01e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"fe2968af-dea6-40d7-93ee-369c99d2fe72\",\"type\":\"LogTicker\"}},\"id\":\"fc0732d1-8028-4a22-8ee9-4f91ea035d60\",\"type\":\"Grid\"}],\"root_ids\":[\"fab25230-ab40-4d68-b039-e6ea8809b01e\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.11\"}};\n",
       "    var render_items = [{\"docid\":\"846d7532-a55b-4381-a284-fa21b5af2e31\",\"elementid\":\"ec713a69-00a7-4861-bac1-cb73f5c46939\",\"modelid\":\"fab25230-ab40-4d68-b039-e6ea8809b01e\"}];\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "fab25230-ab40-4d68-b039-e6ea8809b01e"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOOLS = \"pan,wheel_zoom,reset\"\n",
    "\n",
    "phenotypes = figure(title=\"Fig 1\",\n",
    "                    tools=TOOLS,\n",
    "                    y_axis_type=\"log\",\n",
    "                    width=900,\n",
    "                    toolbar_location=\"above\")\n",
    "\n",
    "source = ColumnDataSource(\n",
    "    data=dict(\n",
    "        ph=[ph['FigureCmpoName'] for ph in phalls],\n",
    "        n=[ph['n'] for ph in phalls],\n",
    "        names=[ph['screens'] for ph in phalls],\n",
    "        desc=[ph['desc'] for ph in phalls],\n",
    "        x=[2*x for x in range(len(phall.keys()))],\n",
    "        r=[1*len(ph['screens']) for ph in phalls],\n",
    "        color=[ph['groupColor'] for ph in phalls],\n",
    "        groupName=[ph['group'] for ph in phalls]\n",
    "    ))\n",
    "\n",
    "label_data = {2*i: x for i, x in\n",
    "              enumerate([ph['FigureCmpoName'] for ph in phalls])}\n",
    "cir = phenotypes.circle('x', 'n', radius='r', source=source, color='color')\n",
    "\n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"Term\", \"@ph\"),\n",
    "            (\"Description\", \"@desc\"),\n",
    "            (\"Number of samples\", \"@n\"),\n",
    "            (\"Screens name\", \"@names\"),\n",
    "            (\"group\", \"@groupName\")\n",
    "            ]\n",
    "    )\n",
    "\n",
    "phenotypes.add_tools(hover)\n",
    "phenotypes.xaxis.major_label_orientation = np.pi/4.\n",
    "phenotypes.xaxis.axis_label_text_font_size = \"10pt\"\n",
    "show(phenotypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### License\n",
    "Copyright (C) 2016-2019 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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