{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries and global variables\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": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "from ipywidgets import widgets, interact, fixed\n",
    "from functools import wraps\n",
    "from IPython import get_ipython\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "from idr import connection\n",
    "from idr import create_http_session\n",
    "from idr import genes_of_interest_go\n",
    "from idr.widgets import textbox_widget\n",
    "from idr.widgets import dropdown_widget\n",
    "from idr import get_phenotypes_for_genelist, get_similar_genes\n",
    "\n",
    "from idr.visualizations import plot_idr_attributes, plot_string_interactions\n",
    "from idr.externalDBs import genes_of_interest_from_string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9823f09051a14b19b3c0993e6491db20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Select Organism:', options=('Homo sapiens', 'Saccharomyces cerevisiae'), value='Homo sap…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0ac7d808ac94c3faf5f153f2c38dc75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='9606', description='Taxonomy Id:', placeholder='Enter Taxonomy Id for Organism')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3adbe2506dc44aab8f8876309ea9680c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='GO:0008290', description='Gene Ontology Id:', placeholder='Enter GO Id')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd1d058fbad64d6dba67aec9f17f8078",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='', description='Manual Gene List:', placeholder='Comma seperated gene symbols')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "organisms_list = ['Homo sapiens', 'Saccharomyces cerevisiae']\n",
    "org_sel = dropdown_widget(organisms_list, 'Select Organism:', True)\n",
    "tax_id = textbox_widget('9606', 'Enter Taxonomy Id for Organism',\n",
    "                        \"Taxonomy Id:\", True)\n",
    "go_term = textbox_widget('GO:0008290', 'Enter GO Id',\n",
    "                         'Gene Ontology Id:', True)\n",
    "manual_gene_list = textbox_widget('', 'Comma seperated gene symbols',\n",
    "                                  'Manual Gene List:', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import query list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query list of genes: ['CAPZA3', 'CAPZA2', 'CAPZA1', 'ADD2', 'ADD1', 'CAPZB', 'HEL-S-86', 'MTPN', 'hCG_28646', 'CAPG']\n"
     ]
    }
   ],
   "source": [
    "go_gene_list = []\n",
    "if go_term.value.split(\",\") != ['']:\n",
    "    go_gene_list = genes_of_interest_go(go_term.value, tax_id.value)\n",
    "else:\n",
    "    print('Please enter a valid Gene Ontology Id')\n",
    "manual_list = manual_gene_list.value.split(\",\")\n",
    "if manual_list != ['']:\n",
    "    go_gene_list = list(set(go_gene_list + manual_list))\n",
    "print(\"Query list of genes:\", go_gene_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query IDR for Phenotypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[============================================================] 100.0% ...Iterating through gene list\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entrez</th>\n",
       "      <th>Ensembl</th>\n",
       "      <th>Key</th>\n",
       "      <th>Value</th>\n",
       "      <th>PhenotypeName</th>\n",
       "      <th>PhenotypeAccession</th>\n",
       "      <th>ScreenIds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CAPZA3</th>\n",
       "      <td>[93661]</td>\n",
       "      <td>[ENSG00000177938]</td>\n",
       "      <td>GeneName</td>\n",
       "      <td>CAPZA3</td>\n",
       "      <td>[misshapen DNA, elongated cell phenotype, fan-...</td>\n",
       "      <td>[CMPO_0000267, CMPO_0000274, CMPO_0000299, CMP...</td>\n",
       "      <td>[206, 206, 206, 206, 206, 206, 206, 206, 206, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAPZB</th>\n",
       "      <td>[832]</td>\n",
       "      <td>[ENSG00000077549]</td>\n",
       "      <td>GeneName</td>\n",
       "      <td>CAPZB</td>\n",
       "      <td>[decreased cell numbers, fan-shaped lamellipod...</td>\n",
       "      <td>[CMPO_0000272, CMPO_0000288, CMPO_0000274, CMP...</td>\n",
       "      <td>[206, 206, 206, 206, 206, 206, 206, 206, 206, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAPZA1</th>\n",
       "      <td>[829]</td>\n",
       "      <td>[ENSG00000116489]</td>\n",
       "      <td>GeneName</td>\n",
       "      <td>CAPZA1</td>\n",
       "      <td>[geometric cell phenotype, increased actin loc...</td>\n",
       "      <td>[CMPO_0000272, CMPO_0000288, CMPO_0000289, CMP...</td>\n",
       "      <td>[206, 206, 206, 206, 206, 206, 206, 206, 206, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAPG</th>\n",
       "      <td>[822]</td>\n",
       "      <td>[ENSG00000042493]</td>\n",
       "      <td>GeneName</td>\n",
       "      <td>CAPG</td>\n",
       "      <td>[cell death phenotype, mitosis delayed phenoty...</td>\n",
       "      <td>[CMPO_0000305, CMPO_0000348, CMPO_0000307, CMP...</td>\n",
       "      <td>[1101, 1101, 1101, 1101, 1101, 1101, 1101, 1302]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session = create_http_session()\n",
    "organism = org_sel.value\n",
    "\n",
    "[query_genes_dataframe,\n",
    " screen_to_phenotype_dictionary] = get_phenotypes_for_genelist(session,\n",
    "                                                               go_gene_list,\n",
    "                                                               organism)\n",
    "display(HTML(query_genes_dataframe.to_html(escape=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Other Genes from the phenotypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to IDR...\n",
      "[============================================================] 100.0% ...Iterating through screens\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>Query Genes:</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round cell phenotype</th>\n",
       "      <th>cell death phenotype</th>\n",
       "      <th>increased variability of nuclear shape in population</th>\n",
       "      <th>mitosis delayed phenotype</th>\n",
       "      <th>metaphase delayed phenotype</th>\n",
       "      <th>mitosis arrested</th>\n",
       "      <th>mitotic metaphase plate congression phenotype</th>\n",
       "      <th>abnormal cell cycle phenotype</th>\n",
       "      <th>metaphase arrested phenotype</th>\n",
       "      <th>polylobed nuclear phenotype</th>\n",
       "      <th>fan-shaped lamellipodia phenotype</th>\n",
       "      <th>increased variability of cell shape in population</th>\n",
       "      <th>increased lamellipodia width phenotype</th>\n",
       "      <th>misshapen DNA</th>\n",
       "      <th>geometric cell phenotype</th>\n",
       "      <th>increased actin localised to the cytoplasm</th>\n",
       "      <th>decreased nucleus size phenotype</th>\n",
       "      <th>increased microtubule-based processes phenotype</th>\n",
       "      <th>increased cell size phenotype</th>\n",
       "      <th>decreased cell numbers</th>\n",
       "      <th>elongated cell phenotype</th>\n",
       "      <th>increased amount of punctate actin foci phenotype</th>\n",
       "      <th>disorganised cortical actin cytoskeleton phenotype</th>\n",
       "      <th>increased cortical actin cytoskeleton mass phenotype</th>\n",
       "      <th>microtubules nuclear ring phenotype</th>\n",
       "      <th>increased amount of stress fibers phenotype</th>\n",
       "      <th>increased amount of zig-zag stress fibers</th>\n",
       "      <th>microtubules nuclear bracket phenotype</th>\n",
       "      <th>increased number of filopodia</th>\n",
       "      <th>more multinucleate cells</th>\n",
       "      <th>increased amount of transverse stress fibers</th>\n",
       "      <th>increased amount of stress fibers located in the cell cortex phenotype</th>\n",
       "      <th>star shaped cell phenotype</th>\n",
       "      <th>increased number of actin filament phenotype</th>\n",
       "      <th>loss of cell monolayer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>[CAPZB]</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>[CAPZB]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[CAPG]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[CAPZA3, CAPZB]</td>\n",
       "      <td>[CAPZA3]</td>\n",
       "      <td>[CAPZA3, CAPZB]</td>\n",
       "      <td>[CAPZA3]</td>\n",
       "      <td>[CAPZA1]</td>\n",
       "      <td>[CAPZB, CAPZA1]</td>\n",
       "      <td>[CAPZA1]</td>\n",
       "      <td>[CAPZA3, CAPZB]</td>\n",
       "      <td>[CAPZB, CAPZA1]</td>\n",
       "      <td>[CAPZB]</td>\n",
       "      <td>[CAPZA3]</td>\n",
       "      <td>[CAPZA3, CAPZB]</td>\n",
       "      <td>[CAPZA3, CAPZB]</td>\n",
       "      <td>[CAPZB, CAPZA1]</td>\n",
       "      <td>[CAPZB, CAPZA1]</td>\n",
       "      <td>[CAPZA3, CAPZA1]</td>\n",
       "      <td>[CAPZA3]</td>\n",
       "      <td>[CAPZA1]</td>\n",
       "      <td>[CAPZB]</td>\n",
       "      <td>[CAPZB]</td>\n",
       "      <td>[CAPZA3, CAPZA1]</td>\n",
       "      <td>[CAPZA3, CAPZA1]</td>\n",
       "      <td>[CAPZA3]</td>\n",
       "      <td>[CAPZB, CAPZA1]</td>\n",
       "      <td>[CAPZA3, CAPZB]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>Similar Genes:</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round cell phenotype</th>\n",
       "      <th>cell death phenotype</th>\n",
       "      <th>increased variability of nuclear shape in population</th>\n",
       "      <th>mitosis delayed phenotype</th>\n",
       "      <th>metaphase delayed phenotype</th>\n",
       "      <th>mitosis arrested</th>\n",
       "      <th>mitotic metaphase plate congression phenotype</th>\n",
       "      <th>abnormal cell cycle phenotype</th>\n",
       "      <th>metaphase arrested phenotype</th>\n",
       "      <th>polylobed nuclear phenotype</th>\n",
       "      <th>fan-shaped lamellipodia phenotype</th>\n",
       "      <th>increased variability of cell shape in population</th>\n",
       "      <th>increased lamellipodia width phenotype</th>\n",
       "      <th>misshapen DNA</th>\n",
       "      <th>geometric cell phenotype</th>\n",
       "      <th>increased actin localised to the cytoplasm</th>\n",
       "      <th>decreased nucleus size phenotype</th>\n",
       "      <th>increased microtubule-based processes phenotype</th>\n",
       "      <th>increased cell size phenotype</th>\n",
       "      <th>decreased cell numbers</th>\n",
       "      <th>elongated cell phenotype</th>\n",
       "      <th>increased amount of punctate actin foci phenotype</th>\n",
       "      <th>disorganised cortical actin cytoskeleton phenotype</th>\n",
       "      <th>increased cortical actin cytoskeleton mass phenotype</th>\n",
       "      <th>microtubules nuclear ring phenotype</th>\n",
       "      <th>increased amount of stress fibers phenotype</th>\n",
       "      <th>increased amount of zig-zag stress fibers</th>\n",
       "      <th>microtubules nuclear bracket phenotype</th>\n",
       "      <th>increased number of filopodia</th>\n",
       "      <th>more multinucleate cells</th>\n",
       "      <th>increased amount of transverse stress fibers</th>\n",
       "      <th>increased amount of stress fibers located in the cell cortex phenotype</th>\n",
       "      <th>star shaped cell phenotype</th>\n",
       "      <th>increased number of actin filament phenotype</th>\n",
       "      <th>loss of cell monolayer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[ENSG00000124209, ENSG00000134955, ZDHHC17, OR...</td>\n",
       "      <td>[, C1orf126, ENSG00000131183, ICK, PPAP2B, ENS...</td>\n",
       "      <td>[ENSG00000124209, , ASCC3L1, ENSG00000164542, ...</td>\n",
       "      <td>[ENSG00000124209, , ASCC3L1, MFAP1, ENSG000001...</td>\n",
       "      <td>[ENSG00000124209, , ASCC3L1, ENSG00000164542, ...</td>\n",
       "      <td>[ENSG00000124209, , ASCC3L1, ENSG00000148136, ...</td>\n",
       "      <td>[ENSG00000124209, , ASCC3L1, ENSG00000148136, ...</td>\n",
       "      <td>[ENSG00000124209, , ASCC3L1, MFAP1, ENSG000001...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>[, SYTL4, APBB1IP, IFLTD1, BTN3A1, CDX1, FLJ16...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[, UBL5, ENSG00000167513, CRISP2, ASCC3L1, NUF...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[C6ORF168, BAF53A, PTK2, CYFIP1, LOC201176, RR...</td>\n",
       "      <td>[C6ORF168, MAP4K3, PTK2, DMD, MYH15, RASD1, TM...</td>\n",
       "      <td>[BAF53A, ACTG1, MAP3K9, FMNL1, RAMP, ARHGAP27,...</td>\n",
       "      <td>[ACTG1, DTL, LOC201176, RAD51L3, FMN2, LASP1, ...</td>\n",
       "      <td>[C6ORF168, MAP4K3, TPM4, CYFIP1, ACTG1, RASD1,...</td>\n",
       "      <td>[RASD1, DTL, RHOT2, SPTBN1, TNNT3, LHX3, ECT2,...</td>\n",
       "      <td>[MINK1, MAP4K3, MINK, SVIL, MYH15, MYH4, WASF3...</td>\n",
       "      <td>[C6ORF168, BAF53A, PTK2, CYFIP1, LOC201176, MY...</td>\n",
       "      <td>[BAF53A, CYFIP1, ACTG1, RASD1, DTL, TMSB4X, MY...</td>\n",
       "      <td>[BAF53A, ACTG1, LASP1, MAP3K9, FMNL1, RAMP, AR...</td>\n",
       "      <td>[C6ORF168, BAF53A, PTK2, CYFIP1, MYH15, RASD1,...</td>\n",
       "      <td>[C6ORF168, BAF53A, PTK2, CYFIP1, MYH15, LOC201...</td>\n",
       "      <td>[BAF53A, MAP3K9, FMNL1, RAMP, ARHGAP27, ARHGAP...</td>\n",
       "      <td>[MAP4K3, TPM4, DMD, ACTG1, RASD1, LOC201176, M...</td>\n",
       "      <td>[MAP4K3, DMD, ACTG1, TMSB4X, MYO1F, SPTBN4, NC...</td>\n",
       "      <td>[C6ORF168, MAP4K3, CYFIP1, ACTG1, LOC201176, M...</td>\n",
       "      <td>[TPM4, CYFIP1, ACTG1, RASD1, DTL, LOC201176, M...</td>\n",
       "      <td>[EPB41L1, MINK1, MAP4K3, MINK, DBNL, MYH15, WD...</td>\n",
       "      <td>[BAF53A, CYFIP1, MYH15, RASD1, DTL, LOC201176,...</td>\n",
       "      <td>[BAF53A, PTK2, DMD, ACTG1, RASD1, DTL, LOC2011...</td>\n",
       "      <td>[MAP4K3, PTK2, CYFIP1, ACTG1, RASD1, MYO1F, RA...</td>\n",
       "      <td>[C6ORF168, MAP4K3, PTK2, CYFIP1, ACTG1, RASD1,...</td>\n",
       "      <td>[C6ORF168, BAF53A, PTK2, CYFIP1, LOC201176, RR...</td>\n",
       "      <td>[ACTG1, WDR1, DTL, RAMP, PTPN14, MYOHD1, CFL1,...</td>\n",
       "      <td>[C6ORF168, BAF53A, PTK2, DMD, MYH15, DTL, LOC2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conn = connection('idr.openmicroscopy.org')\n",
    "try:\n",
    "    query_genes_list = list(query_genes_dataframe['Value'])\n",
    "    [similar_genes,\n",
    "     overlap_genes] = get_similar_genes(conn, query_genes_list,\n",
    "                                        screen_to_phenotype_dictionary)\n",
    "    overlap_genes_dataframe = pandas.DataFrame.from_dict(overlap_genes,\n",
    "                                                         orient='index')\n",
    "    display(HTML(\"<strong>Query Genes:</strong>\"))\n",
    "    display(HTML(overlap_genes_dataframe.to_html(escape=False)))\n",
    "\n",
    "    similar_genes_dataframe = pandas.DataFrame.from_dict(similar_genes,\n",
    "                                                         orient='index')\n",
    "    display(HTML(\"<strong>Similar Genes:</strong>\"))\n",
    "    display(HTML(similar_genes_dataframe.to_html(escape=False)))\n",
    "finally:\n",
    "    conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Query Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec724893797d4378b9160149528dfbe4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description=u'Threshold_for_plot', max=10, min=1), IntSlider(value=1, description=u'Threshold_for_category', max=10, min=1), Dropdown(description=u'Filter_by_category', options=('Phenotypes', 'Screens'), value='Phenotypes'), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "filter_by_category = widgets.Dropdown(description='Filter',\n",
    "                                      options=['Phenotypes', 'Screens'])\n",
    "threshold_for_category = widgets.IntSlider(description='Threshold',\n",
    "                                           min=1, max=10, step=1, value=1)\n",
    "threshold_for_plot = widgets.IntSlider(description='Threshold_for_plot',\n",
    "                                       min=1, max=10, step=1, value=1)\n",
    "\n",
    "\n",
    "@interact(primary_dictionary=fixed(overlap_genes),\n",
    "          secondary_dictionary=fixed(overlap_genes),\n",
    "          plot_title=fixed('Query Genes'),\n",
    "          filter_by_category=filter_by_category,\n",
    "          threshold_for_category=threshold_for_category,\n",
    "          threshold_for_plot=threshold_for_plot)\n",
    "@wraps(plot_idr_attributes)\n",
    "def myfun(**kwargs):\n",
    "    global screenids_removed, phenotypes_removed, genes_of_interest\n",
    "    [screenids_removed, phenotypes_removed,\n",
    "     genes_of_interest] = plot_idr_attributes(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Similar Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "029072c2cde54ba58cd8633c89d46a1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=5, description=u'threshold_for_plot', max=10, min=1), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ids in screenids_removed:\n",
    "    if ids in similar_genes:\n",
    "        del similar_genes[ids]\n",
    "\n",
    "\n",
    "@interact(primary_dictionary=fixed(similar_genes),\n",
    "          secondary_dictionary=fixed(overlap_genes),\n",
    "          plot_title=fixed('Similar genes'),\n",
    "          filter_by_category=fixed(filter_by_category.value),\n",
    "          threshold_for_category=fixed(threshold_for_category.value),\n",
    "          threshold_for_plot=widgets.IntSlider(min=1, max=10, step=1, value=5))\n",
    "@wraps(plot_idr_attributes)\n",
    "def myfun2(**kwargs):\n",
    "    global screenids_removed, phenotypes_removed, genes_of_interest\n",
    "    [screenids_removed, phenotypes_removed,\n",
    "     genes_of_interest] = plot_idr_attributes(**kwargs)\n",
    "\n",
    "\n",
    "similar_genes_list = genes_of_interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get String Interactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot interactions between similar genes and query genes/similar genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primary Interactors:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f7a152dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Secondary Interactors:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f7a9e0510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "similar_genes_list = list(set(genes_of_interest) - set(go_gene_list))\n",
    "genes_of_interest1 = list(set(go_gene_list + similar_genes_list))\n",
    "interactions_dataframe = genes_of_interest_from_string(genes_of_interest1,\n",
    "                                                       1, tax_id.value)\n",
    "\n",
    "print('Primary Interactors:')\n",
    "df = plot_string_interactions(go_gene_list, similar_genes_list,\n",
    "                              interactions_dataframe)\n",
    "\n",
    "primary_genes = list(df.columns.values)\n",
    "secondary_genes = set(similar_genes_list) - set(primary_genes)\n",
    "print('Secondary Interactors:')\n",
    "df = plot_string_interactions(secondary_genes, primary_genes,\n",
    "                              interactions_dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### License (BSD 2-Clause)¶\n",
    "\n",
    "Copyright (C) 2016-2021 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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}