{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# tmp-API-check\n",
    "The `clustergrammer_widget` class is now being loaded into the `Network` class. The class and widget instance are saved in th `Network` instance, `net`. This allows us to load data, cluster, and finally produce a new widget instance using the `widget` method. The instance of the widget is saved in `net` and can be used to grab the data from the clustergram as a Pandas DataFrame using the `widget_df` method. The exported DataFrame will reflect any filtering or imported categories that were added on the front end.\n",
    "\n",
    "In these examples, we will filter the matrix using the brush crop tool, export the filtered matrix as a DataFrame, and finally visualize this as a new clustergram widget. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from clustergrammer_widget import *\n",
    "net = Network(clustergrammer_widget)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Make widget using new API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4eccc9d7bb86416a8ad9274ec602d7da"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.load_file('rc_two_cats.txt')\n",
    "net.cluster()\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Above, we have filtered the matrix to a region of interest using the brush cropping tool. Below we will get export this region of interest, defined on the front end, to a DataFrame, `df_genes`. This demonstrates the two-way communication capabilities of widgets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genes = net.widget_df()\n",
    "df_genes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ddf969cbf234b1a9298ef37f7ef92b5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.load_df(df_genes)\n",
    "net.cluster()\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Above, we made a new widget visualizing this region of interest. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Generate random DataFrame\n",
    "Here we will genrate a DataFrame with random data and visualize it using the widget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# generate random matrix\n",
    "num_rows = 500\n",
    "num_cols = 10\n",
    "np.random.seed(seed=100)\n",
    "mat = np.random.rand(num_rows, num_cols)\n",
    "\n",
    "# make row and col labels\n",
    "rows = range(num_rows)\n",
    "cols = range(num_cols)\n",
    "rows = [str(i) for i in rows]\n",
    "cols = [str(i) for i in cols]\n",
    "\n",
    "# make dataframe \n",
    "df = pd.DataFrame(data=mat, columns=cols, index=rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c3de50ebed64ddd861646e8985f4bd8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.load_df(df)\n",
    "net.cluster()\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Above, we selected a region of interest using the front-end brush crop tool and export to DataFrame, df_random. Below we will visualize it using a new widget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random = net.widget_df()\n",
    "df_random.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49d62383af884f1393f2495c32597b74"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.load_df(df_random)\n",
    "net.cluster()\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
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   "codemirror_mode": {
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    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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      "model_name": "hello_model",
      "state": {
       "_dom_classes": [],
       "_model_module_version": "*",
       "_view_module_version": "*",
       "layout": "IPY_MODEL_d63b50f58453413e8f9269b583c5c51b",
       "mat_string": "\t('H1792', 'Category: three', 'Gender: Male')\t('H460', 'Category: three', 'Gender: Male')\t('H1666', 'Category: four', 'Gender: Female')\t('A549', 'Category: four', 'Gender: Male')\t('H1944', 'Category: three', 'Gender: Female')\t('H1650', 'Category: two', 'Gender: Male')\t('H1437', 'Category: four', 'Gender: Male')\n('PDK4', 'Gene Type: Not Interesting')\t1.09320354\t0.386976419\t0.087671916\t-0.122764837\t1.680895489\t-0.6432463310000001\t0.508498891\n('LRRK2', 'Gene Type: Not Interesting')\t-0.382639145\t-0.377853523\t-0.440334448\t1.6773765269999998\t4.308904581\t-0.6978761509999999\t-0.638131949\n('UHMK1', 'Gene Type: Not Interesting')\t0.424470033\t0.283221899\t0.8471713670000001\t-0.255283556\t1.747343933\t0.8505465179999999\t-1.020927175\n('SRC', 'Gene Type: Interesting')\t2.961024714\t2.299033235\t1.47598938\t3.702617515\t1.161954005\t-0.294263824\t-0.15700654\n('ULK4', 'Gene Type: Interesting')\t-0.364768511\t-0.5472691\t-1.02427507\t0.35514519\t-0.105370633\t-0.693868027\t-0.13943154900000002\n('MYLK3', 'Gene Type: Not Interesting')\t-0.623071147\t-0.406230833\t-0.35486840000000003\t0.08160995900000001\t0.117950503\t-0.3681732170000001\t0.1975473\n('INSRR', 'Gene Type: Not Interesting')\t-0.7449007440000001\t-0.43363819\t-1.025689676\t0.387715581\t0.765012989\t0.331108191\t0.400356525\n('ERBB4', 'Gene Type: Not Interesting')\t-0.054684881\t-0.484730631\t-0.331935876\t-0.42868169\t0.143245619\t-0.452907052\t-0.266932489\n",
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       "network": "{\"mat\": [[-0.452907052, -0.484730631, -0.054684881, -0.42868169, 0.143245619, -0.331935876, -0.266932489], [-0.3681732170000001, -0.406230833, -0.623071147, 0.08160995900000001, 0.117950503, -0.35486840000000003, 0.1975473], [-0.693868027, -0.5472691, -0.364768511, 0.35514519, -0.105370633, -1.02427507, -0.13943154900000002], [-0.294263824, 2.299033235, 2.961024714, 3.702617515, 1.161954005, 1.47598938, -0.15700654], [-0.6978761509999999, -0.377853523, -0.382639145, 1.6773765269999998, 4.308904581, -0.440334448, -0.638131949], [0.8505465179999999, 0.283221899, 0.424470033, -0.255283556, 1.747343933, 0.8471713670000001, -1.020927175], [-0.6432463310000001, 0.386976419, 1.09320354, -0.122764837, 1.680895489, 0.087671916, 0.508498891], [0.331108191, -0.43363819, -0.7449007440000001, 0.387715581, 0.765012989, -1.025689676, 0.400356525]], \"links\": [], \"views\": [{\"N_row_sum\": \"all\", \"dist\": \"cos\", \"nodes\": {\"row_nodes\": [{\"group\": [4.0, 4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"ERBB4\", \"clust\": 0, \"cat_0_index\": 2, \"rank\": 1, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 0, \"ini\": 8}, {\"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"MYLK3\", \"clust\": 2, \"cat_0_index\": 3, \"rank\": 2, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 1, \"ini\": 7}, {\"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"ULK4\", \"clust\": 3, \"cat_0_index\": 0, \"rank\": 0, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 2, \"ini\": 6}, {\"group\": [8.0, 8.0, 7.0, 5.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"SRC\", \"clust\": 4, \"cat_0_index\": 1, \"rank\": 7, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 6, \"ini\": 5}, {\"group\": [5.0, 5.0, 4.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"LRRK2\", \"clust\": 6, \"cat_0_index\": 4, \"rank\": 6, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 7, \"ini\": 4}, {\"group\": [7.0, 7.0, 6.0, 4.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"UHMK1\", \"clust\": 5, \"cat_0_index\": 5, \"rank\": 4, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 5, \"ini\": 3}, {\"group\": [6.0, 6.0, 5.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"PDK4\", \"clust\": 7, \"cat_0_index\": 6, \"rank\": 5, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 4, \"ini\": 2}, {\"group\": [3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"INSRR\", \"clust\": 1, \"cat_0_index\": 7, \"rank\": 3, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 3, \"ini\": 1}], \"col_nodes\": [{\"group\": [6.0, 5.0, 4.0, 3.0, 3.0, 3.0, 2.0, 2.0, 1.0, 1.0, 1.0], \"name\": \"H1650\", \"clust\": 1, \"cat_1_index\": 2, \"cat_0_index\": 6, \"rank\": 0, \"cat-1\": \"Gender: Male\", \"ini\": 7, \"cat-0\": \"Category: two\", \"rankvar\": 1}, {\"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"H460\", \"clust\": 5, \"cat_1_index\": 3, \"cat_0_index\": 3, \"rank\": 3, \"cat-1\": \"Gender: Male\", \"ini\": 6, \"cat-0\": \"Category: three\", \"rankvar\": 3}, {\"group\": [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"H1792\", \"clust\": 6, \"cat_1_index\": 4, \"cat_0_index\": 4, \"rank\": 4, \"cat-1\": \"Gender: Male\", \"ini\": 5, \"cat-0\": \"Category: three\", \"rankvar\": 4}, {\"group\": [4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"A549\", \"clust\": 3, \"cat_1_index\": 5, \"cat_0_index\": 0, \"rank\": 5, \"cat-1\": \"Gender: Male\", \"ini\": 4, \"cat-0\": \"Category: four\", \"rankvar\": 5}, {\"group\": [5.0, 4.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"H1944\", \"clust\": 2, \"cat_1_index\": 0, \"cat_0_index\": 5, \"rank\": 6, \"cat-1\": \"Gender: Female\", \"ini\": 3, \"cat-0\": \"Category: three\", \"rankvar\": 6}, {\"group\": [3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"H1666\", \"clust\": 4, \"cat_1_index\": 1, \"cat_0_index\": 1, \"rank\": 2, \"cat-1\": \"Gender: Female\", \"ini\": 2, \"cat-0\": \"Category: four\", \"rankvar\": 2}, {\"group\": [7.0, 6.0, 5.0, 4.0, 4.0, 4.0, 3.0, 3.0, 2.0, 1.0, 1.0], \"name\": \"H1437\", \"clust\": 0, \"cat_1_index\": 6, \"cat_0_index\": 2, \"rank\": 1, \"cat-1\": \"Gender: Male\", \"ini\": 1, \"cat-0\": \"Category: four\", \"rankvar\": 0}]}}, {\"N_row_var\": \"all\", \"nodes\": {\"row_nodes\": [{\"group\": [4.0, 4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"ERBB4\", \"clust\": 0, \"cat_0_index\": 2, \"rank\": 1, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 0, \"ini\": 8}, {\"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"MYLK3\", \"clust\": 2, \"cat_0_index\": 3, \"rank\": 2, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 1, \"ini\": 7}, {\"group\": [2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"ULK4\", \"clust\": 3, \"cat_0_index\": 0, \"rank\": 0, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 2, \"ini\": 6}, {\"group\": [8.0, 8.0, 7.0, 5.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"SRC\", \"clust\": 4, \"cat_0_index\": 1, \"rank\": 7, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 6, \"ini\": 5}, {\"group\": [5.0, 5.0, 4.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"LRRK2\", \"clust\": 6, \"cat_0_index\": 4, \"rank\": 6, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 7, \"ini\": 4}, {\"group\": [7.0, 7.0, 6.0, 4.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"UHMK1\", \"clust\": 5, \"cat_0_index\": 5, \"rank\": 4, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 5, \"ini\": 3}, {\"group\": [6.0, 6.0, 5.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"PDK4\", \"clust\": 7, \"cat_0_index\": 6, \"rank\": 5, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 4, \"ini\": 2}, {\"group\": [3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"INSRR\", \"clust\": 1, \"cat_0_index\": 7, \"rank\": 3, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 3, \"ini\": 1}], \"col_nodes\": [{\"group\": [6.0, 5.0, 4.0, 3.0, 3.0, 3.0, 2.0, 2.0, 1.0, 1.0, 1.0], \"name\": \"H1650\", \"clust\": 1, \"cat_1_index\": 2, \"cat_0_index\": 6, \"rank\": 0, \"cat-1\": \"Gender: Male\", \"ini\": 7, \"cat-0\": \"Category: two\", \"rankvar\": 1}, {\"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"H460\", \"clust\": 5, \"cat_1_index\": 3, \"cat_0_index\": 3, \"rank\": 3, \"cat-1\": \"Gender: Male\", \"ini\": 6, \"cat-0\": \"Category: three\", \"rankvar\": 3}, {\"group\": [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"H1792\", \"clust\": 6, \"cat_1_index\": 4, \"cat_0_index\": 4, \"rank\": 4, \"cat-1\": \"Gender: Male\", \"ini\": 5, \"cat-0\": \"Category: three\", \"rankvar\": 4}, {\"group\": [4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"A549\", \"clust\": 3, \"cat_1_index\": 5, \"cat_0_index\": 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       "viz_title": "updating python by restarting kernel"
      }
     },
     "49d62383af884f1393f2495c32597b74": {
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      "model_module_version": "*",
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       "viz_title": "updating python by restarting kernel"
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       "viz_title": "updating python by restarting kernel"
      }
     },
     "4eccc9d7bb86416a8ad9274ec602d7da": {
      "model_module": "clustergrammer_widget",
      "model_module_version": "*",
      "model_name": "hello_model",
      "state": {
       "_dom_classes": [],
       "_model_module_version": "*",
       "_view_module_version": "*",
       "layout": "IPY_MODEL_78840af6df214746b6f596c949e7f189",
       "mat_string": "\t('H1650', 'Category: two', 'Gender: Male')\t('H460', 'Category: three', 'Gender: Male')\t('H1792', 'Category: three', 'Gender: Male')\t('A549', 'Category: four', 'Gender: Male')\t('H1944', 'Category: three', 'Gender: Female')\t('H1666', 'Category: four', 'Gender: Female')\t('H1437', 'Category: four', 'Gender: Male')\n('ERBB4', 'Gene Type: Not Interesting')\t-0.45290705200000003\t-0.484730631\t-0.054684881\t-0.42868169\t0.143245619\t-0.331935876\t-0.266932489\n('MYLK3', 'Gene Type: Not Interesting')\t-0.36817321700000005\t-0.40623083299999996\t-0.623071147\t0.08160995900000001\t0.11795050300000001\t-0.35486840000000003\t0.1975473\n('ULK4', 'Gene Type: Interesting')\t-0.693868027\t-0.5472691\t-0.364768511\t0.35514519\t-0.105370633\t-1.02427507\t-0.13943154900000002\n('SRC', 'Gene Type: Interesting')\t-0.294263824\t2.299033235\t2.9610247139999997\t3.702617515\t1.161954005\t1.47598938\t-0.15700654\n('LRRK2', 'Gene Type: Not Interesting')\t-0.6978761509999999\t-0.37785352299999997\t-0.382639145\t1.6773765269999998\t4.308904581\t-0.440334448\t-0.638131949\n('UHMK1', 'Gene Type: Not Interesting')\t0.8505465179999999\t0.283221899\t0.42447003299999997\t-0.255283556\t1.747343933\t0.8471713670000001\t-1.020927175\n('PDK4', 'Gene Type: Not Interesting')\t-0.6432463310000001\t0.38697641899999996\t1.09320354\t-0.12276483699999999\t1.680895489\t0.087671916\t0.508498891\n('INSRR', 'Gene Type: Not Interesting')\t0.331108191\t-0.43363819\t-0.7449007440000001\t0.38771558100000003\t0.765012989\t-1.025689676\t0.400356525\n",
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\"cat_0_index\": 5, \"rank\": 20, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 30, \"ini\": 23}, {\"group\": [34.0, 33.0, 30.0, 22.0, 17.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: KDR\", \"clust\": 33, \"cat_0_index\": 27, \"rank\": 19, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 11, \"ini\": 22}, {\"group\": [20.0, 19.0, 18.0, 12.0, 10.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: NPR1\", \"clust\": 16, \"cat_0_index\": 6, \"rank\": 27, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 29, \"ini\": 21}, {\"group\": [16.0, 15.0, 14.0, 9.0, 8.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: PAK3\", \"clust\": 9, \"cat_0_index\": 28, \"rank\": 11, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 12, \"ini\": 20}, {\"group\": [7.0, 6.0, 5.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: PDGFRA\", \"clust\": 7, \"cat_0_index\": 7, \"rank\": 2, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 0, \"ini\": 19}, {\"group\": [24.0, 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1.0, 1.0], \"name\": \"Cell Line: H661\", \"clust\": 15, \"cat_1_index\": 18, \"cat_0_index\": 2, \"rank\": 18, \"cat-1\": \"Gender: Male\", \"ini\": 18, \"cat-0\": \"Category: five\", \"rankvar\": 26}, {\"group\": [16.0, 14.0, 10.0, 7.0, 4.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: H838\", \"clust\": 16, \"cat_1_index\": 19, \"cat_0_index\": 3, \"rank\": 19, \"cat-1\": \"Gender: Male\", \"ini\": 17, \"cat-0\": \"Category: five\", \"rankvar\": 23}, {\"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: H1703\", \"clust\": 2, \"cat_1_index\": 20, \"cat_0_index\": 4, \"rank\": 21, \"cat-1\": \"Gender: Male\", \"ini\": 16, \"cat-0\": \"Category: five\", \"rankvar\": 21}, {\"group\": [25.0, 21.0, 15.0, 10.0, 6.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: CALU-3\", \"clust\": 24, \"cat_1_index\": 21, \"cat_0_index\": 7, \"rank\": 5, \"cat-1\": \"Gender: Male\", \"ini\": 15, \"cat-0\": \"Category: four\", \"rankvar\": 7}, {\"group\": [17.0, 15.0, 10.0, 7.0, 4.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: H2342\", \"clust\": 17, \"cat_1_index\": 3, \"cat_0_index\": 8, \"rank\": 27, \"cat-1\": \"Gender: Female\", \"ini\": 14, \"cat-0\": \"Category: four\", \"rankvar\": 25}, {\"group\": [19.0, 17.0, 12.0, 9.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: H2228\", \"clust\": 19, \"cat_1_index\": 4, \"cat_0_index\": 15, \"rank\": 16, \"cat-1\": \"Gender: Female\", \"ini\": 13, \"cat-0\": \"Category: one\", \"rankvar\": 16}, {\"group\": [3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: H1299\", \"clust\": 1, \"cat_1_index\": 22, \"cat_0_index\": 20, \"rank\": 10, \"cat-1\": \"Gender: Male\", \"ini\": 12, \"cat-0\": \"Category: three\", \"rankvar\": 11}, {\"group\": [29.0, 25.0, 18.0, 11.0, 6.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: H1792\", \"clust\": 26, \"cat_1_index\": 23, \"cat_0_index\": 21, \"rank\": 2, \"cat-1\": \"Gender: Male\", 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\"Cell Line: HCC44\", \"clust\": 4, \"cat_1_index\": 8, \"cat_0_index\": 11, \"rank\": 15, \"cat-1\": \"Gender: Female\", \"ini\": 3, \"cat-0\": \"Category: four\", \"rankvar\": 0}, {\"group\": [6.0, 5.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: A549\", \"clust\": 6, \"cat_1_index\": 28, \"cat_0_index\": 12, \"rank\": 12, \"cat-1\": \"Gender: Male\", \"ini\": 2, \"cat-0\": \"Category: four\", \"rankvar\": 6}, {\"group\": [20.0, 17.0, 12.0, 9.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Cell Line: H1781\", \"clust\": 20, \"cat_1_index\": 9, \"cat_0_index\": 18, \"rank\": 24, \"cat-1\": \"Gender: Female\", \"ini\": 1, \"cat-0\": \"Category: one\", \"rankvar\": 18}]}, \"dist\": \"cos\"}], \"cat_colors\": {\"col\": {\"cat-0\": {\"Category: five\": \"#98df8a\", \"Category: four\": \"#404040\", \"Category: one\": \"#c5b0d5\", \"Category: two\": \"#FFDB58\", \"Category: three\": \"#1f77b4\"}, \"cat-1\": {\"Gender: Male\": \"#98df8a\", \"Gender: Female\": \"#ff7f0e\"}}, \"row\": {\"cat-0\": {\"Gene Type: Interesting\": \"#393b79\", \"Gene Type: Not Interesting\": \"#eee\"}}}, \"row_nodes\": [{\"group\": [10.0, 9.0, 8.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: CDK4\", \"clust\": 14, \"cat_0_index\": 0, \"rank\": 30, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 20, \"ini\": 38}, {\"group\": [21.0, 20.0, 19.0, 13.0, 11.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: LMTK3\", \"clust\": 20, \"cat_0_index\": 17, \"rank\": 28, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 24, \"ini\": 37}, {\"group\": [26.0, 25.0, 22.0, 16.0, 13.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: LRRK2\", \"clust\": 26, \"cat_0_index\": 18, \"rank\": 9, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 10, \"ini\": 36}, {\"group\": [28.0, 27.0, 24.0, 18.0, 14.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: UHMK1\", \"clust\": 25, \"cat_0_index\": 19, \"rank\": 26, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 14, \"ini\": 35}, {\"group\": [12.0, 11.0, 10.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: EGFR\", \"clust\": 13, \"cat_0_index\": 1, \"rank\": 33, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 7, \"ini\": 34}, {\"group\": [33.0, 32.0, 29.0, 22.0, 17.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: STK32A\", \"clust\": 32, \"cat_0_index\": 2, \"rank\": 36, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 32, \"ini\": 33}, {\"group\": [11.0, 10.0, 9.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: NRK\", \"clust\": 15, \"cat_0_index\": 3, \"rank\": 35, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 33, \"ini\": 32}, {\"group\": [35.0, 34.0, 31.0, 23.0, 18.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: ERBB2\", \"clust\": 34, \"cat_0_index\": 20, \"rank\": 24, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 9, \"ini\": 31}, {\"group\": [31.0, 30.0, 27.0, 20.0, 16.0, 7.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: ERBB4\", \"clust\": 30, \"cat_0_index\": 21, \"rank\": 6, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 2, \"ini\": 30}, {\"group\": [37.0, 36.0, 33.0, 25.0, 19.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: AAK1\", \"clust\": 36, \"cat_0_index\": 22, \"rank\": 18, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 8, \"ini\": 29}, {\"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: SRPK3\", \"clust\": 4, \"cat_0_index\": 23, \"rank\": 8, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 15, \"ini\": 28}, {\"group\": [36.0, 35.0, 32.0, 24.0, 18.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: STK39\", \"clust\": 35, \"cat_0_index\": 4, \"rank\": 7, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 4, \"ini\": 27}, {\"group\": [3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: GRK4\", \"clust\": 3, \"cat_0_index\": 24, \"rank\": 1, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 3, \"ini\": 26}, {\"group\": [13.0, 12.0, 11.0, 6.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: TBK1\", \"clust\": 12, \"cat_0_index\": 25, \"rank\": 34, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 26, \"ini\": 25}, {\"group\": [23.0, 22.0, 20.0, 14.0, 12.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: INSRR\", \"clust\": 23, \"cat_0_index\": 26, \"rank\": 13, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 5, \"ini\": 24}, {\"group\": [14.0, 13.0, 12.0, 7.0, 6.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: IRAK1\", \"clust\": 11, \"cat_0_index\": 5, \"rank\": 20, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 30, \"ini\": 23}, {\"group\": [34.0, 33.0, 30.0, 22.0, 17.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: KDR\", \"clust\": 33, \"cat_0_index\": 27, \"rank\": 19, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 11, \"ini\": 22}, {\"group\": [20.0, 19.0, 18.0, 12.0, 10.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: NPR1\", \"clust\": 16, \"cat_0_index\": 6, \"rank\": 27, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 29, \"ini\": 21}, {\"group\": [16.0, 15.0, 14.0, 9.0, 8.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: PAK3\", \"clust\": 9, \"cat_0_index\": 28, \"rank\": 11, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 12, \"ini\": 20}, {\"group\": [7.0, 6.0, 5.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: PDGFRA\", \"clust\": 7, \"cat_0_index\": 7, \"rank\": 2, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 0, \"ini\": 19}, {\"group\": [24.0, 23.0, 20.0, 14.0, 12.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: PDK4\", \"clust\": 24, \"cat_0_index\": 29, \"rank\": 22, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 22, \"ini\": 18}, {\"group\": [29.0, 28.0, 25.0, 19.0, 15.0, 7.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: ULK4\", \"clust\": 28, \"cat_0_index\": 8, \"rank\": 16, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 19, \"ini\": 17}, {\"group\": [22.0, 21.0, 19.0, 13.0, 11.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: PRKCE\", \"clust\": 21, \"cat_0_index\": 30, \"rank\": 14, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 1, \"ini\": 16}, {\"group\": [4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: PRKG2\", \"clust\": 2, \"cat_0_index\": 31, \"rank\": 25, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 18, \"ini\": 15}, {\"group\": [17.0, 16.0, 15.0, 10.0, 9.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: MAPK4\", \"clust\": 18, \"cat_0_index\": 9, \"rank\": 10, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 16, \"ini\": 14}, {\"group\": [8.0, 7.0, 6.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: MAPK11\", \"clust\": 8, \"cat_0_index\": 10, \"rank\": 17, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 34, \"ini\": 13}, {\"group\": [32.0, 31.0, 28.0, 21.0, 16.0, 7.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: STK31\", \"clust\": 31, \"cat_0_index\": 11, \"rank\": 37, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 31, \"ini\": 12}, {\"group\": [18.0, 17.0, 16.0, 10.0, 9.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: GRK1\", \"clust\": 19, \"cat_0_index\": 32, \"rank\": 15, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 23, \"ini\": 11}, {\"group\": [38.0, 37.0, 34.0, 26.0, 20.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: ROS1\", \"clust\": 37, \"cat_0_index\": 12, \"rank\": 31, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 37, \"ini\": 10}, {\"group\": [15.0, 14.0, 13.0, 8.0, 7.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: MAP2K4\", \"clust\": 10, \"cat_0_index\": 13, \"rank\": 23, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 28, \"ini\": 9}, {\"group\": [27.0, 26.0, 23.0, 17.0, 13.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: SRC\", \"clust\": 27, \"cat_0_index\": 14, \"rank\": 21, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 21, \"ini\": 8}, {\"group\": [19.0, 18.0, 17.0, 11.0, 9.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: TGFBR1\", \"clust\": 17, \"cat_0_index\": 15, \"rank\": 12, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 13, \"ini\": 7}, {\"group\": [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: CAMK2B\", \"clust\": 5, \"cat_0_index\": 33, \"rank\": 3, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 17, \"ini\": 6}, {\"group\": [9.0, 8.0, 7.0, 4.0, 4.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: STK24\", \"clust\": 6, \"cat_0_index\": 16, \"rank\": 0, \"cat-0\": \"Gene Type: Interesting\", \"rankvar\": 36, \"ini\": 5}, {\"group\": [5.0, 4.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: DCLK3\", \"clust\": 1, \"cat_0_index\": 34, \"rank\": 5, \"cat-0\": \"Gene Type: Not Interesting\", \"rankvar\": 25, \"ini\": 4}, {\"group\": [25.0, 24.0, 21.0, 15.0, 12.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"name\": \"Gene: LATS1\", \"clust\": 22, \"cat_0_index\": 35, 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