example_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ScfindME: Mining Alternative Splicing Patterns in Large Single-cell Atlases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example analysis worlflow\n",
    "**Author:**  \n",
    "Yuyao Song  \n",
    "**Contact:**  \n",
    "<song-yy17@mails.tsinghua.edu.cn>  \n",
    "**Date:**  \n",
    "2020.11.30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Set up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Install package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/home/jovyan/R/x86_64-pc-linux-gnu-library/3.6’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Warning message in install.packages(\"devtools\"):\n",
      "“installation of package ‘devtools’ had non-zero exit status”\n",
      "Downloading GitHub repo hemberg-lab/scfindME@HEAD\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Skipping 3 packages not available: biomaRt, SummarizedExperiment, SingleCellExperiment\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m✔\u001b[39m  \u001b[90mchecking for file ‘/tmp/RtmpMTxLh0/remotes245a429dad97/hemberg-lab-scfindME-5c8c69f/DESCRIPTION’\u001b[39m\u001b[36m\u001b[39m\n",
      "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mpreparing ‘scfindME’:\u001b[39m\u001b[36m\u001b[39m\n",
      "\u001b[32m✔\u001b[39m  \u001b[90mchecking DESCRIPTION meta-information\u001b[39m\u001b[36m\u001b[39m\n",
      "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mcleaning src\u001b[39m\u001b[36m\u001b[39m\n",
      "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mchecking for LF line-endings in source and make files and shell scripts\u001b[39m\u001b[36m\u001b[39m\n",
      "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mchecking for empty or unneeded directories\u001b[39m\u001b[36m\u001b[39m\n",
      "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mlooking to see if a ‘data/datalist’ file should be added\u001b[39m\u001b[36m\u001b[39m\n",
      "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mbuilding ‘scfindME_0.1.0.tar.gz’\u001b[39m\u001b[36m\u001b[39m\n",
      "   \n",
      "\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/home/jovyan/R/x86_64-pc-linux-gnu-library/3.6’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# to install the latest release from GitHub\n",
    "\n",
    "install.packages(\"devtools\")\n",
    "devtools::install_github(\"hemberg-lab/scfindME\", force = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the package scfindME\n",
    "# also load packages for data manipulation and visulisation\n",
    "\n",
    "library(scfindME)\n",
    "library(tibble)\n",
    "library(dplyr)\n",
    "library(tidyr)\n",
    "library(ggplot2)\n",
    "library(repr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a useful function for setting figure size\n",
    "\n",
    "library(repr)\n",
    "fig <- function(width, heigth){\n",
    "     options(repr.plot.width = width, repr.plot.height = heigth)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load example index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72 MB \n",
      "Version 6\n",
      "76 MB \n",
      "Version 6\n"
     ]
    }
   ],
   "source": [
    "# These are the pre-built indices using the Tasic et al.(2016) dataset\n",
    "# The dataset have detailed subtype classifications of glutamatergic neurons and GABA-ergic neurons in the mouse primary visual cortex\n",
    "\n",
    "index.glu <- loadObject(\"~/index.above.glu.z.rds\")\n",
    "index.GABA <- loadObject(\"~/index.above.GABA.z.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'SCFind'"
      ],
      "text/latex": [
       "'SCFind'"
      ],
      "text/markdown": [
       "'SCFind'"
      ],
      "text/plain": [
       "[1] \"SCFind\"\n",
       "attr(,\"package\")\n",
       "[1] \"scfindME\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'index'</li><li>'datasets'</li><li>'serialized'</li><li>'metadata'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'index'\n",
       "\\item 'datasets'\n",
       "\\item 'serialized'\n",
       "\\item 'metadata'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'index'\n",
       "2. 'datasets'\n",
       "3. 'serialized'\n",
       "4. 'metadata'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"index\"      \"datasets\"   \"serialized\" \"metadata\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'node_list'</li><li>'stats'</li><li>'read_count'</li><li>'type'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'node\\_list'\n",
       "\\item 'stats'\n",
       "\\item 'read\\_count'\n",
       "\\item 'type'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'node_list'\n",
       "2. 'stats'\n",
       "3. 'read_count'\n",
       "4. 'type'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"node_list\"  \"stats\"      \"read_count\" \"type\"      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the index is a SCFind class object\n",
    "class(index.glu)\n",
    "\n",
    "# the object contains four attributes\n",
    "slotNames(index.glu)\n",
    "\n",
    "# the metadata attribute further stores splicing-related information for comprehending results\n",
    "names(index.glu@metadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build custom index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# please refer to the scfindME package vignette on how to build input matrix from scratch\n",
    "# here we load the input matrix as an .rds object\n",
    "\n",
    "scaled_matrix <- readRDS(\"~/scfindME_data/Tasic2016/scaled.glu.above.z.rds\")\n",
    "metadata <- readRDS(\"~/scfindME_data/Tasic2016/metadata.glu.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'L2 Ngb'</li><li>'L2/3 Ptgs2'</li><li>'L4 Arf5'</li><li>'L4 Ctxn3'</li><li>'L4 Scnn1a'</li><li>'L5 Chrna6'</li><li>'L5 Ucma'</li><li>'L5a Batf3'</li><li>'L5a Hsd11b1'</li><li>'L5a Pde1c'</li><li>'L5a Tcerg1l'</li><li>'L5b Cdh13'</li><li>'L5b Tph2'</li><li>'L6a Car12'</li><li>'L6a Mgp'</li><li>'L6a Sla'</li><li>'L6a Syt17'</li><li>'L6b Rgs12'</li><li>'L6b Serpinb11'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'L2 Ngb'\n",
       "\\item 'L2/3 Ptgs2'\n",
       "\\item 'L4 Arf5'\n",
       "\\item 'L4 Ctxn3'\n",
       "\\item 'L4 Scnn1a'\n",
       "\\item 'L5 Chrna6'\n",
       "\\item 'L5 Ucma'\n",
       "\\item 'L5a Batf3'\n",
       "\\item 'L5a Hsd11b1'\n",
       "\\item 'L5a Pde1c'\n",
       "\\item 'L5a Tcerg1l'\n",
       "\\item 'L5b Cdh13'\n",
       "\\item 'L5b Tph2'\n",
       "\\item 'L6a Car12'\n",
       "\\item 'L6a Mgp'\n",
       "\\item 'L6a Sla'\n",
       "\\item 'L6a Syt17'\n",
       "\\item 'L6b Rgs12'\n",
       "\\item 'L6b Serpinb11'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'L2 Ngb'\n",
       "2. 'L2/3 Ptgs2'\n",
       "3. 'L4 Arf5'\n",
       "4. 'L4 Ctxn3'\n",
       "5. 'L4 Scnn1a'\n",
       "6. 'L5 Chrna6'\n",
       "7. 'L5 Ucma'\n",
       "8. 'L5a Batf3'\n",
       "9. 'L5a Hsd11b1'\n",
       "10. 'L5a Pde1c'\n",
       "11. 'L5a Tcerg1l'\n",
       "12. 'L5b Cdh13'\n",
       "13. 'L5b Tph2'\n",
       "14. 'L6a Car12'\n",
       "15. 'L6a Mgp'\n",
       "16. 'L6a Sla'\n",
       "17. 'L6a Syt17'\n",
       "18. 'L6b Rgs12'\n",
       "19. 'L6b Serpinb11'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"L2 Ngb\"        \"L2/3 Ptgs2\"    \"L4 Arf5\"       \"L4 Ctxn3\"     \n",
       " [5] \"L4 Scnn1a\"     \"L5 Chrna6\"     \"L5 Ucma\"       \"L5a Batf3\"    \n",
       " [9] \"L5a Hsd11b1\"   \"L5a Pde1c\"     \"L5a Tcerg1l\"   \"L5b Cdh13\"    \n",
       "[13] \"L5b Tph2\"      \"L6a Car12\"     \"L6a Mgp\"       \"L6a Sla\"      \n",
       "[17] \"L6a Syt17\"     \"L6b Rgs12\"     \"L6b Serpinb11\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load metadata and select those belongs to gultamatergic neurons\n",
    "\n",
    "metadata$primary_type <- droplevels(metadata$primary_type)\n",
    "levels(metadata$primary_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating index for glutamatergic_neurons\n",
      "\n",
      "\tIndexing L2 Ngb as glutamatergic_neurons.L2 Ngb  with  21  cells.\n",
      "\n",
      "\tIndexing L2/3 Ptgs2 as glutamatergic_neurons.L2/3 Ptgs2  with  94  cells.\n",
      "\n",
      "\tIndexing L4 Arf5 as glutamatergic_neurons.L4 Arf5  with  44  cells.\n",
      "\n",
      "\tIndexing L4 Ctxn3 as glutamatergic_neurons.L4 Ctxn3  with  79  cells.\n",
      "\n",
      "\tIndexing L4 Scnn1a as glutamatergic_neurons.L4 Scnn1a  with  98  cells.\n",
      "\n",
      "\tIndexing L5 Chrna6 as glutamatergic_neurons.L5 Chrna6  with  8  cells.\n",
      "\n",
      "\tIndexing L5 Ucma as glutamatergic_neurons.L5 Ucma  with  16  cells.\n",
      "\n",
      "\tIndexing L5a Batf3 as glutamatergic_neurons.L5a Batf3  with  64  cells.\n",
      "\n",
      "\tIndexing L5a Hsd11b1 as glutamatergic_neurons.L5a Hsd11b1  with  53  cells.\n",
      "\n",
      "\tIndexing L5a Pde1c as glutamatergic_neurons.L5a Pde1c  with  19  cells.\n",
      "\n",
      "\tIndexing L5a Tcerg1l as glutamatergic_neurons.L5a Tcerg1l  with  35  cells.\n",
      "\n",
      "\tIndexing L5b Cdh13 as glutamatergic_neurons.L5b Cdh13  with  32  cells.\n",
      "\n",
      "\tIndexing L5b Tph2 as glutamatergic_neurons.L5b Tph2  with  27  cells.\n",
      "\n",
      "\tIndexing L6a Car12 as glutamatergic_neurons.L6a Car12  with  20  cells.\n",
      "\n",
      "\tIndexing L6a Mgp as glutamatergic_neurons.L6a Mgp  with  41  cells.\n",
      "\n",
      "\tIndexing L6a Sla as glutamatergic_neurons.L6a Sla  with  62  cells.\n",
      "\n",
      "\tIndexing L6a Syt17 as glutamatergic_neurons.L6a Syt17  with  13  cells.\n",
      "\n",
      "\tIndexing L6b Rgs12 as glutamatergic_neurons.L6b Rgs12  with  15  cells.\n",
      "\n",
      "\tIndexing L6b Serpinb11 as glutamatergic_neurons.L6b Serpinb11  with  20  cells.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# build an index object for fast searching\n",
    "\n",
    "index_custom <- buildAltSpliceIndex(scaled_matrix, metadata, \"glutamatergic_neurons\", \"primary_type\", qb = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'glutamatergic neuron.L2 Ngb'</li><li>'glutamatergic neuron.L2/3 Ptgs2'</li><li>'glutamatergic neuron.L4 Arf5'</li><li>'glutamatergic neuron.L4 Ctxn3'</li><li>'glutamatergic neuron.L4 Scnn1a'</li><li>'glutamatergic neuron.L5 Chrna6'</li><li>'glutamatergic neuron.L5 Ucma'</li><li>'glutamatergic neuron.L5a Batf3'</li><li>'glutamatergic neuron.L5a Hsd11b1'</li><li>'glutamatergic neuron.L5a Pde1c'</li><li>'glutamatergic neuron.L5a Tcerg1l'</li><li>'glutamatergic neuron.L5b Cdh13'</li><li>'glutamatergic neuron.L5b Tph2'</li><li>'glutamatergic neuron.L6a Car12'</li><li>'glutamatergic neuron.L6a Mgp'</li><li>'glutamatergic neuron.L6a Sla'</li><li>'glutamatergic neuron.L6a Syt17'</li><li>'glutamatergic neuron.L6b Rgs12'</li><li>'glutamatergic neuron.L6b Serpinb11'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'glutamatergic neuron.L2 Ngb'\n",
       "\\item 'glutamatergic neuron.L2/3 Ptgs2'\n",
       "\\item 'glutamatergic neuron.L4 Arf5'\n",
       "\\item 'glutamatergic neuron.L4 Ctxn3'\n",
       "\\item 'glutamatergic neuron.L4 Scnn1a'\n",
       "\\item 'glutamatergic neuron.L5 Chrna6'\n",
       "\\item 'glutamatergic neuron.L5 Ucma'\n",
       "\\item 'glutamatergic neuron.L5a Batf3'\n",
       "\\item 'glutamatergic neuron.L5a Hsd11b1'\n",
       "\\item 'glutamatergic neuron.L5a Pde1c'\n",
       "\\item 'glutamatergic neuron.L5a Tcerg1l'\n",
       "\\item 'glutamatergic neuron.L5b Cdh13'\n",
       "\\item 'glutamatergic neuron.L5b Tph2'\n",
       "\\item 'glutamatergic neuron.L6a Car12'\n",
       "\\item 'glutamatergic neuron.L6a Mgp'\n",
       "\\item 'glutamatergic neuron.L6a Sla'\n",
       "\\item 'glutamatergic neuron.L6a Syt17'\n",
       "\\item 'glutamatergic neuron.L6b Rgs12'\n",
       "\\item 'glutamatergic neuron.L6b Serpinb11'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'glutamatergic neuron.L2 Ngb'\n",
       "2. 'glutamatergic neuron.L2/3 Ptgs2'\n",
       "3. 'glutamatergic neuron.L4 Arf5'\n",
       "4. 'glutamatergic neuron.L4 Ctxn3'\n",
       "5. 'glutamatergic neuron.L4 Scnn1a'\n",
       "6. 'glutamatergic neuron.L5 Chrna6'\n",
       "7. 'glutamatergic neuron.L5 Ucma'\n",
       "8. 'glutamatergic neuron.L5a Batf3'\n",
       "9. 'glutamatergic neuron.L5a Hsd11b1'\n",
       "10. 'glutamatergic neuron.L5a Pde1c'\n",
       "11. 'glutamatergic neuron.L5a Tcerg1l'\n",
       "12. 'glutamatergic neuron.L5b Cdh13'\n",
       "13. 'glutamatergic neuron.L5b Tph2'\n",
       "14. 'glutamatergic neuron.L6a Car12'\n",
       "15. 'glutamatergic neuron.L6a Mgp'\n",
       "16. 'glutamatergic neuron.L6a Sla'\n",
       "17. 'glutamatergic neuron.L6a Syt17'\n",
       "18. 'glutamatergic neuron.L6b Rgs12'\n",
       "19. 'glutamatergic neuron.L6b Serpinb11'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"glutamatergic neuron.L2 Ngb\"        \"glutamatergic neuron.L2/3 Ptgs2\"   \n",
       " [3] \"glutamatergic neuron.L4 Arf5\"       \"glutamatergic neuron.L4 Ctxn3\"     \n",
       " [5] \"glutamatergic neuron.L4 Scnn1a\"     \"glutamatergic neuron.L5 Chrna6\"    \n",
       " [7] \"glutamatergic neuron.L5 Ucma\"       \"glutamatergic neuron.L5a Batf3\"    \n",
       " [9] \"glutamatergic neuron.L5a Hsd11b1\"   \"glutamatergic neuron.L5a Pde1c\"    \n",
       "[11] \"glutamatergic neuron.L5a Tcerg1l\"   \"glutamatergic neuron.L5b Cdh13\"    \n",
       "[13] \"glutamatergic neuron.L5b Tph2\"      \"glutamatergic neuron.L6a Car12\"    \n",
       "[15] \"glutamatergic neuron.L6a Mgp\"       \"glutamatergic neuron.L6a Sla\"      \n",
       "[17] \"glutamatergic neuron.L6a Syt17\"     \"glutamatergic neuron.L6b Rgs12\"    \n",
       "[19] \"glutamatergic neuron.L6b Serpinb11\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'GABA-ergic neurons.Igtp'</li><li>'GABA-ergic neurons.Ndnf Car4'</li><li>'GABA-ergic neurons.Ndnf Cxcl14'</li><li>'GABA-ergic neurons.Pvalb Cpne5'</li><li>'GABA-ergic neurons.Pvalb Gpx3'</li><li>'GABA-ergic neurons.Pvalb Obox3'</li><li>'GABA-ergic neurons.Pvalb Rspo2'</li><li>'GABA-ergic neurons.Pvalb Tacr3'</li><li>'GABA-ergic neurons.Pvalb Tpbg'</li><li>'GABA-ergic neurons.Pvalb Wt1'</li><li>'GABA-ergic neurons.Smad3'</li><li>'GABA-ergic neurons.Sncg'</li><li>'GABA-ergic neurons.Sst Cbln4'</li><li>'GABA-ergic neurons.Sst Cdk6'</li><li>'GABA-ergic neurons.Sst Chodl'</li><li>'GABA-ergic neurons.Sst Myh8'</li><li>'GABA-ergic neurons.Sst Tacstd2'</li><li>'GABA-ergic neurons.Sst Th'</li><li>'GABA-ergic neurons.Vip Chat'</li><li>'GABA-ergic neurons.Vip Gpc3'</li><li>'GABA-ergic neurons.Vip Mybpc1'</li><li>'GABA-ergic neurons.Vip Parm1'</li><li>'GABA-ergic neurons.Vip Sncg'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'GABA-ergic neurons.Igtp'\n",
       "\\item 'GABA-ergic neurons.Ndnf Car4'\n",
       "\\item 'GABA-ergic neurons.Ndnf Cxcl14'\n",
       "\\item 'GABA-ergic neurons.Pvalb Cpne5'\n",
       "\\item 'GABA-ergic neurons.Pvalb Gpx3'\n",
       "\\item 'GABA-ergic neurons.Pvalb Obox3'\n",
       "\\item 'GABA-ergic neurons.Pvalb Rspo2'\n",
       "\\item 'GABA-ergic neurons.Pvalb Tacr3'\n",
       "\\item 'GABA-ergic neurons.Pvalb Tpbg'\n",
       "\\item 'GABA-ergic neurons.Pvalb Wt1'\n",
       "\\item 'GABA-ergic neurons.Smad3'\n",
       "\\item 'GABA-ergic neurons.Sncg'\n",
       "\\item 'GABA-ergic neurons.Sst Cbln4'\n",
       "\\item 'GABA-ergic neurons.Sst Cdk6'\n",
       "\\item 'GABA-ergic neurons.Sst Chodl'\n",
       "\\item 'GABA-ergic neurons.Sst Myh8'\n",
       "\\item 'GABA-ergic neurons.Sst Tacstd2'\n",
       "\\item 'GABA-ergic neurons.Sst Th'\n",
       "\\item 'GABA-ergic neurons.Vip Chat'\n",
       "\\item 'GABA-ergic neurons.Vip Gpc3'\n",
       "\\item 'GABA-ergic neurons.Vip Mybpc1'\n",
       "\\item 'GABA-ergic neurons.Vip Parm1'\n",
       "\\item 'GABA-ergic neurons.Vip Sncg'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'GABA-ergic neurons.Igtp'\n",
       "2. 'GABA-ergic neurons.Ndnf Car4'\n",
       "3. 'GABA-ergic neurons.Ndnf Cxcl14'\n",
       "4. 'GABA-ergic neurons.Pvalb Cpne5'\n",
       "5. 'GABA-ergic neurons.Pvalb Gpx3'\n",
       "6. 'GABA-ergic neurons.Pvalb Obox3'\n",
       "7. 'GABA-ergic neurons.Pvalb Rspo2'\n",
       "8. 'GABA-ergic neurons.Pvalb Tacr3'\n",
       "9. 'GABA-ergic neurons.Pvalb Tpbg'\n",
       "10. 'GABA-ergic neurons.Pvalb Wt1'\n",
       "11. 'GABA-ergic neurons.Smad3'\n",
       "12. 'GABA-ergic neurons.Sncg'\n",
       "13. 'GABA-ergic neurons.Sst Cbln4'\n",
       "14. 'GABA-ergic neurons.Sst Cdk6'\n",
       "15. 'GABA-ergic neurons.Sst Chodl'\n",
       "16. 'GABA-ergic neurons.Sst Myh8'\n",
       "17. 'GABA-ergic neurons.Sst Tacstd2'\n",
       "18. 'GABA-ergic neurons.Sst Th'\n",
       "19. 'GABA-ergic neurons.Vip Chat'\n",
       "20. 'GABA-ergic neurons.Vip Gpc3'\n",
       "21. 'GABA-ergic neurons.Vip Mybpc1'\n",
       "22. 'GABA-ergic neurons.Vip Parm1'\n",
       "23. 'GABA-ergic neurons.Vip Sncg'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"GABA-ergic neurons.Igtp\"        \"GABA-ergic neurons.Ndnf Car4\"  \n",
       " [3] \"GABA-ergic neurons.Ndnf Cxcl14\" \"GABA-ergic neurons.Pvalb Cpne5\"\n",
       " [5] \"GABA-ergic neurons.Pvalb Gpx3\"  \"GABA-ergic neurons.Pvalb Obox3\"\n",
       " [7] \"GABA-ergic neurons.Pvalb Rspo2\" \"GABA-ergic neurons.Pvalb Tacr3\"\n",
       " [9] \"GABA-ergic neurons.Pvalb Tpbg\"  \"GABA-ergic neurons.Pvalb Wt1\"  \n",
       "[11] \"GABA-ergic neurons.Smad3\"       \"GABA-ergic neurons.Sncg\"       \n",
       "[13] \"GABA-ergic neurons.Sst Cbln4\"   \"GABA-ergic neurons.Sst Cdk6\"   \n",
       "[15] \"GABA-ergic neurons.Sst Chodl\"   \"GABA-ergic neurons.Sst Myh8\"   \n",
       "[17] \"GABA-ergic neurons.Sst Tacstd2\" \"GABA-ergic neurons.Sst Th\"     \n",
       "[19] \"GABA-ergic neurons.Vip Chat\"    \"GABA-ergic neurons.Vip Gpc3\"   \n",
       "[21] \"GABA-ergic neurons.Vip Mybpc1\"  \"GABA-ergic neurons.Vip Parm1\"  \n",
       "[23] \"GABA-ergic neurons.Vip Sncg\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first, check the avaliable cell types from previous annotations\n",
    "\n",
    "cell_type_glu <- cellTypeNames(index.glu)\n",
    "cell_type_GABA <- cellTypeNames(index.GABA)\n",
    "cell_type_glu\n",
    "cell_type_GABA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all the nodes encoded in the index object\n",
    "# nodes that are not detected, or did not pass filters will not be indexed\n",
    "\n",
    "node_list_glu <- scfindGenes(index.glu)\n",
    "node_list_GABA <- scfindGenes(index.GABA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mine marker nodes of an interested cell type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cellType</th><th scope=col>genes</th><th scope=col>tp</th><th scope=col>fp</th><th scope=col>fn</th><th scope=col>precision</th><th scope=col>recall</th><th scope=col>f1</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>66227</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000056296.17_5 </td><td>54</td><td> 41</td><td>40</td><td>0.5684211</td><td>0.5744681</td><td>0.5714286</td></tr>\n",
       "\t<tr><th scope=row>53908</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000039474.13_13</td><td>61</td><td> 66</td><td>33</td><td>0.4803150</td><td>0.6489362</td><td>0.5520362</td></tr>\n",
       "\t<tr><th scope=row>58342</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000042671.12_26</td><td>52</td><td> 48</td><td>42</td><td>0.5200000</td><td>0.5531915</td><td>0.5360825</td></tr>\n",
       "\t<tr><th scope=row>76099</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000089706.7_8  </td><td>48</td><td> 41</td><td>46</td><td>0.5393258</td><td>0.5106383</td><td>0.5245901</td></tr>\n",
       "\t<tr><th scope=row>17824</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000022425.16_5 </td><td>87</td><td>154</td><td> 7</td><td>0.3609959</td><td>0.9255320</td><td>0.5194030</td></tr>\n",
       "\t<tr><th scope=row>45434</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000033981.14_29</td><td>93</td><td>172</td><td> 1</td><td>0.3509434</td><td>0.9893617</td><td>0.5181059</td></tr>\n",
       "\t<tr><th scope=row>66540</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000056820.7_1  </td><td>41</td><td> 28</td><td>53</td><td>0.5942029</td><td>0.4361702</td><td>0.5030675</td></tr>\n",
       "\t<tr><th scope=row>54992</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000040183.13_30</td><td>47</td><td> 49</td><td>47</td><td>0.4895833</td><td>0.5000000</td><td>0.4947369</td></tr>\n",
       "\t<tr><th scope=row>42601</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000032366.15_8 </td><td>48</td><td> 54</td><td>46</td><td>0.4705882</td><td>0.5106383</td><td>0.4897959</td></tr>\n",
       "\t<tr><th scope=row>31464</th><td>glutamatergic neuron.L2/3 Ptgs2</td><td>ENSMUSG00000028222.2_8  </td><td>56</td><td> 80</td><td>38</td><td>0.4117647</td><td>0.5957447</td><td>0.4869565</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & cellType & genes & tp & fp & fn & precision & recall & f1\\\\\n",
       "  & <fct> & <fct> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t66227 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000056296.17\\_5  & 54 &  41 & 40 & 0.5684211 & 0.5744681 & 0.5714286\\\\\n",
       "\t53908 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000039474.13\\_13 & 61 &  66 & 33 & 0.4803150 & 0.6489362 & 0.5520362\\\\\n",
       "\t58342 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000042671.12\\_26 & 52 &  48 & 42 & 0.5200000 & 0.5531915 & 0.5360825\\\\\n",
       "\t76099 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000089706.7\\_8   & 48 &  41 & 46 & 0.5393258 & 0.5106383 & 0.5245901\\\\\n",
       "\t17824 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000022425.16\\_5  & 87 & 154 &  7 & 0.3609959 & 0.9255320 & 0.5194030\\\\\n",
       "\t45434 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000033981.14\\_29 & 93 & 172 &  1 & 0.3509434 & 0.9893617 & 0.5181059\\\\\n",
       "\t66540 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000056820.7\\_1   & 41 &  28 & 53 & 0.5942029 & 0.4361702 & 0.5030675\\\\\n",
       "\t54992 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000040183.13\\_30 & 47 &  49 & 47 & 0.4895833 & 0.5000000 & 0.4947369\\\\\n",
       "\t42601 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000032366.15\\_8  & 48 &  54 & 46 & 0.4705882 & 0.5106383 & 0.4897959\\\\\n",
       "\t31464 & glutamatergic neuron.L2/3 Ptgs2 & ENSMUSG00000028222.2\\_8   & 56 &  80 & 38 & 0.4117647 & 0.5957447 & 0.4869565\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 8\n",
       "\n",
       "| <!--/--> | cellType &lt;fct&gt; | genes &lt;fct&gt; | tp &lt;int&gt; | fp &lt;int&gt; | fn &lt;int&gt; | precision &lt;dbl&gt; | recall &lt;dbl&gt; | f1 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 66227 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000056296.17_5  | 54 |  41 | 40 | 0.5684211 | 0.5744681 | 0.5714286 |\n",
       "| 53908 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000039474.13_13 | 61 |  66 | 33 | 0.4803150 | 0.6489362 | 0.5520362 |\n",
       "| 58342 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000042671.12_26 | 52 |  48 | 42 | 0.5200000 | 0.5531915 | 0.5360825 |\n",
       "| 76099 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000089706.7_8   | 48 |  41 | 46 | 0.5393258 | 0.5106383 | 0.5245901 |\n",
       "| 17824 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000022425.16_5  | 87 | 154 |  7 | 0.3609959 | 0.9255320 | 0.5194030 |\n",
       "| 45434 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000033981.14_29 | 93 | 172 |  1 | 0.3509434 | 0.9893617 | 0.5181059 |\n",
       "| 66540 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000056820.7_1   | 41 |  28 | 53 | 0.5942029 | 0.4361702 | 0.5030675 |\n",
       "| 54992 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000040183.13_30 | 47 |  49 | 47 | 0.4895833 | 0.5000000 | 0.4947369 |\n",
       "| 42601 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000032366.15_8  | 48 |  54 | 46 | 0.4705882 | 0.5106383 | 0.4897959 |\n",
       "| 31464 | glutamatergic neuron.L2/3 Ptgs2 | ENSMUSG00000028222.2_8   | 56 |  80 | 38 | 0.4117647 | 0.5957447 | 0.4869565 |\n",
       "\n"
      ],
      "text/plain": [
       "      cellType                        genes                    tp fp  fn\n",
       "66227 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000056296.17_5  54  41 40\n",
       "53908 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000039474.13_13 61  66 33\n",
       "58342 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000042671.12_26 52  48 42\n",
       "76099 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000089706.7_8   48  41 46\n",
       "17824 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000022425.16_5  87 154  7\n",
       "45434 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000033981.14_29 93 172  1\n",
       "66540 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000056820.7_1   41  28 53\n",
       "54992 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000040183.13_30 47  49 47\n",
       "42601 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000032366.15_8  48  54 46\n",
       "31464 glutamatergic neuron.L2/3 Ptgs2 ENSMUSG00000028222.2_8   56  80 38\n",
       "      precision recall    f1       \n",
       "66227 0.5684211 0.5744681 0.5714286\n",
       "53908 0.4803150 0.6489362 0.5520362\n",
       "58342 0.5200000 0.5531915 0.5360825\n",
       "76099 0.5393258 0.5106383 0.5245901\n",
       "17824 0.3609959 0.9255320 0.5194030\n",
       "45434 0.3509434 0.9893617 0.5181059\n",
       "66540 0.5942029 0.4361702 0.5030675\n",
       "54992 0.4895833 0.5000000 0.4947369\n",
       "42601 0.4705882 0.5106383 0.4897959\n",
       "31464 0.4117647 0.5957447 0.4869565"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# L2/3 Ptgs2 cells looks interesting since it spans two cortical layers\n",
    "interested_cell_type <- \"glutamatergic neuron.L2/3 Ptgs2\"\n",
    "\n",
    "# rank the splicing nodes by their frequency of above-mean inclusion in the dataset\n",
    "# compare to background cell types from L2-l4\n",
    "markers_Ptgs2 <- cellTypeMarkers(index.glu, \"glutamatergic neuron.L2/3 Ptgs2\", background.cell.types = cell_type_glu, top.k = 10)\n",
    "markers_Ptgs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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btmwRQrRt2zYsLGzZsmVm\ns9lisaxfv764uLhTp07eqwoAAAAAIPPuKt5JkyYtXrz49ddft9vtd91114wZM+RPKH/88cfC\nwsJHH31UvjjhihUrJkyYYLfbW7RoMWvWrOjoaK9WBQAAAAAQ3g6EgYGBU6ZMqdw+bdo05/dx\ncXEzZ870ahkAAAAAUB2bzfbMM8+sW7dOkqSzZ8/GxMQIIU6fPj18+PBjx47t27fvwQcf9HWN\n3sLp3QEAAAAo2s6dO1euXDlkyJA1a9aEh4cLIRYtWnTfffdlZ2f7ujSv48SvAAAAABQtJydH\nCDF+/PgePXoIIQ4cOPDCCy/MnTtXp9ONGzfO19V5F3sIAQAAAChX7969R48eLYTo2bOnSqW6\nePFiZGTkoUOHJk6c6OvSbgUCIQAAAADl+uCDD2bNmiWEWLJkyZEjR5o1a3bnnXfec889vq7r\nFmHJKAAAAADlatOmTcuWLYUQCQkJCrwAHnsIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAA\nACgUgRAAAAAAFIqzjAIAAADAf+3fv//06dPyN0KIrVu3yjf79esXGxvr4+LqG4EQAAAAAP5r\n+fLlS5cudd58++235W+2bNly+wVClowCAAAAULTRo0dLkvTggw/KN5csWSJVZdCgQb6t0xsI\nhAAAAACgUARCAAAAAFAoAiEAAAAAKBSBEAAAAAAUikAIAAAAAApFIAQAAAAAhSIQAgAAAIBC\nEQgBAAAA4FbLy8sbMWJE8+bNIyIiBg0adPHixcpjOnTooHIRHBwshNizZ4+qkr///e+1K4NA\nCAAAAAC32ujRoy9durRt27aDBw+aTKZBgwbZ7fYKY3JzcxcsWJD5H2fPnhVCdOvWLdPFvn37\ngoODH3nkkdqVoa3r8wAAAACABiuzSLpa5Kj3aRMj1KF6VbUPmpm5ZcuWY8eOdejQQQixcOHC\nJk2a7N69u3fv3q7DcnNz4+PjY2JiXBv1er1ry5gxY1566aV27drVrk4CIQAAAADl2pdp2/SL\nrd6nffUB/T2R1QbCo0ePGgwGOQ0KIcLCwhITEw8dOuQaCMvLy0tKStavXz9jxowbN2506tTp\nL3/5S5s2bVznWb169blz57788sta18mSUQAAAADKJUmSJDnq/UtIkpsHvX79enh4uEr138QY\nGRmZnZ3tOqawsDAqKspisSxatGjt2rWlpaU9e/bMz893DrDb7bNmzXrttdcCAgJq/fTZQwgA\nAABAuVqGql/orHdtWXPK8qv55haRdm2u7db8/8tWBm21uwdlrmmwypbIyMisrKz/VrVmTXR0\n9Lp168aOHSu3fP7558XFxaNGjbqpUisgEAIAAABQrov59o1nLXWc5MAV64ErVteW1x40ulmP\nGRUVlZOTI0mSMwRmZ2dHRUW5eYiQkJAWLVpkZmY6W1auXJmamqrV1inTsWQUAAAAgJJJQnLU\n/5dbnTt3Li8v//777+WbOTk5P//8c/fu3V3HnDx5cty4cRbL/4VVs9l8+fLl+Ph4+WZ+fn56\nevqjjz5axydPIAQAAACgXJK3uHvQZs2aDR06dPz48cePHz979uyoUaM6duzYo0cPIcTSpUvn\nz58vhIiOjt6wYcO4ceMuXLhw5syZ3/3ud+Hh4ampqfIM33//vdVqbd26dR2fPoEQAAAAgLJJ\nUv1/CbeJUIhly5bdfffdAwYM6N69u8Fg2LRpk7x8ND09fcuWLUKIiIiIXbt2Xb16Vc6KNptt\n7969gYGB8t2vXbumUqmio6Pr+NQ5hhAAAACAgkmSVNMKT28wmUzLly+v3L569Wrn90lJSbt2\n7ary7iNGjBgxYkTdyyAQAgAAAFAyyf0lImo9a4NAIAQAAACgXJIkajjgr7YTe2HO+kcgBAAA\nAKBkvlky6icIhAAAAAAUTPLOktEGgkAIAAAAQLkk75xUxjvLUOsfgRAAAACAsjWQ8OYNBEIA\nAAAAClbjVeRrO68X5qx/BEIAAAAASiYJTioDAAAAAAokeWkPYcPYQUggBAAAAKBwLBkFAAAA\nACWSWDIKAAAAAArFklEAAAAAUCaJJaMAAAAAoEiSd/YQNow4SCAEAAAAoGSSJEleOYawYURC\nAiEAAAAAJfPOSWUaRh4kEAIAAABQMkl45aQyDQSBEAAAAICSSZxUBgAAAACUyEvHEDaMOEgg\nBAAAAKBoknf2EDaQREggBAAAAKBsLBkFAAAAACWSvHIdwoaCQAgAAABAybxz2YkGsodQ7esC\nAAAAAMBnJEneR1jPasyDeXl5I0aMaN68eURExKBBgy5evFh5TIcOHVQugoOD3bfXAnsIAQAA\nACiZly47UYPRo0fn5uZu27YtMDBw1qxZgwYNOn78uEajcR2Tm5u7YMGCIUOGyDfVarX79log\nEAIAAABQMO9cdsL9ktHMzMwtW7YcO3asQ4cOQoiFCxc2adJk9+7dvXv3dh2Wm5sbHx8fExNT\n4e7VtdcCS0YBAAAAKJckvLJk1P1Ox6NHjxoMBjkNCiHCwsISExMPHTrkOqa8vLykpGT9+vUd\nO3aMi4tLTU09e/asm/baIRACAAAAUDBJEpKj/r/cun79enh4uEqlcrZERkZmZ2e7jiksLIyK\nirJYLIsWLVq7dm1paWnPnj3z8/Ora6/ds2fJKAAAAADlahlhnNL7DteWNUeu/ppfdlOT3H9H\n2APx4a4tBl0N+95c02CVLZGRkVlZWf+tas2a6OjodevWjR07trr2m6pZRiAEAAAAoFwXc0pW\nH75Sx0kOnc89dD7XteXtx+9yMz4qKionJ0eSJGcIzM7OjoqKcnOXkJCQFi1aZGZmetjuIXeB\nsG3btp5Mcfr06do9NgAAAAD4Ws0rPGvD7UGEnTt3Li8v//777zt16iSEyMnJ+fnnn7t37+46\n5uTJk/Pnz1+4cGFAQIAQwmw2X758OT4+vrr22pXpLhA2bty4dpMCAAAAQIMgX4fwFj9os2bN\nhg4dOn78+GXLlhmNxilTpnTs2LFHjx5CiKVLl5rN5smTJ0dHR2/YsMFiscyaNctqtU6fPj08\nPDw1NbW0tLTK9tpV4i4Q7t+/v3aTAgAAAEAD4ZvrEC5btmzSpEkDBgywWq09evTYtGmTvHw0\nPT09Jydn8uTJERERu3btmjp1aseOHQMCArp167Z3797AwMDAwMAq22tXBscQAgAAAFAwX+wh\nFEKYTKbly5dXbl+9erXz+6SkpF27dlUeU117LXAMIQAAAAAFky874Y1pGwKOIQQAAACgXJKo\n6SrytZy2YajTMYRms/natWv1Wg8AAAAA3EJSg9mb5w01XC3RvUOHDnXt2rW+SgEAAACAW07y\nhoYSMj09qcyXX365atWqy5cvOxz/t77Wbrf/7//+r16v91ptAAAAAOBlXjqGsIHwKBCuXr36\nqaee0mq1TZs2vXLlSrNmzXJzc8vKypKTk6dOnertEgEAAADASyQhSV4JhA1jD6FHS0bfeeed\n/v375+bmZmZmajSanTt3FhUVLViwQJIk+eKJAAAAANAgSf93cfp6/mogPAqEZ8+efe6550JC\nQuSbkiRptdrnn38+KSnplVde8WZ5AAAAAOBVXjqG0NdPyzMeBUKr1arRaOTvg4KC8vPz5e9T\nU1M3bNjgrdIAAAAAwNu8sXtQkm6rJaOJiYlLly61WCxCiNjY2J07d8rtubm5BQUFXqwOAAAA\nALxJPoaw/r98/bw85NFJZV588cWRI0fm5eXt2rVr6NChf/7zn7Ozs2NiYhYvXtyhQwdvlwgA\nAAAA3qLs6xB6FAhHjBih1WovXrwohEhLSzt48ODHH38shIiNjZ0/f75X6wMAAAAA75GEJHkj\nEDaQkOnpdQiHDRsmfxMYGPjVV1+dO3fOarXeeeedOp3Oa7UBAAAAgJcpew+hR8cQCiGysrLe\nf/99583Q0NDPP/88Ly/PO1UBAAAAwK3hlWMIff2kPOVRIDxz5sy9997reg36kpKSWbNmdejQ\n4cKFC16rDQAAAAC8TfLaVwPgUSBMS0sLDg7ev3+/syUuLu7UqVPBwcHTpk3zWm0AAAAA4F2S\n5J0LETaMPOhZIPzuu++mT5/euXNn18bExMRp06alp6d7pzAAAAAA8D5JEg5H/X81kD2EHp1U\nxmw2BwQEVG4PDg622+31XRIAAAAA3CoNaHeeF3i0h/Dee+9duXJlhexXVFT03nvv3Xvvvd4p\nDAAAAABuCUnywpevn5RnPNpDOHPmzAEDBrRp02bAgAGRkZEOhyMzM3Pr1q03btzYtm2bt0sE\nAAAAAC/x2klBG0Yi9CgQ9uvXb+fOna+88srChQudjffcc8/y5cv79evntdoAAAAA4BZoGOHN\nGzy9MH2fPn369Olz48aNX3/9VaPRxMbGhoSEeLUyAAAAAPA+iQvTe6SsrOzChQvnzp1r0qRJ\nSEiIzWbzXlkAAAAAcCt457ITvn5WnvI0EL777rtNmjTp0qXL0KFDz507J4SYNWvWmDFjiIUA\nAAAAGjJvnFGm5r2OeXl5I0aMaN68eURExKBBgy5evFh5TIcOHVQugoOD5faff/558ODBERER\n4eHhjzzyyIEDB2r95D0KhB9//PHUqVOTk5MXLVrkbExISPjnP/85b968Wj82AAAAAPiWJEny\neWXq+aumxx09evSlS5e2bdt28OBBk8k0aNCgypf0y83NXbBgQeZ/nD17VghhsVh69+7dqFGj\nf/3rX4cPH46NjR04cGBRUVHtnr5HxxD+/e9/nzBhwocfflhWVjZhwgS5cdSoUadPn16yZMm0\nadNq99gAAAAA4HteWeHpbs7MzMwtW7YcO3asQ4cOQoiFCxc2adJk9+7dvXv3dh2Wm5sbHx8f\nExPj2lhQUPDCCy+MHz9ePqvL9OnTP/nkk/PnzyclJdWiSo/2EJ49ezY1NbVy+8MPP5yRkVGL\nRwUAAAAAv+CNIwhrug7h0aNHDQaDnAaFEGFhYYmJiYcOHXIdU15eXlJSsn79+o4dO8bFxaWm\npsp7CCMjI6dOnSqnwdzc3Pnz57dt2zYxMbF2z96jQGgymcrKyiq3FxQUGI3G2j0wAAAAAPiF\nW34A4fXr18PDw1UqlbMlMjIyOzvbdUxhYWFUVJTFYlm0aNHatWtLS0t79uyZn58v99rtdoPB\nEBERcerUqV27dun1+to9dY+WjN5zzz3vvPNOr169XCvOzc2dPXt2165da/fAAAAAAOBzdzRt\n9Mqw7q4tn6Qfv5JTeFOTPNi+Rc+741xbjAEa93dxzVZVtkRGRmZlZTlvrlmzJjo6et26dWPH\njhVCaDSaH3/8MSsr6+9///vDDz98+PDhsLCwm6pZ5lEgfPXVV3v37n3PPff85je/EUJ8/PHH\nixYt2rBhQ2lpqetpZgAAAACgYTl/LXfFVz/WcZL9Jy7tP3HJtWXh879xMz4qKionJ0eSJGcI\nzM7OjoqKcnOXkJCQFi1aZGZmOlvatm3btm3bHj16REREfPrpp88991wtKvdoyejDDz+8c+fO\nkJCQ+fPnCyGWLVu2YsWKtm3bpqend+/evca7AwAAAICf8s5ZRt0fQ9i5c+fy8vLvv/9evpmT\nk/Pzzz9XyFYnT54cN26cxWKRb5rN5suXL8fHx3/11Vd33nlnSUmJ3K5Wq3U6XeX9jR7yaA+h\nEKJXr17Hjh3Lzs7+9ddfhRBxcXG12yMJAAAAAH7Eg0P+ajevm75mzZoNHTp0/Pjxy5YtMxqN\nU6ZM6dixY48ePYQQS5cuNZvNkydPjo6O3rBhg8VimTVrltVqnT59enh4eGpqanl5udlsHj16\n9Ouvv24wGBYsWFBcXNy/f//aVenRHsIHHnhg27ZtQogmTZokJSUlJSWRBgEAAADcBiQvnWe0\npsddtmzZ3XffPWDAgO7duxsMhk2bNsl7+dLT07ds2SKEiIiI2LVr19WrV+WsaLPZ9u7dGxgY\nGBYWtmvXrqKioi5duiQlJR06dGjLli3x8fG1e/oe7SHMzMw8ffr0wIEDa/cYAAAAAOCnvLWH\nsAYmk2n58uWV21evXu38PikpadeuXZXHtG/ffvv27fVShkd7CBcuXLhkyZKNGzdardZ6eVQA\nAAAA8BNe2UHog4xZGx7tIXznnXe0Wu2QIUMCAgIaN26s0+lcey9evOiV0qpiMBgCAwNv2cNV\noFarhRBBQUFcfbE6Go1GvkQmqqPVakNDQ31dhZ9Sq9UqlarCXxg4aTQaIURwcLAHi1AUSqVS\n8fqqjrwMKSAggE1UHY1GI0lSrS/kddvTarVCCJPJ5OtC/JdGo+H1VR35XbTBYAgICPB1LVWS\nhHB4Z9oGwKNA6HA4IiMje/Xq5e1qamSxWGw2m68e3WAwGI3G0tJS9pRWx2QylZSUOBzeeEU1\neCqVqlGjRna73Ww2+7oWPxUQEKDRaEpLS31diJ8KDAzU6/UlJSV2u93Xtfip0NBQXl/V0Wg0\nJpPJarU6z0qHCoxGo91ud57NDxWEhIRotdri4mI+k6qSSqUKCQnhT1B1dDpdcHCwxWIpKyur\n+2z1fzYTHy0Z9RMeBcL9+/d7uw4PORwOH74TknOOb2vwc5Ik2e12AmGV5I/n5U3k61r8lMPh\nUKlUbJ/qyG/C+BPkHhunOvwJqpHD4WD7uCH/CbLb7QTCKqnVan5/3JAXufjtvzDpP7/hXpi4\nAfD0shMAAAAAcBtiDyEAAAAAKJY39hA2lIxJIAQAAACgYJIkJE4qAwAAAADKIwlPLiN/2yIQ\nAgAAAFAwL+0hbCAZk0AIAAAAQLm8toewYSRCAiEAAAAABZMazhlgvIBACAAAAEDJOIYQAAAA\nAJTJS3sIG0jIJBACAAAAUC5J8soewoYRBwmEAAAAAJRNEsIb1yFsGAiEAAAAAJSNs4wCAAAA\ngBJ5Z8loQ0EgBAAAAKBsXjmpTP1P6Q0EQgAAAADKJUmSJHnjGMKGkQgJhAAAAACUTGool4jw\nBgIhAAAAAAWThFeOIWwgGZNACAAAAEDJvLSHsGEkQgIhAAAAAOXy0jGEDSMOCqH2dQEAAAAA\n4EOSkLzwVZO8vLwRI0Y0b948IiJi0KBBFy9erDymQ4cOKhfBwcHOrjNnznTt2lWrresePgIh\nAAAAAAWT5J2E9azGTDh69OhLly5t27bt4MGDJpNp0KBBdru9wpjc3NwFCxZk/sfZs2fl9jVr\n1iQnJyckJNT92bNkFAAAAICSSbd+gWdmZuaWLVuOHTvWoUMHIcTChQubNGmye/fu3r17uw7L\nzc2Nj4+PiYmpcPfy8vKDBw8eO3bs008/rWMl7CEEAAAAoGy3fMno0aNHDQaDnAaFEGFhYYmJ\niYcOHXIdU15eXlJSsn79+o4dO8bFxaWmpjr3EI4aNapFixb18tQJhAAA/uAP7gAAIABJREFU\nAACUyxvrRaWaAuH169fDw8NVKpWzJTIyMjs723VMYWFhVFSUxWJZtGjR2rVrS0tLe/bsmZ+f\nX79PnyWjAAAAAJSrdYumf/qfx11bPl7/zeVrOTc1ycOd2/Xq0t61xWjQub+LaxqssiUyMjIr\nK8t5c82aNdHR0evWrRs7duxN1eYegRAAAACAcv1y6dpHX+yq4yS7D5/cffika8vyP/2Pm/FR\nUVE5OTmSJDlDYHZ2dlRUlJu7hISEtGjRIjMzs46lVsCSUQAAAAAK5o0DCKUaTlTTuXPn8vLy\n77//Xr6Zk5Pz888/d+/e3XXMyZMnx40bZ7FY5Jtms/ny5cvx8fH1++zZQwgAAABAuSQhajzk\nr941a9Zs6NCh48ePX7ZsmdFonDJlSseOHXv06CGEWLp0qdlsnjx5cnR09IYNGywWy6xZs6xW\n6/Tp08PDw1NTU4UQWVlZNpvtxo0bQogrV64IIRo1auR6lULPsYcQAPzI3LlzI6thNBrlS9bK\nY/r06eNwOCrcPSUlxfV01bm5ue+++25ycnKbNm2io6MTEhJSU1O3bt1a4eHatGnj/PTR1auv\nvhoZGSn/cxJCpKWlRUZGZmRkVB7Zo0ePjh07ev64suvXr7/55pvJycl33HFHdHR0u3btxowZ\ns3v37grDTp06NWLECPmk2/3799+5c2d1vQ888ICbXvf3rd/eb7/9NjU1NSEhISYmJjk5ecWK\nFa5vNQ4cODB48OC4uLi4uLiUlP/H3p3HNXXl/+M/CQkQloRFFllFCwwuFRUVF1REUPlZEUEW\nERSLSr8qtlgFKtgphYoyorTjgsWtVEFQBEGo4IJVlFFGUZjaFhUZxaIgmwQJgeT3x/1MJpNA\niAsSyuv58A9ylnvOSQLmnXPuOW43btyQfkoJIVVVVaampqIzppqbm3t6b+Tl5cmu25MHDx44\nOzvr6ekVFxdLZMkeQq/67soAAO+eUEiEgj7410uzhw4dGjNmzPz586dNm6aqqpqdnU0tHy0s\nLMzJySGE6Orqnj9/vqamhooVOzs7L1++rKamRgixt7c3NTUNCgrq6uoyNTU1NTVNTk5+s9Fj\nhhAAQOGsX79eei9pFRUVXV1d0ZG1ZWVlhw4dCgoK6ukijY2Nc+bMqa+v9/X1tbe3ZzAYDx8+\nTEtLCwwM3LlzZ0BAAFWMTqe3tLTk5+e7ubmJV+fz+adOnWIye7kh/o3bLS4uXr58OZfLnTdv\n3uLFixkMRnV1dVZWVm5ubkhISFRUFFWsqqpqwYIF+vr6kZGRTCYzOzvb39//6NGj8+fPl849\ne/asjFzZdd9hbkFBgb+//+jRozdt2qSkpJSZmfn555/X19dv3LiRyvXz8xs5cuSXX36pqqq6\nZ88ed3f3s2fP2traij+NQqHws88+a29vp/7jJ4SwWKxdu3ZJPNuXL1/Ozs4eNmyY7LrdOnLk\nyNatW3V0dKSzZA+hV313ZQCAvtH7pqBvdlnZ2Ww2+8iRI9LpaWlpop9tbW3Pn+/m/kbqO+J3\ngjawvpZrbm7m8/n91TqLxVJXV29paen2q3QghGhpabW0tEjPWgAhhEaj6erq8vn85ubm/u6L\nglJRUVFSUmpra+vvjvSnhISEbdu25efn29nZSWRpaGioqqo2NTXt2LEjPj7eycnp+vXr169f\n19fXF5Vxc3PjcrnU/xz79++PiopKTk4Wj/RaW1tnzZrV1tZWVlamrKyckJAQFxc3efJkNTW1\nEydOiDeXl5e3YsWK8ePHc7ncK1euEELCw8MPHjx448YNCwsLib45ODhwudxbt27J2e7Tp09n\nzJjBZDLT09PHjBkjXiwgIODKlSvHjh1zcXEhhKxZsyY/P//mzZvUffadnZ1z5szhcrk3b96U\nzmWz2VSHu82VXfcd5k6cOFEoFF69elVVVZUQwufzZ8yY0dzc/MsvvxBCJk+e/OrVq5KSEipa\na25unjJlio2NzalTp8Sf0h9++CEiImLq1Kl379797bffun23tLa2Tp061dnZeefOnb3WZTAY\nWlpa7e3tra2thJCbN2+6u7t/9dVX6urq69evz8rKEr9xRfYQZOu7K/c1NTU1gUDQ3t7e3x1R\nUBwOh8lkvnjxYmB9dHxv6HQ6m81+5+cB/GkoKyuz2Wwul/vq1au3v9qQIUPe/iLidhw6vT+9\n4N1ekxDywzch08fbvPPLvnNYMgoAMPB0dnbGxMTw+fzIyMieyjx79owQ8uGHH4onamho5OXl\n3b59W1lZmUoRCoUuLi5FRUVPnz4VL3nixIlx48ZxOJzX7Zs87e7du7e5uTk+Pl48GqSK7du3\nb+vWrTY2NoSQrq6u/Pz8uXPninZdYzAYS5cuffToUXl5uWLmdnZ2BgUFxcTEUAEPIYTJZE6c\nOLGurq69vb2+vv7hw4dz584Vzd1xOJwlS5ZcvXq1sbFR9DzU1tZ+9dVXGzZsMDY2lvFUx8bG\n8ng8ifeAnHWHDBlSUFDQ7cblsocg45p9fWUAgL4i7IeD6RUHAkIAgAHJyMgoNDT09OnTRUVF\n3RagQrKoqKiGhgbxdH19fRUVFfGUhQsX0mi09PR0UUpDQ0NhYaGHh8cbTAXI0+65c+d0dXVd\nXV2lqxsYGKxfv97U1JQQUlVV9erVq9Gj/+dYp1GjRhFCKioqFDOXwWCsWbNm3rx5oiyhUPjg\nwQMTExNVVVVqgQmLxRKva2xsLBAIxKcBN2/ebGho+Omnn0o/PyKVlZWHDh0KCwvT1tYWT5en\nLiHEwsJi5MiR3WbJHoLsy/bplQEA+oiwb46m7+9hyQv3EAIAKJympqbnz59LJAqFQvEPzUKh\ncO3atenp6WFhYVeuXBHN+Im4ubkVFBScPHlyzJgx06dPnzJlir29vZ2dHYMh+Zff0NBwxowZ\nx48fF0URJ0+eFAqFHh4e3d63IFuv7XZ2dj569GjGjBl0ei9fStbX1xOppUF6enqEkLq6OsXM\nFaV0dHTU1dX98ccfycnJt2/fPnDgACHE0NBQS0vr2rVr4nVLS0tFgyWEZGdn//TTT7m5udKv\nqbj4+HgDA4Nly5aJJ8pZV07dDuGd6LsrAwC8CWqG8N1fdmDEhAgIAQAUjq+vr3RicHDwvn37\nxFOYTOaOHTvc3d137969efNmifJ0On3fvn1+fn4nTpwoKiq6ePEiIYTD4fj4+ISFhWlqakq0\nuHr16pKSEnt7e0JIamqqk5OTrq7uG3S+13ap20QltsZubW0VXzNJo9FMTEyoZYQSG9tQ04zt\n7e2KmStKuX79uqenJyHE1NQ0JSXFycmJenLWrl0bGxsbHh4eHByspKSUkpJy9epVQkhnZych\npLGxMTw8PDAwcNKkSTKe5MrKyuzs7NjYWPHAT8668ut2CAp+ZQCAN9InE3oDIxxEQAgAoIBi\nYmIsLS0lEq2srKRLTp8+3dPTMzEx0dPTc/jw4d0WmD59OiGkurr62rVrGRkZSUlJxcXFhYWF\n4lOFrq6uHA7n2LFj9vb29+7dq6ioeMtdH2W0q6GhQafTJRaUpqWlRUREiB6y2ewHDx5QSysl\n9vGigi4Wi6WYuaKUMWPG/Pjjj42NjRcuXPD19d2wYcOWLVsIIWvXrm1oaEhKSjp48CAhxNHR\nMTIycsOGDerq6oSQqKgoJpMp49ZQypEjRxgMho+Pj3iinHXl19MQFPnKAABvoo9mCAcIBIQA\nAApnwoQJ3e4y2m3h6OjogoKCsLCwjIwM6vyiblGn3vn6+n7xxRfff/99UVGR+ImFKioqixYt\nysjIiIuLS01N1dLSojb5FCc9CSbS2traU/e6bdfa2rq8vJzH44nuKnRxcTEyMqJ+TkpKqqio\nIIRQG6iK1lJSamtrCSEGBgaKmStK0dHRmTt3LiHEx8dn+PDhCQkJrq6u48aNYzKZ0dHRn376\naXV19dChQw0NDb///ntCiJmZ2aVLl9LT05OTk+l0OpfLJYRQp4xwuVwGgyF6roRC4dmzZ2fM\nmCH+nMtZ97X0NIQ3uNR7uzIAwBsQEqGQ9MUm+QMjyMSmMgAAA5uenl5ERERRUVFmZqZoHSOP\nx0tLS6OOtZUwa9YsQkhNTY1Euo+PT1tb24ULFzIzM93d3aVvQqMOu7t7965Eek1NTU1NDTWl\nKWe71PEYKSkpolwzMzPX/xBFhubm5pqamnfu3BG/zu3btwkhY8eOVczcurq6gwcP/vOf/xTP\npcJ78ZMVdHR0xo0bZ2hoSAi5fPmytrb2iBEjzp07JxQKP/7442H/kZaW1tDQMGzYsMDAQFHd\nW7du1dTUSGzJI2ddecg5hDfQd1cGAHg7fbDF6MCZcsQMIQDAgLdy5cq0tLSoqCjRslJlZeXE\nxMQXL17Y2Nh88MEHopICgSAjI4MQIrFJJiHEzs7O0tIyMTHx2bNnXl5e0q24urp++eWX27dv\nd3FxEe1syePxIiIihEIhtbuJnO0GBQWlpKRER0cPGzZMfKKSEPLw4cPy8nJqvxk6ne7m5paR\nkfHkyRMTExNCSEdHR2pq6siRI62trQkhErk8Hk9Gruy67yq3oaHhiy++mDJlSmZmpmjXHGon\nWKpwSEhISUnJ1atXqZC7vLz8/PnzgYGBDAbjk08+cXd3F382vv3225KSkuPHj2tpaYkSqT1p\nJkyYIF5SzrryUFJSkj2EN9Z3VwYAeBtCIREK+mJTmXd/yb6AgBAAQOHk5OSUlZVJJKqoqDg4\nOIhHWSJ0Oj0+Pn7evHl1dXXUqQ80Gi0hIWHp0qVOTk6LFy+2tbVlMpm1tbW5ubnl5eX+/v4S\n4QTF29s7JiZmxIgR0gtWCSEGBgbx8fEhISH29vZeXl4mJia1tbVZWVlPnjwJDg6ePXu2/O1y\nOJzU1FQfHx9fX9+ZM2dOmzZNU1Ozvr6+tLT06tWrurq6om0nN23alJeXt2jRIn9/fzU1tdOn\nTz9+/JiKLaVzc3NzZeTKrvuucnV0dNavX5+YmLhgwYKFCxeqqqpev3799OnTdnZ2Dg4OhJD5\n8+enpaV5eXn5+PjU19d/9913RkZGn3/+OfnP8lrx51xPT4/BYEyePFk8sbKykiosnthr3fz8\nfH9//+3bty9fvpwQcuPGjd9//536gRBSWFhYVVVFCHFwcDA3N5c9BNn67soAAH1GSAbxklEE\nhAAACmfv3r3dpn/99dfr1q3rNmvcuHHLly8/fPiwKGXKlCkFBQV79+4tLi7OyMjo6urS1ta2\ntbUNDQ1dsGBBtxfx8vL65ptvvL29e+qYt7e3paXlnj17MjMzGxoa2Gy2ra1tXFwcdT/Ya7Vr\nY2Nz9erV5OTk/Pz8v//97+3t7dra2jY2NjExMX5+fqLdWYyMjM6ePRsdHZ2YmMjn821tbdPT\n06dNm9Zt7sSJE2Xkyq77DnO3bNliamr6ww8/fPPNN3Q63czMbO3atRs3bqTmxObPn5+UlPTt\nt99u2rRJTU3N2dk5MjLytTZ0bWxspNPp1CY08hMIBF1dXaJt9NLT048ePSrK3bNnD/XDgQMH\nzM3NZQ9Btr67MgBAXxlQKzzfOdoAOjORENLc3Mzn8/urdRaLpa6u3tLSIrG5HIhoaWm1tLQI\nBH3xFcuAR6PRdHV1+Xx+c3Nzf/dFQamoqCgpKVFnEoA0DQ0NVVXVpqYm6nwCkKajoyOxeSmI\nMBgMLS2t9vb21tbW/u6LglJTUxMIBN1umwSEEA6Hw2QyX7x4MbA+Or43dDqdzWY3NTX1d0cU\nlLKyMpvN5nK5r169evurSZwE+/a2JZ3Ye6ybu9/f0vGEcAc7yRs0FBBmCAEAAAAAYBDroxnC\nAfLtCQJCAAAAAAAYvIRCHEwPAAAA0JuLFy/KuMWUEBITE7NmzZr31h8AgHdnoIRv7x4CQgAA\nGNR4ncLy2ld1rZ00GhmqyRxpqMqk0/q7Uwpq8uTJP//8s4wCBgYG760zAADvTp/MEA4UCAgB\nAGCQEgiFOb+0ZP+rqZ3/362wNFXonh9qO1ux+7FjCktdXd3Gxqa/ewEA8K4J++Z+vwESZCIg\nBACAwUggFO6+Ulf6mCuR/pInOHzzRXVjR9Dkd7yLHQAAKKpBfewEAkIAABiMTpc3SUeDIhfv\nv7TQUXGy1HyfXQIAgH4hFJLBvGQU58ACAMCg08rryvmllxNBM+42dnYN3s8HAACDiZAIBe/+\n3wDZqAYzhAAAMOjcefqqo7dgr6W969e69tGGrPfTJQAA6Dd9dOzEwIgHMUMIAACDzx8v+fIU\ne9osVzEAABjwqLPp3+2/AQIzhAAAMOjI+d/0gPnPHAAA3gIVwPXJhQcCzBACAMCgY8hmylPM\nSL5iAAAwsAn7Z4awsbFx2bJlxsbGurq6CxYsePTokYzCR44codFoWVlZ1MMHDx54eHjo6+tr\namouWbKkrq7ujUePgBAAAAYdWyO1Xk+f11RRsjHADYQAAINBH0SDcgSEK1asqK6uzsvLKykp\nYbPZCxYs6Orq6rbks2fPwsPDWaz/+1+Jx+PNnz+fz+dfuXLl+vXrjY2NS5YseePBY8koAAAM\nOpoqdNeRnOyKJhllFn+oxcC3pgAAg4FQKBQK+uKyMjIfP36ck5Nz69atsWPHEkL27Nmjr69/\n6dKlOXPmSBdeu3atn5/fsWPHqIdlZWWVlZWXLl0yNjYmhBw+fNjMzKyiomL06NFv0E38XwcA\nAIORxxitsUN7nAB0sNBwsWK/z/4AAED/6YcZwtLSUlVVVSoaJIRoa2vb2Nj84x//kC6ZmZl5\n69at6OhoUQqPxyOEiCYMhw4dymQyS0tL32zwCAgBAGAwYtBpn88yWDRaS1npf9aOqinT/cbr\nBE/V62VFKQAA/FkoMZQ4bHXxf0pK9NeN/5SVmRIXodFk/U9SV1eno6MjXkZPT+/58+cSxRob\nG9etW5eUlKSuri5KHDdu3JAhQ7Zu3drR0dHR0fH1118TQl68ePFmw8eSUQAAGKSU6DSvsdr/\nnw27/I/2py18Oo0YsZkfDmWpMvFtKQDAIPKBmXFkSKB4yt+PZDx6/MdrXWTGpLFzZ9mLp7BU\nlWVXkY4YpVNCQ0Pnzp3r7OwsnqipqXny5MmgoCANDQ01NbUNGzaYm5szmW+4ERoCQgAAGNTU\nlZXszdV7LwcAAH9Svz6o/u5Q+ltepPDnfxT+/D8LPjP2fyOjvIGBQX19vVAoFAWBz58/NzAw\n+J9rFhb+9NNP//rXv6Srz5w5s7KysqmpSU1NjRASFxdnZmb2Zj3Hl6AAAAAAADCICfuGzDYn\nTpzI4/H++c9/Ug/r6+vv3bs3bdo08TKHDh1qamqysrIaMmTIkCFDnj9/HhAQ4OHh0dnZeeLE\nidraWi0tLWVl5YKCAoFAMH369DcbPQJCAAAAAAAY5IR98E8WIyOjxYsXr1mz5s6dO7///ntA\nQMD48eMdHBwIIQcPHkxMTCSE7Nmzp7Kysuw/hgwZsmvXrqSkJAaDERcX9//+3/978uTJlStX\n1qxZExQUNGTIkDcbOQJCAAAAAAAYvIRCoVDw7v/1GhMeOnRozJgx8+fPnzZtmqqqanZ2NrV8\ntLCwMCcnhxCio6NjIoZOp+vq6lKBX3p6elNTk7W1tYeHh5eXFxVAvhncQwgAAAAAAIPXSEuL\nj5zfcL2lDHq62rILsNnsI0eOSKenpaV1W762tlb0s6Wl5cWLF9+id/+FgBAAAAAAAAYvzwVO\nnguc+rsX/QZLRgEAAAAAAAYpBIQAAAAAAACDFAJCAAAAAACAQQoBIQAAAAAAwCCFgBAAAAAA\nAGCQQkAIAAAAAAAwSCEgBAAAAAAAGKQQEAIAAAAAAAxSCAgBAAAAAAAGKQSEAAAAAAAAgxQC\nQgAAAAAAgEEKASEAAAAAAMAghYAQAAAAAABgkEJACAAAAAAAMEghIAQAAAAAABikEBACAAAA\nAAAMUggIAQAAAAAABikEhAAAAAAAAIMUAkIAAAAAAIBBCgEhAAAAAADAIIWAEBRUQkKCXs8e\nP34sKuPs7CwQCCSqu7m5zZkzR/SwoaFh586djo6OVlZWQ4cOtba29vDwyM3NlWjOysqqo6ND\nujNbtmzR09NzcHCgHoaHh+vp6VVVVUmXdHBwGD9+fLftGhoa6unpzZs3T7xdSl1dXWxsrKOj\n4/Dhw4cOHTpy5MjAwMBLly5JFPvll1+WLVs2YsQIExOTefPmnTt3TvFz79y54+3tbWFhYWFh\n4eTklJKSIv5inT179qOPPrK2tjY2Np4yZUpcXFx7e7ucdd8mV8JrFZYgMYS//e1vPB5PzroA\nAAAA/Y7R3x0AkGX9+vVmZmbS6dra2qKfy8rKDh06FBQU1NNFGhsb58yZU19f7+vra29vz2Aw\nHj58mJaWFhgYuHPnzoCAAKoYnU5vaWnJz893c3MTr87n80+dOsVkMl+389LtPnv27PDhwxLt\nFhcXL1++nMvlzps3b/HixQwGo7q6OisrKzc3NyQkJCoqiipWVVW1YMECfX39yMhIJpOZnZ3t\n7+9/9OjR+fPnK2xuaWnpwoULjY2NN23apK6unpOTExoaWldXFxoaSghJSUkJDQ318PD47LPP\nlJWVL1++vG3btl9//TUpKanXum+TK+G1CkuQHsKOHTvu3bt38ODB1323AAAAAPQLmlAo7O8+\nvIbm5mY+n99frbNYLHV19ZaWlm4nkYAQoqWl1dLSIv/sigwJCQnbtm3Lz8+3s7OTUSY+Pt7J\nyen69evXr1/X19cXZbm5uXG53PPnzxNC9u/fHxUVlZycLB7ptba2zpo1q62traysTFlZOSEh\nIS4ubvLkyWpqaidOnBBvJS8vb8WKFePHj+dyuVeuXCGEhIeHHzx48MaNGxYWFhJdcnBw4HK5\nt27dkm6XRqPp6uo2NDSMHz9e1O7Tp09nzJjBZDLT09PHjBkj3r2AgIArV64cO3bMxcWFELJm\nzZr8/PybN28aGBgQQjo7O+fMmcPlcm/evKmwuW5ubhUVFSUlJXp6eoQQgUAwe/bsqqqq+/fv\nM5lMR0fH1tbWGzdu0Gg0atQrV67My8urqqpisViy675NrsRL9lqFJUgPYcWKFT/99BM1BNl1\n34CGhoaqqmpTU1NnZ+c7v/ifg46OTkNDQ3/3QkExGAwtLa329vbW1tb+7ouCUlNTEwgE4usU\nQByHw2EymS9evBhYHx3fGzqdzmazm5qa+rsjCkpZWZnNZnO53FevXr391YYMGfL2FwERLBmF\nga2zszMmJobP50dGRvZU5tmzZ4SQDz/8UDxRQ0MjLy/v9u3bysrKVIpQKHRxcSkqKnr69Kl4\nyRMnTowbN47D4bxu37ptV1NTU7zdvXv3Njc3x8fHi0eDVPf27du3detWGxsbQkhXV1d+fv7c\nuXOpuIsQwmAwli5d+ujRo/LycsXMJYR4eXlt376dCrQIIXQ6fdKkSW1tbS9fviSECAQCFosl\nCqWoJ4cQQqXIrvs2uRJeq7AE2UMAAAAAUHwICGHAMzIyCg0NPX36dFFRUbcFqJAsKipKYupA\nX19fRUVFPGXhwoU0Gi09PV2U0tDQUFhY6OHh8QZfiMrT7rlz53R1dV1dXaWrGxgYrF+/3tTU\nlBBSVVX16tWr0aNHixcYNWoUIaSiokIxcwkhfn5+np6e4rlVVVV6eno6OjqEkE8++eTevXu7\nd++ura1ta2s7e/ZsVlbWqlWrVFVVe637NrkSXquwBOkhnDlzJjAwkBoCAAAAgOLDPYSg0Jqa\nmp4/fy6RqKampqGhIXooFArXrl2bnp4eFhZ25coV0YyfiJubW0FBwcmTJ8eMGTN9+vQpU6bY\n29vb2dkxGJLvf0NDwxkzZhw/fvzTTz+lUk6ePCkUCj08PKjVp69Fut25c+eKr4Dt7Ox89OjR\njBkz6PRevpqpr68nUgskqEmturo6xcyVHsWZM2eKioq++uor6qGPj4+Kispnn30WGxtLCKHT\n6Rs3bvzyyy+7XUwiUfcd5r5NYekhbNiwISIiQp66AAAAAIoAASEoNF9fX+nEjz/+OC4uTjyF\nyWTu2LHD3d199+7dmzdvlihPp9P37dvn5+d34sSJoqKiixcvEkI4HI6Pj09YWBi1xk+8xdWr\nV5eUlNjb2xNCUlNTnZycdHV136Dz0u3GxsZqaWl5e3tT7ba1tRFCxINbQkhra2tjY6PoIY1G\nMzExoe5pkbiljZpmbG9vV8xciWejsLBw3bp18+fPDw4OplKuX7/++eefT5kyZdmyZWpqaufP\nn09ISFBVVQ0JCem17rvKfZvC3Q7h22+/VVFR2bhxozzVAQAAAPodAkJQaDExMZaWlhKJJiYm\n0iWnT5/u6emZmJjo6ek5fPjwbgtMnz6dEFJdXX3t2rWMjIykpKTi4uLCwkLxqUJXV1cOh3Ps\n2DF7e/t79+5VVFS85Yd7Ubv//ve/79y5k5KSImpXQ0ODTqdLLChNS0sTn2Jis9kPHjygdiiR\n2M2ICrpYLJZi5oonHjx4cMuWLW5ubt999x01HSoQCNatW2dubn78+HHqjjtHR0cGgxEbG/vR\nRx+J79YjXVf2leXPfZvCPQ2BRqPt2LFj8eLF0hsOAQAAACgg3EMICm3ChAmzpVhZWXVbODo6\nWlVVNSwsjMjc1cPc3NzX1zczM3PVqlUVFRUSdx6qqKgsWrTozJkzXC43NTVVS0uL2uRTogzp\nbhKMENLa2qqurt5Tu4GBgefOnRO1S6fTra2ty8vLxU+uc3FxOfofU6dOpRKpDVSpJZoitbW1\nhBADAwPFzBWlREZGhoeHb9iwYf/+/aIFvTU1Nf/+97+dnJzEXylHR0eBQFBaWiq77jvJfZvC\nMoYwc+ZMiSEAAAAAKDIEhPDnoaenFxERUVRUlJmZKVrHyOPx0tLScnJypMvPmjWLEFJTUyOR\n7uPj09bWduHChczMTHd3d+nwYNiwYYSQu3fvSqTX1NTU1NRQU5pytksdj5GSkiLKNTMzc/0P\nIyMjKtHc3FxTU/POnTvi17l9+zYhZOzYsYqZSz2MjY09cOBAQkKUNWXOAAAgAElEQVRCRESE\neOBEbdIjcYoMFRiLwuOe6r597tsUfq0hAAAAACg4BITwp7Jy5cqxY8dGRUWJDmpTVlZOTEzc\nuHHj/fv3xUsKBIKMjAxCiMQmmYQQOzs7S0vLxMTEZ8+eeXl5Sbfi6urKYrG2b98ufrMfj8eL\niIgQCoXLli2Tv92goCBjY+Po6GjpTWsePnxYXl5OrV2k0+lubm6FhYVPnjyhcjs6OlJTU0eO\nHGltba2YuYSQoqKi3bt3x8TE+Pv7S4zOxMSEw+FcuHBB/NTKS5cuEUImTJggu+5b5r5N4V6H\ncPnyZdEQAAAAABQf7iEEhZaTk1NWViadbmdnZ2trK51Op9Pj4+PnzZtXV1dHnfpAo9ESEhKW\nLl3q5OS0ePFiW1tbJpNZW1ubm5tbXl7u7+/f7Wd3b2/vmJiYESNGiG8KKmJgYBAfHx8SEmJv\nb+/l5WViYlJbW5uVlfXkyZPg4ODZs2f31G5LS8upU6fKyspE7XI4nNTUVB8fH19f35kzZ06b\nNk1TU7O+vr60tPTq1au6uroHDhygGt20aVNeXt6iRYv8/f3V1NROnz79+PFjKrZUzNzOzs7w\n8HBtbW0Wi/Xjjz+KP4EzZ840NTUNDw+PiIjw8fHx8/NTU1O7dOnS4cOHPT09bWxsZNcdOnTo\nG+eampr+9NNPK1as+Prrr1etWtVrJ6VffRE6nS49hB9++GHRokXU6ZEAAAAAio/2Bqer9aPm\n5maJBVrvE4vFUldXb2lpkdhFA0S0tLRaWlrEJ0zeWEJCwrZt23rKjYiICA0Npco8efJE4jjB\nzZs3Hz58eOzYsaJpt8rKyr179xYXFz99+rSrq0tbW9vW1nbp0qULFiwQb050qT/++MPW1jY8\nPPyzzz6jCnh5ef3xxx9XrlwRtXLr1q09e/aUlJQ0NDSw2WxbW9uVK1fOnTtXvCcS7erq6k6Y\nMGHJkiWidikvX75MTk7Oz89/8OBBe3u7tra2jY3N3Llz/fz8xHdnuX//fnR09NWrV/l8vq2t\nbVhYGLVdjWLmNjQ0UPOE0o4ePUodvXjmzJn9+/f/61//6uzsHDZsmK+vb2hoaEdHh+y69vb2\nb5zr6uqal5e3fPnymJiYNWvWyNNJ2SSGsGTJknXr1kmfaPJOaGhoqKqqNjU1iSbAQYKOjo7E\nLk0gwmAwtLS02tvbW1tb+7svCkpNTU0gEHR7fzgQQjgcDpPJfPHixcD66Pje0Ol0Npvd1NTU\n3x1RUMrKymw2m8vldnu41OuSOPIK3hICwteAgLBX7zAg/POh0Wi6urp8Pr+5ubm/+6KgVFRU\nlJSUqNM4QBoCwl4hIJQBAWGvEBDKhoBQNgSEsiEgVGR9u2S0tbX1wIEDd+/e5fP51tbWwcHB\n1LaEEvLy8k6fPv3ixQtjY+OAgICJEyf2aa8AAAAAAACA9PWmMrt3737+/PmXX34ZHx+vpqYW\nHR0tPXd04cKFEydOrFmzZv/+/XPmzPn+++8xPwAAAAAAAPAe9GFAWF9ff/PmzdWrV1tYWBgZ\nGQUHB9fU1JSXl0sUO3HixPLly+3s7PT19d3c3A4cOKCmptZ3vQIAkMfFixf1ZEpKSurvPgIA\nAAC8rT5cMlpZWclkMi0sLKiHGhoaJiYmv/32m+iAMkLIixcvqGOsQ0JC/vjjD3Nz86CgoL/8\n5S991yuAQUsoFF4s/ufZi9fuP3rc2dllZDDEceoED1dHVRW5zmEfbCZPnvzzzz/LKGBgYPDe\nOgMAAADQR/owIGxpadHU1BQ/5ZnD4Uhsp/HixQtCyPnz5zdv3szhcNLS0r766qv9+/dzOByq\nQGlpaWJioqj8pk2bRo4c2Xd9lo06EU5dXR1zmD1RUlJis9n93QuFRu3r8P7brW9oWrXpm+v/\n/O8U/cN/P7168+6hE7nfx28ZP0YhvoWh0Wg0Gk1ZWSECVC0tLWNj4/7uxf+g/gRpampiR4ee\n0On0fvn9GhCo/46VlZXxFPWE+hVTVVXt744oKCUlJUKI6BMaSFNSUsLvV0+oP0EsFktiZ3hQ\nBH27qYx4NCiDt7e3iYkJIWTlypWXLl0qLS11cnKisl6+fHnv3j1Ryfb29j7az11+1B9E6Em/\nv0AKjkajvf+nqJXbtmRN+L3KR9JZNbV1nqvD835MHG094j33qifUZzLoCf4EyYY/QbLR6XT8\nismG50c2/IrJhudHNvwJUkx9+K6lTiAQCoWisLC5uVlbW1u8jI6ODiFEXV2deqikpKSjo9PY\n2Cgq4OjoWFpaKnrY3NxcX1/fd32WDcdO9ArHTsjQj8dOxO35odtokNL2qj047JvMA3F0ulzf\n4PQdHDshG46d6BWOnZABx070CsdOyIZjJ2TDsROy4dgJRdaHMbqlpSWfz3/w4AH1sKWl5fHj\nxzY2NuJldHR0tLW1f/31V+phR0dHXV0d7swBeIfaeR1pZ87LLvPr/eqSWxXvpz8AAAAAoDj6\nMCDU0dGZMmXKnj17qqqqampqdu3aNWLECOoOwMLCwpycHEIInU7/6KOP0tLSysrK6uvrk5KS\nVFVVcQ4hwDtU9q/f23m9z2n/o+xf76EzAAAAAKBQ+nYVb0hIiLm5+V//+tewsDBlZeXIyEhq\n+WhZWdmNGzeoMosXL3ZxcUlISFi9evXTp09jYmJwPzfAO1TfINcK1ef1jb0XAgCAvpeQkCDj\nzJvHjx+Lyjg7O0vfpuHm5jZnzhzRw4aGhp07dzo6OlpZWQ0dOtTa2trDwyM3N1eiOSsrq27v\niNmyZYuenp6DgwP1MCwsTE9Pr6qqSrqkg4PD+PHj5W+XUldXFxsb6+joOHz48KFDh44cOTIw\nMPDSpUsSxX755Zdly5aNGDHCxMRk3rx5586dU7RcFxcX8dHt2LFD+rXz8PAQFejo6NixY8f4\n8eONjY0nTpyYmJgovhb3zp073t7eFhYWFhYWTk5OKSkp4i/09evXFy5caG5ubm5u7ubmJvpQ\nLc+VpT148MDZ2VlPT6+4uFiU2Nzc3NM7MC8vT8bVeho+h8MRf1uCQunbO1/V1NQ+/fRT6fRN\nmzaJfqbT6QEBAQEBAX3aE4BBS1NDrk1x2Rrqfd0TAACQ3/r1683MzKTTxbdjKCsrO3ToUFBQ\nUE8XaWxsnDNnTn19va+vr729PYPBePjwYVpaWmBg4M6dO0Wfvuh0ektLS35+vpubm3h1Pp9/\n6tQpJpP5up2Xs93i4uLly5dzudx58+YtXryYwWBUV1dnZWXl5uaGhIRERUVRxaqqqhYsWKCv\nrx8ZGclkMrOzs/39/Y8ePTp//nyFyl28eLEot6WlhU6n79y5U/xpGTp0qOjn4ODgvLy81atX\n29raXrx4MSYmhsfjbd68mRBSWlq6cOFCY2PjTZs2qaur5+TkhIaG1tXVhYaGEkIKCgr8/PxG\njhz55Zdfqqqq7tmzx93d/ezZs7a2tr1eWdqRI0e2bt1KbeohjsVi7dq1SyLx8uXL2dnZw4YN\n6/UNID18JSWlDz74oNeK0C9oA+vO4ObmZj6f31+tY1OZXmFTGRn6a1OZhqaW6YvXCAS9/KYn\nfLnB1XHK++lST7CpjGzYVKZX2FRGBmwq0yvF2VQmISFh27Zt+fn5dnZ2MsrEx8c7OTldv379\n+vXr+vr6oiw3Nzcul3v+/HlCyP79+6OiopKTk8UjvdbW1lmzZrW1tZWVlSkrKyckJMTFxU2e\nPFlNTe3EiRPireTl5a1YsWL8+PFcLvfu3btMJjMoKOjgwYM3btwQHTQt4uDgwOVyb926JWe7\nT58+nTFjBpPJTE9PHzNmjHixgICAK1euHDt2zMXFhRCyZs2a/Pz8mzdvUttMdHZ2zpkzh8vl\n3rx5U3FyBQKBs7NzS0sLlRsSEnL27FnRVhoSioqKlixZEh0d/cknn1ApgYGBjY2Np0+fptFo\nbm5uFRUVJSUlenp61JVnz55dVVV1//59JpM5efLkV69elZSUUKegNTc3T5kyxcbG5tSpU71e\nWaIbN2/edHd3/+qrr9TV1devX5+VlTVt2rRuO0y9LlOnTnV2dpaIcrslPXxsKqPIsPErwJ+c\njhZ79rQeP1JQhuhoOU4ZL7sMAAAolM7OzpiYGD6fHxkZ2VOZZ8+eEUI+/PBD8UQNDY28vLzb\nt2+Lzn0VCoUuLi5FRUVPnz4VL3nixIlx48a9wdmD8rS7d+/e5ubm+Ph48WiQKrZv376tW7dS\nOxF2dXXl5+fPnTtXtOkgg8FYunTpo0ePysvLFSp3xYoVVC4h5OXLl5qamj09P+np6RoaGh9/\n/LEo5fDhw1lZWVTM5uXltX37dioaJITQ6fRJkya1tbW9fPmyvr7+4cOHc+fOFZ2JzeFwlixZ\ncvXqVWqXftlXljBkyJCCggLxwjLExsbyeDwZbzZxsocPigYBIcCf3+ZgP46mhowCW9avYKni\noFgAgAHGyMgoNDT09OnTRUVF3RagQrKoqCiJyXN9fX2J88EXLlxIo9HS09NFKQ0NDYWFhR4e\nHm+wmkyeds+dO6erq+vq6ipd3cDAYP369aampoSQqqqqV69ejR49WrzAqFGjCCEVFRUKlUuN\nuqKighDS0tIiioikp51v3LgxceJEKjCWXlfl5+fn6ekpnlJVVaWnp6ejo0MtUmOxWOK5xsbG\nAoHgt99+6/XKEiwsLKjtHntVWVl56NChsLAwiQPkeiJ7+KBoEBAC/PmZGRvuj9uso8WWzlJm\nMiJDVsx3tH//vQIAABmampqeS5FY8SsUCteuXWtpaRkWFtbt/Sxubm6enp7nzp0bM2aMt7f3\n7t27S0pKul12bmhoOGPGjOPHj4tSTp48KRQKxfdBkV+v7XZ2dj569GjUqFG9HlNOHUAtsUSQ\nmj2rq6tTqFxqtrCuro4Q0tLSwuPxgoODraysTE1Nra2t4+LieDweIUQoFD5+/NjU1DQlJWXS\npEmGhoZWVlZ//etfe1pLeebMmaKionXr1hFCDA0NtbS0rl27Jl6AOrK7vr7+da8sv/j4eAMD\ng2XLlslZXnr4sbGxiAwVVt9uKgMACmLcKKvcI39L+jEr79K1uhdNhBCWqsqMybafBCz+ywjz\n/u4dAABI8vX1lU78+OOP4+LixFOYTOaOHTvc3d13794tvXEInU7ft2+fn5/fiRMnioqKLl68\nSAjhcDg+Pj5hYWESi/p8fX1Xr15dUlJib29PCElNTXVyctLV1X2DzvfaLnW7uIbG/6xeaW1t\npdY9Umg0momJCRVFSGxsQ00ztre3K2YuIaS5ubm6unrKlCm7du3q6urKzc3duXPnr7/+euTI\nkVevXgkEgqKiooqKii+++ILD4Vy6dGnfvn3V1dWHDx+WeCYLCwvXrVs3f/784OBg6oldu3Zt\nbGxseHh4cHCwkpJSSkrK1atXCSGdnZ2vdWX5VVZWZmdnx8bGitYY90p6+Dt27KisrExJSXnj\nbkDfQUAIMFjoaLEj1gVErAtobH7ZwecP0dFS6u17WQAA6C8xMTGWlpYSiSYmJtIlp0+f7unp\nmZiY6OnpOXz48G4LTJ8+nRBSXV197dq1jIyMpKSk4uLiwsJCBuO/HwVdXV05HM6xY8fs7e3v\n3btXUVGxcePGtxmCjHY1NDTodLrEgtK0tLSIiAjRQzab/eDBA2p5pMT8JxV0sVgsxcwlhGRl\nZTEYDNFmPwsXLlRVVU1NTS0pKRk3bhwhpLW19fLly1RI7OjoyOfzk5OTy8rKRJuFEkIOHjy4\nZcsWNze37777TjSVunbt2oaGhqSkpIMHD1J1IyMjN2zYoK6urqSkJOeVX8uRI0cYDIaPj4/8\nVaSHr66u/uOPP167do0aPigUfBwEGHS0OZoGQ3QQDQIAKLIJEybMlmJlZdVt4ejoaFVV1bCw\nMEJIt9uHUMzNzX19fTMzM1etWlVRUSFx56GKisqiRYvOnDnD5XJTU1O1tLSoTT4lypAe7gpr\nbW1VV+/+BCPpdul0urW1dXl5ObWKkuLi4nL0P6ZOnUolUkEFtURTpLa2lhBiYGCgULnUljzU\nwlEjIyPxrV8JIYsWLSKE3LlzR0VFRVNT08bGRnyC1NnZmfzn/kNKZGRkeHj4hg0b9u/fLz41\nx2Qyo6Oj7927V1BQUF5enp6ezuVyCSFmZmZyXvm1CIXCs2fPzpgxQ2I6Vzbp4bu7uxNCbt++\n/WbdgD6FT4QAAAAAA5uenl5ERERRUVFmZqZoHSOPx0tLS8vJyZEuP2vWLEJITU2NRLqPj09b\nW9uFCxcyMzPd3d2llwhSp03cvXtXIr2mpqampoaa0pSzXep4DPE1hGZmZq7/YWRkRCWam5tr\namreuXNH/DpUXDF27FiFyqXu5Rs7diwh5OXLly9fvhTPpe7/pOYPR48eTW3EKkIFxqInPDY2\n9sCBAwkJCREREd1G+Do6OuPGjTM0NCSEXL58WVtbe8SIEfJc+XXdunWrpqam271/ZJA9fFA0\nCAgBAAAABryVK1eOHTs2KipKtHeLsrJyYmLixo0b79+/L15SIBBkZGQQQiQ2ySSE2NnZWVpa\nJiYmPnv2zMvLS7oVV1dXFou1fft28Zv9eDxeRESEUCikNh2Rs92goCBjY+Po6GjqyERxDx8+\nLC8vpxZJ0ul0Nze3wsLCJ0+eULkdHR2pqakjR460trZWnFwej3fkyBEqt7a29oMPPqC2gRE5\nfvw4jUajDvpzd3e/f//+5cuXRblnzpxRUlKaNGkSIaSoqGj37t0xMTH+/v7SL0FISMikSZNE\nq1XLy8vPnz/v4eFBrf6VfeU3QG1gM2HCBPmrdDv8H3/8kUajzZgx4826AX0K9xACAAAAKJyc\nnJyysjLpdDs7u25vBqPT6fHx8fPmzaurq6POP6DRaAkJCUuXLnVyclq8eLGtrS2Tyaytrc3N\nzS0vL/f39+/2U763t3dMTMyIESPs7Lo5w9bAwCA+Pj4kJMTe3t7Ly8vExKS2tjYrK+vJkyfB\nwcGzZ8+Wv10Oh5Oamurj4+Pr6ztz5sxp06ZpamrW19eXlpZevXpVV1f3wIEDVKObNm3Ky8tb\ntGiRv7+/mpra6dOnHz9+TMWWipOblZVVXV1N5RoaGq5cuTI5OXnp0qVz587l8/lnzpy5fv36\nqlWrqHk8Pz+/Y8eOBQQEfPLJJ+bm5hcuXMjOzl61atWwYcM6OzvDw8O1tbVZLNaPP/4o/uTP\nnDnT1NR0/vz5aWlpXl5ePj4+9fX13333nZGR0eeff06VkXFlQshPP/20YsWKr7/+etWqVYSQ\nGzdu/P7779QPhJDCwsKqqipCiIODg7n5/204V1lZSQgRPZRHT8MPCQn54IMP3snB9PBu0d7g\nbJl+1NzczOfz+6t1Foulrq7e0tLS7c7OQAjR0tJqaWnp9dybwYlGo+nq6vL5/Obm5v7ui4JS\nUVFRUlKitp4DaRoaGqqqqk1NTd1uGQ+EEB0dHYk9KkCEwWBoaWm1t7dLnFsAImpqagKBQBF2\nxk9ISNi2bVtPuREREaGhoVSZJ0+eSBwnuHnz5sOHD48dO1Y07VZZWbl3797i4uKnT592dXVp\na2vb2touXbp0wYIF4s2JLvXHH3/Y2tqGh4d/9tlnVAEvL68//vjj7t27TCbzxYsXQqHw1q1b\ne/bsKSkpaWhoYLPZtra2K1eunDt3rnhPem2X8vLly+Tk5Pz8/AcPHrS3t2tra9vY2MydO9fP\nz098eeH9+/ejo6OvXr3K5/NtbW3DwsKo7WoUJ3fcuHHR0dGiWL2rq+uHH344fvz4w4cPOzo6\nbGxsli9f7ufnJ6rb3Nz8zTff5ObmNjY2mpmZrVq1auXKlTQaraGhwdrautuX/ujRo9TSzdOn\nT3/77bf3799XU1NzdnaOjIyk1o7KvjIhJC8vb/ny5TExMWvWrCGEfP7550ePHpVu6MCBA9Qt\nf4QQf3//goICiWWovZIe/sqVK9etW8flct9JQChx4Ae8JQSErwEBYa8QEMqAgLBXCAhlQ0DY\nKwSEMiAg7JXiBISKicPhiALC/u6LIqLT6Ww2u6mpqb87oqCUlZXZbDYCQsWEewgBAAAAAAAG\nKQSEAAAAAAAAgxQ2lQEAAACAQedVJ62hgy4QCnVVBGr4RCy3ixcvent7yyggukcRBgq8/QEA\nAABgEHnMpef8W/nXZkaXgBBC6DRiye76yKzDQrOrv7s2AEyePPnnn3+WUcDAwOC9dQbeCQSE\nAAAAADBYlDxnpj5Q7hT+97T3LiH5rVnpt3KWpwXPcWi/bV44UKirq9vY2PR3L+Bdwj2EAAAA\nADAo/NaslHJfRTwaJISIHpysUil7ofT+ewXQvxAQAgAAAMCfn5CQjCoV2WVOPVLtxOFZMMgg\nIAQAAACAP79/t9L/aOvlo28Dj1bZgjuqYHBBQAgAAPDuJSQk6PXs8ePHojLOzs4CgeSUhJub\n25w5c0QPGxoadu7c6ejoaGVlNXToUGtraw8Pj9zcXInmrKysOjo6pDuzZcsWbW3t0aNHUw/D\nw8P19PSqqqqkSzo4OIwfP17+dil1dXWxsbGOjo7Dhw8fOnToyJEjAwMDL126JFHsl19+WbZs\n2YgRI0xMTObNm3fu3DmFytXV1XV0dJTIFamqqjI1NbW2thZPvHPnjre3t4WFhYWFhZOTU0pK\nivhL2dHRsWPHjvHjxxsbG0+cODExMZE60r25ubmnN0ZeXp6o+oMHD5ydnfX09IqLi7vtkjgZ\nhWV3UjbxupMnT/7+++/lGaDCesyVaznov1tpvRcC+BPBVyAAAAB9Zf369WZmZtLp2traop/L\nysoOHToUFBTU00UaGxvnzJlTX1/v6+trb2/PYDAePnyYlpYWGBi4c+fOgIAAqhidTm9pacnP\nz3dzcxOvzufzT506xWQyX7fzcrZbXFy8fPlyLpc7b968xYsXMxiM6urqrKys3NzckJCQqKgo\nqlhVVdWCBQv09fUjIyOZTGZ2dra/v//Ro0fnz5+vILnq6uqnTp0SzxURCoWfffZZe3u7mpqa\nKLG0tHThwoXGxsabNm1SV1fPyckJDQ2tq6sLDQ2lCgQHB+fl5a1evdrW1vbixYsxMTE8Hm/z\n5s0sFmvXrl0Sz/bly5ezs7OHDRtGPTxy5MjWrVt1dHTkeaVkFO61kzJI1M3Pz1+9enVVVVWv\nA5Snz/2CJ98eorwuBIQwuNAU/LscCc3NzXx+v+3+xGKx1NXVW1pauv3+FQghWlpaLS0t8n/1\nOKjQaDRdXV0+n9/c3NzffVFQKioqSkpKbW1t/d0RBaWhoaGqqtrU1NTZ2dnffVFQOjo6DQ0N\n/d2L/5OQkLBt27b8/Hw7OzsZZeLj452cnK5fv379+nV9fX1RlpubG5fLPX/+PCFk//79UVFR\nycnJ4pFea2vrrFmz2traysrKlJWVExIS4uLiJk+erKamduLECfFW8vLyVqxYMWHChFevXpWW\nlra2toaHhx88ePDGjRsWFhYSXXJwcOByubdu3ZKz3adPn86YMYPJZKanp48ZM0a8WEBAwJUr\nV44dO+bi4kIIWbNmTX5+/s2bN6kt6Ts7O+fMmcPlcm/evKkguWpqah0dHdOnTxflivzwww8R\nERFTp069e/fub7/9JnqNKioqSkpK9PT0CCECgWD27NlVVVX3799nMplFRUVLliyJjo7+5JNP\nqPKBgYGNjY2nT5+m0STjjdbW1qlTpzo7O+/cuZMQcvPmTXd396+++kpdXX39+vVZWVnTpk3r\n6V0ku7DsTvZ0zW7rampqTp48ubKy8g0GqCBuvWAc/E2112I+w3kOhq/9aZNOp7PZ7Kampjfq\n2p+fsrIym83mcrmvXr16+6sNGTLk7S8CIlgyCgAA0G86OztjYmL4fH5kZGRPZZ49e0YI+fDD\nD8UTNTQ08vLybt++raysTKUIhUIXF5eioqKnT5+Klzxx4sS4ceM4HM7r9k2edvfu3dvc3Bwf\nHy8eDVLF9u3bt3XrVmp7+q6urvz8/Llz54oOKGMwGEuXLn306FF5ebli5orGUltb+9VXX23Y\nsMHY2Fh8jF5eXtu3b6eCJUIInU6fNGlSW1vby5cvCSHp6ekaGhoff/yxqPzhw4ezsrK6DZZi\nY2N5PJ7oPTBkyJCCggLxujLILiy7k7JJ1506deqbDVBBWHO6lOTonY02TiOEwQUBIQAAQH8y\nMjIKDQ09ffp0UVFRtwWokCwqKkpi/lNfX19F5X+2TFy4cCGNRktPTxelNDQ0FBYWenh4vEHH\n5Gn33Llzurq6rq6u0tUNDAzWr19vampKCKmqqnr16pXoJkbKqFGjCCEVFRWKmStK2bx5s6Gh\n4aeffioxQD8/P09PT/GUqqoqPT09aunmjRs3Jk6cSIXNshfOVFZWHjp0KCwsTLSQ2MLCYuTI\nkTKqiJNdWHYnZZOue//+/TcYoOJQZwinGvQy9TdhSOcQlYExHIB3BQEhAABAX2lqanoupbW1\nVbyMUChcu3atpaVlWFhYt7ckuLm5eXp6njt3bsyYMd7e3rt37y4pKel25bChoeGMGTOOHz8u\nSjl58qRQKHyzgLDXdjs7Ox89ejRq1Cg6vZePE/X19URqlRc19VRXV6eYudTD7Ozsn376adeu\nXaKZ2J6cOXOmqKho3bp1hBChUPj48WNTU9OUlJRJkyYZGhpaWVn99a9/7XaxXHx8vIGBwbJl\ny2Rf/50Q7+TrOnXqVGFh4fr168lrDlChLDLvMFLrMd7TVRF4D+e9z/4AKAJsKgMAANBXfH19\npRM//vjjuLg48RQmk7ljxw53d/fdu3dL78lBp9P37dvn5+d34sSJoqKiixcvEkI4HI6Pj09Y\nWJimpqZEi6tXry4pKbG3tyeEpKamOjk56erqvkHne22XuuNXQ0NDvFZra2tjY6PoIY1GMzEx\naW9vp4YpXpKaZmxvb1fMXEJIY2NjeHh4YGDgpEmTZD9XhYWF69atmz9/fnBwMCHk1atXAoGg\nqKiooqLiiy++4HA4ly5d2rdvX3V19eHDh8UrVlZWZmdnx3jo/sYAACAASURBVMbG9hpwvj2J\nTr5u3aCgIDc3t9cdoKJRVRJuGNWWcl+1olHyM7AlpyvQsl2dMZA21wB4JxAQAgAA9JWYmBhL\nS0uJRBMTE+mS06dP9/T0TExM9PT0HD58eLcFpk+fTgiprq6+du1aRkZGUlJScXFxYWEhg/Hf\n/81dXV05HM6xY8fs7e3v3btXUVGxcePGtxmCjHY1NDTodLrEgtK0tLSIiAjRQzab/eDBAxaL\nRQiRmP+kgi4Wi6WYuYSQqKgoJpMp4/ZOysGDB7ds2eLm5vbdd99Rk6VKSkqEkNbW1suXL1MB\ns6OjI5/PT05OLisrs7W1FdU9cuQIg8Hw8fGR3cTbk+7k69b19PT84YcfXr58KRQK5R+gAtJg\nkk9s2u+3KN2qV3reriQQEgOW4EOdThst3DoIgxQCQgAAgL4yYcIEGbuMSoiOji4oKAgLC8vI\nyJCxM4e5ubm5ubmvr+8XX3zx/fffFxUViZ9YqKKismjRooyMjLi4uNTUVC0tLWqTT3Hik2AS\nWltbJWb8ZLdrbW1dXl7O4/FEdxW6uLgYGRlRPyclJVE341EbqFJLNEVqa2sJIQYGBoqZe+nS\npfT09OTkZDqdzuVyCSFdXV2EEC6Xy2AwROONjIxMSkoKDQ0NDw8XvWoqKiqampo2NjbiT6az\ns3NycnJFRYUoXhIKhWfPnp0xY0ZPz/m70m0nX7futm3bRNOYcg5QkX3A7vqAjQgQgBDcQwgA\nAKAg9PT0IiIiioqKMjMzResYeTxeWlpaTk6OdPlZs2YRQmpqaiTSfXx82traLly4kJmZ6e7u\nLr0WkTrs7u7duxLpNTU1NTU11JSmnO1Sx2OkpKSIcs3MzFz/QxQZmpuba2pq3rlzR/w6t2/f\nJoSMHTtWMXPPnTsnFAo//vjjYf+RlpbW0NAwbNiwwMBAqnBsbOyBAwcSEhIiIiIkAq3Ro0dT\n27SK8Hg8Qoj4y3Hr1q2ampput+R5h2R08m3qyjNAABgQEBACAAAoipUrV44dOzYqKkq0d4uy\nsnJiYuLGjRvv378vXlIgEGRkZBBCJDbJJITY2dlZWlomJiY+e/bMy8tLuhVXV1cWi7V9+3bx\nm/14PF5ERIRQKKR2N5Gz3aCgIGNj4+joaOrIRHEPHz4sLy+nVifS6XQ3N7fCwsInT55QuR0d\nHampqSNHjrS2tlacXB6PJ8r95JNPcv+Xi4sLm83Ozc398ssvCSFFRUW7d++OiYnx9/eXfpLd\n3d3v379/+fJlUcqZM2eUlJTEb0e8du0aIWTChAnS1d8V2Z18m7ryDBAABgQsGQUAAOgrOTk5\nZWVl0ul2dnbdLquj0+nx8fHz5s2rq6ujTn2g0WgJCQlLly51cnJavHixra0tk8msra3Nzc0t\nLy/39/fvNpzw9vaOiYkZMWJEtwtWDQwM4uPjQ0JC7O3tvby8TExMamtrs7Kynjx5EhwcPHv2\nbPnb5XA4qampPj4+vr6+M2fOnDZtmqamZn19fWlp6dWrV3V1dQ8cOEA1umnTpry8vEWLFvn7\n+6upqZ0+ffrx48dUbKkguRwO5+TJk6Jcaoms+POmp6fHYDAmT55MCOns7AwPD9fW1maxWD/+\n+KN4sZkzZ5qamvr5+R07diwgIOCTTz4xNze/cOFCdnb2qlWrqOlZSmVlJdWQxAt048aN33//\nnfqBEFJYWFhVVUUIcXBwMDc3/+mnn1asWPH111+vWrVKdmFjY2PZnZR+b4hID5DFYikpKbW2\ntso/QAAYEBAQAgAA9JW9e/d2mx4REdHTfVbjxo1bvny5+FaNU6ZMKSgo2Lt3b3FxcUZGRldX\nl7a2tq2tbWho6IIFC7q9iJeX1zfffOPt7d1Tx7y9vS0tLffs2ZOZmdnQ0MBms21tbePi4ubO\nnfu67drY2Fy9ejU5OTk/P//vf/97e3u7tra2jY1NTEyMn58ftTsLIcTIyOjs2bPR0dGJiYl8\nPt/W1jY9PX3atGmKk9vZ2Tlu3DjxXBlaWloePHhACAkNDZXIOnr0qKmpqbKy8qlTp7755puU\nlJTGxkYzM7O4uLiVK1eKl2xsbKTT6erq6hJXSE9PP3r0qOjhnj17qB8OHDhgbm4uEAi6urpE\nR//JKKypqSm7k309QAAYEGhC4UDaXbe5uZnP7+VE0b7DYrHU1dVbWlq6PScKCCFaWlotLS0D\n5YDa94xGo+nq6vL5/Obm5v7ui4JSUVFRUlKiNrIHaRoaGqqqqk1NTd0eQAeEEB0dHYkdL0GE\nwWBoaWm1t7dLnIIIImpqagKBoNu9doAQwuFwmEzmixcvBtZHx/eGTqez2eympqb+7oiCUlZW\nZrPZXC73nRxWKXF8KLwl3EMIAAAAAAAwSCEgBAAAAAAAGKQQEAIAAAAMLhcvXtSTKSkpqb/7\nCADvCTaVAQAAgD+DytqWskcNDa08NRWlDwzYE4YPUWbgi+/uTZ48+eeff5ZRwMDA4L11BgD6\nFwJCAAAAGNhqm199m/9L+b8bxRN1NFSCZls5/AWBTTfU1dVtbGz6uxcAoBAQEAIAAMAAVtPQ\ntvnYzZZXkpuQN7Tydpwpb2jludmZ9UvHAAAGBCylAAAAgIFKIBBuy74rHQ2KJF/8/fc/cNgP\nAECPEBACAADAQHX1t+fVdb2crHi8+OH76QwAwECEgBAAAAAGqn/cr+u1zN3qxnZ+13voDADA\nQISAEAAAAAaq2qZXvZbhdwnqWtrfQ2cAAAYiBIQAAAAwUCnJ90GGIWc5AIDBB38fAQAAYKAy\n1dXotYwqU2mIpsp76AwAwECEgBAAAAAGqul/0e+1zFQrfSZmCAEAeoC/jwAAADBQjRumO95C\nV0YBVaaS7/Th760/AAADDgJCAAAAGMA2Lhg1TK/7haPKDPrmhWMMOaz33CUAgAEEASEAAAAM\nYGyWcvyyiQsnmDKUaOLpo0y1/rZs4sQRQ/qrYwAAAwKjvzsAAAAA8FZUmUqrnKyXOXxQ8bix\noZXHUlayGsox1MLEIABA7xAQAgAAwJ8BS1kJ84EAAK8LS0YBAAAAAAAGKQSEAAAAAAAAgxQC\nQgAAAAAAgEEKASEAAAAAAMAghYAQAAAAAABgkEJACAAAAAAAMEghIAQAAAAAABikEBACAAAA\nAAAMUggIAQAAAAAABikEhAAAAAAAAIMUAkIAAAAAAIBBCgEhAAAAAADAIIWAEAAAAAAAYJBC\nQAgAAAAAADBIISAEAHgNCQkJej17/PixqIyzs7NAIJCo7ubmNmfOHNHDhoaGnTt3Ojo6WllZ\nDR061Nra2sPDIzc3V6I5Kyurjo4O6c5s2bJFT0/PwcGBehgeHq6np1dVVSVd0sHBYfz48fK3\nS6mrq4uNjXV0dBw+fPjQoUNHjhwZGBh46dIliWK//PLLsmXLRowYYWJiMm/evHPnzil+7s8/\n/+zh4WFtbW1iYuLo6Hj06FGhUCjKvXPnjre3t4WFhYWFhZOTU0pKivhLKbtuR0fHjh07xo8f\nb2xsPHHixMTERPlzpT148MDZ2VlPT6+4uFg8fceOHdJvPw8PDxmXEnf27NmPPvrI2tra2Nh4\nypQpf/vb33g8npx15TFAf03s7OxGjRolf7uUP/GvicT7JCYmpr29XZR7/fr1hQsXmpubm5ub\nu7m53bhxg0pvbm7u6aXPy8uTp105f03kaUi2vv5FABgoGP3dAQCAgWf9+vVmZmbS6dra2qKf\ny8rKDh06FBQU1NNFGhsb58yZU19f7+vra29vz2AwHj58mJaWFhgYuHPnzoCAAKoYnU5vaWnJ\nz8/38/MTr87n80+dOsVkMl+383K2W1xcvHz5ci6XO2/evMWLFzMYjOrq6qysrNzc3JCQkKio\nKKpYVVXVggUL9PX1IyMjmUxmdna2v7//0aNH58+f//5z1dXVp0+f3mvdgoICf3//0aNHb9q0\nSUlJKTMz8/PPP6+vr9+4cSMhpLS0dOHChcbGxps2bVJXV8/JyQkNDa2rqwsNDe21LiEkODg4\nLy9v9erVtra2Fy9ejImJ4fF4mzdvlidXwpEjR7Zu3aqjoyOd1dLSQqfTd+7cKZ44dOhQed4A\nKSkpoaGhHh4en332mbKy8uXLl3fs2HHv3r2DBw/KU11+/fJr4ubmJl4dvyZvnCv9Pvn666/v\n3bu3b98+QkhBQYGfn9/IkSO//PJLVVXVPXv2uLu7nz171tbWlsVi7dq1S+L5vHz5cvb/396d\nB0RV9Y8fPzOsM+yggMrikpI75p6KCypKPiKuKC6FmpjbU5ZKab8el9y+rn1TM5eyFJceREVc\n0MS0IPVRC5+0XAlRFFSGfZ/fH7dnnvkOOIyiDDrv11/MPefM+czlnMt8uPeeu29f/fr1K+1X\nGDxNKu1Iv2qbCEDNJ9P/v8maRqVSFRcXG6t3hUJhY2OTlZVV4f8gIYRwdHTMysoq/+9eCCFk\nMpmLi0txcbFKpTJ2LDWUlZWVmZlZXl6esQPRZ+XKlYsXLz506FC7du301Fm+fLm/v39CQkJC\nQoKrq6umKCgoKDc399ixY0KIDRs2zJs3b9OmTdpfYXNycnr06JGXl3fx4kVLS8uVK1cuWbKk\nY8eOSqXy4MGD1tbWmZmZJSUlQojY2Ng333zztddey83NPXXqlBBizpw5mzdvPnPmTIMGDXRC\n6tatW25u7vnz5w3s986dO35+fhYWFrt3727ZsqV2tbFjx546dWr79u19+/YVQkyaNOnQoUNn\nz551c3MTQpSUlPTu3Ts3N/fs2bPVX1pQUJCYmFhp2/bt26vV6tOnT1tbWwshiouL/fz8VCrV\nb7/9Jv2OLl26lJiYWLt2bSFEWVlZr169bt68ee3aNQsLC/1t4+Pjhw0bNn/+/MmTJ0t77K23\n3nr06NHevXtlMpn+Up1f2dmzZ4ODg//xj3/Y2NhMmzYtOjq6S5cumtLp06cfPHjw+vXrjxuE\n5Zmbmzs6OhYUFLRv3z4nJ+fMmTOaTt98883Dhw/fvHlToVAY/oZ6GHGa7Nq1S7uXJ5omfn5+\nubm5//rXvwzs9wWdJgaW9uzZU2ecTJgwISYm5tatW9bW1h07dszPz09MTFQqlUIIlUrVuXPn\npk2b/vOf/yz/u87JyXn99df79Okj/QtDf79PNE30d6Rf+Q9YxYkgl8vt7e0zMzOfoq0psLS0\ntLe3z83Nzc/Pr/q71apVq+pvAg0uGQWAZ6+kpGThwoXFxcVz5859XJ179+4JIVq1aqW90dbW\nNjY29sKFC5aWltIWtVrdt2/f+Pj41NRU7Zq7du1q06aNg4PDk8ZmSL/r1q1TqVTLly/X/por\nVVu/fv3HH3/ctGlTIURpaemhQ4cCAgKkL3ZCCHNz81GjRt26dSspKan6S69fv15p25KSkgkT\nJixcuFDK6IQQUpqXnp4uXQ43fPjwpUuXStmgEEIul3fo0CEvLy87O7vStrt377a1tR0/frxm\nj23dujU6Olr6xqm/VEetWrWOHj2qXVlbdna2nZ1dhUWVKisrUygU2p1Kb1Xpt+1n7nlMkzt3\n7mjXZJo8XamoaJzY29sLIWQyWUZGxo0bNwICAqRsUAjh4OAwbNiw06dPP3r0qPzOXLRoUWFh\nofRbrrTfJ5omejqqVM2ZCIDRkRACwHNRt27d9957b+/evfHx8RVWkL5rzps37+HDh9rbXV1d\nraystLcMHDhQJpNt375ds+Xhw4dxcXFDhgx5iqs8DOn3yJEjLi4ugYGB5Zu7ublNmzbN09NT\nCHHz5s38/PwWLVpoV5Duwrp06VLNLDU3N580aVK/fv00RWq1+vr16x4eHlKaFxoaOnToUO22\nN2/erF27trOzc6Vtz5w50759eylbKH+thP5SHQ0aNGjWrNnjSrOysjQJofZtXYaYPHny5cuX\nV69enZaWlpeXd/Dgwf3797/11luaLLc6PfNpsnv3bs0WpslTl4qKxklUVNTkyZOtra2l66R0\nTqPVq1evrKzs999/19kVV69e3bJly+zZs6VLhSvt94mmiZ6OKlWjJgJgXCSEAPDEMjMz75eT\nk5OjXUetVk+ZMqVx48azZ8+u8DrzoKCgoUOHHjlypGXLliNGjFi9enViYqJ0OagOd3d3Pz+/\nbdu2abZ89913arXa8EVEnqjfkpKSW7duNW/eXC6v5G9ERkaGKHfpjnRuLT09vWaWarYUFRWl\npqaeO3du8uTJFy5cWLBgQYWfcf/+/fHx8VOnTtXeWGFbtVqdkpLi6en5zTffdOjQwd3dvUmT\nJp988ol0fZT+0ieVlZVVWFgYHh7epEkTT09PHx+fJUuWGLgeRkhIyMaNG1evXt2yZUtvb++w\nsLBJkyZ9+umnTxGGfkaZJjt27NBsYZo8damoaJxMnz597dq1Qgh3d3dHR8effvpJu+25c+c0\nH1bb8uXL3dzcRo8ebcjeqMo00emoUtU2EYCaj0VlAOCJjRw5svzG8ePHL1myRHuLhYXFsmXL\ngoODV69eXX5FBLlcvn79+tDQ0F27dsXHx3///fdCCAcHh5CQkNmzZ+tcEDhy5Mi333771KlT\n0sVpkZGR/v7+Li4uTxF8pf1Kt3Ha2tpqt8rJydG+GEwmk3l4eEjnpnRW7JDOnxQUFNTMUs2W\nhIQE6Uyg9O3T39+//L6Ki4ubOnVq//79w8PDtbdX2DY/P7+srCw+Pv7SpUsffvihg4PDiRMn\n1q9fn5ycvHXrVv2l5bvWT6VSJScnd+7cedWqVaWlpTExMStWrLhy5cpXX31VaduEhIT333+/\nc+fOo0ePViqVx44dW7t2rZWVlWZdnGfFWNMkMTGxU6dOgmlStWlSfpwsW7ZMoVC88847crl8\nypQpixYtmjNnTnh4uJmZ2TfffHP69GkhhE6ufvXq1X379i1atEhzca/+fp96mpTvqFLVNhGA\nmo+EEACe2MKFCxs3bqyz0cPDo3zNrl27Dh06dM2aNUOHDm3YsGGFFaSFMZOTk3/66ac9e/Z8\n8cUXP/74Y1xcnLn5fw/RgYGBDg4OW7ZsWbVq1eXLly9dulTFby16+rW1tZXL5TpXyu3cuTMi\nIkLz0t7e/vr169I1YzondqQvfAqFomaWara0bNny22+/ffTo0fHjx0eOHDljxoyPPvpIu8nm\nzZs/+uijoKCgzz77TOcsUIVtzczMhBA5OTknT56U8oSePXsWFxdv2rTp4sWL0u1kjyv19fUV\nTyI6Otrc3FyzEMvAgQOtra0jIyM1udDjlJWVTZ061dvbe8eOHdK9Uj179pTJZMuWLRs8eHD5\ndVaqwljTZPv27Z06dWKaVKW0wnFibW39ySef9O/fv379+lOmTHn48OEXX3whrcnZs2fPuXPn\nzpgxw8bGRvsNv/rqK3Nz85CQEM0W/f3qn0R6pkn5jvSrzokA1HxcMgoAT6xt27a9ymnSpEmF\nlefPn29tbT179myhd7kCb2/vkSNHRkVFTZw48dKlSzq3VFlZWQ0bNmzPnj25ubmRkZGOjo7S\n6oU6dcRj7ijLycnR+aKmp1+5XO7j45OUlKR9CWLfvn2//o/XX39d2iglJDoXiaWlpQkh3Nzc\namapZouzs3NAQEBISMiXX3757rvvrl69+sKFC5rSuXPnzpkzZ8aMGRs2bCh/zqHCtlZWVnZ2\ndk2bNtU+a9SnTx8hxKVLl/SXiidUt25d7WU5hRCDBg0SQvzyyy/6G6akpPz555/+/v7aQ7F7\n9+5lZWXSJX/PkFGmyaBBg/bv3880qWJpampq+XHSq1cvzTixsLCYP3/+5cuXjx49mpSUtHv3\n7tzcXCGE9oNG1Gr1wYMH/fz8tMe8/n6fbppU2JF+FX7A5zQRgJqPhBAAnq/atWtHRETEx8dH\nRUVpLpQqLCzcuXPngQMHytfv0aOHEEJnTVEhxJgxY3Jzc+Pi4qKiooKDg8tnKdKjt3799Ved\n7ampqampqdK5GgP7ldb9/+abbzSlXl5egf9Rt25daaO3t7ednZ1OEiKlVa1bt66Zpenp6Zs3\nb5YeLaAhPR1BenSEEGLRokUbN25cuXJlRESE9vfFStu2aNFCWp1SQ8oWpF+W/tInkp2dnZ2d\nrb1Fujev0uXypeVVdB7gJIVh3EdyP6tpEhISkpeXd/z48aeYJrdv3/bx8TG835d4mlQ4TqTT\netq5tLOzc5s2bdzd3YUQJ0+edHJyatSokab0/PnzqampOovu6O9XPNU0qbAj/WrsRACMgoQQ\nAJ67sLCw1q1bz5s3T3ODjaWl5Zo1a2bOnHnt2jXtmmVlZXv27BFC6KzCJ4To0KHDq6++unr1\n6nv37g0fPrx8L4GBgQqFYunSpdp3MRUWFkZERKjVammtBQP7nTBhQr169ebPny89C07bjRs3\nkpKSpEso5XJ5UFBQXFzc7du3pdKioqLIyMhmzZr5+PhUf2mrVq0qbWtmZvbhhx8uWLBAewFD\n6USTdDVjfHz86tWrFy5cOGbMGJ3PXmnb4ODga9eunTx5UlO6f/9+MzOzDh06VFpquLS0tFde\neUVnnRvpyjftZxVWyMvLy8HB4fjx49ofQQqpbdu2TxTGM/dMpkm7du0aN268Zs2ap5sm0hPn\nX+5pYkiph4dH+XEifUxpnEyfPr1Dhw6aKz+TkpKOHTs2ZMgQ7St4pVVndMaV/n7FU02TCjvS\nr8IPWEMmAlD9uIcQAJ7YgQMHLl68WH57u3btKrzLRS6XL1++vF+/funp6dJy9jKZbOXKlaNG\njfL39x88eLCvr6+FhUVaWlpMTExSUtKYMWMq/FIybty4iIiIRo0aVfi8bzc3t+XLl0+fPr1T\np07Dhw/38PBIS0uLjo6+fft2eHh4r169DO/XwcEhMjIyJCRk5MiR3bt379Kli52dXUZGxrlz\n506fPu3i4rJx40ap0w8++CA2NnbQoEFjxoxRKpV79+5NSUmRvjRXf+mRI0cqbevs7Dxt2rQ1\na9YMGDBAuvUuISFh79697dq169atW0lJyZw5c5ycnBQKxbfffqu9e7t37+7p6amnrRAiNDR0\n+/btY8eOnTx5sre39/Hjx/ft2zdx4kTprJT+0sOHD7/55psLFiyYOHGiEOLMmTN//PGH9IMQ\nIi4u7ubNm0KIbt26SSsibtq0adSoUQEBAcXFxfv3709ISJg4caL2+ZkKyeXyOXPmREREhISE\nhIaGKpXKEydObNu2bdCgQdJdjs+QsabJiBEjFi5c+BTTZNq0aX369CkoKHi5p4khpRWOk82b\nN48YMaJZs2Zqtbp///47d+4cPnx4SEhIRkbGZ599Vrdu3ffff197V1+9elUI4e3trfMr0B/V\nE00T/R3pUZ0TAaj5ZE/xcB4jUqlUOuf3q5NCobCxscnKyqpwaWwIIRwdHbOysp7owUGmQyaT\nubi4FBcXq1QqY8dSQ1lZWZmZmUlr99VYK1euXLx48eNKIyIi3nvvPanO7du3dZ6TNmvWrK1b\nt7Zu3VpzPuHq1avr1q378ccf79y5U1pa6uTk5OvrO2rUqAEDBmh3J72Vra3tgwcPvLy85syZ\n8+6770oVhg8ffvfu3VOnTml6OX/+/Oeff56YmPjw4UN7e3tfX9+wsLCAgADtSCrtV5Kdnb1p\n06ZDhw5dv369oKDAycmpadOmAQEBoaGh2pcmXrt2bf78+adPny4uLvb19Z09e7a0Dkf1lw4c\nOFCzyIeetmq1etu2bdu2bbt69apcLvfy8vL39585c6atre3Dhw+l0xTlff3114GBgXraStVU\nKtWnn34aExPz6NEjLy+viRMnhoWFaa471VMaGxs7bty4hQsXTpo0SQjx/vvvf/311+XD2Lhx\nY3BwcGlp6bZt23bs2HHjxo2ioqKmTZuOGzcuNDS0wsgl5ubmjo6OBQUFOTk5+/fv37Bhw7//\n/e+SkpL69esPGzZs6tSp2ud2qsiI00QIcffuXV9f36eYJsHBwWVlZZpLIl/WaWJ4qc44GTNm\nzJw5c1QqlfTVce/evWvXrr127ZpSqezTp8/cuXOla0c1xowZc/ToUZ3rPw3p1/BpUmlH+j3b\niSCXy+3t7TMzM5+u+UvP0tLS3t4+Nzf36Z61o0PnySWoIhLCJ0BCWCkSQj1ICCv1QiSERmRr\na2ttbZ2ZmVnhQ9gghHB2dtZZ9REa2gmhsWOpoZRKpXZCCB0ODg4WFhYPHjx4sb46VhsSQv1I\nCGsy7iEEAAAAABNFQggAAAAAJopFZQAANUtRccmV68mZWTmOdravNvKytLQwdkQvjO+//37E\niBF6KixfvvzNN9+srnDwjGWo8lIyskpLyzxq27s7GfrMPRNU6UTQuQsRMHEkhACAmiI7N2/9\nN9Hfxcbn5P11k4lSYT24n9+UsYMd7Cp+Yji0dezY8YcffqiwyNzc3M7OztnZuZpDwjPx6417\nmw5f+O3PdM2Whu6Ob/b1fb2ZpxGjqrH0TASJm5tbtQUD1HwkhACAGiEt/eGE2Utv/HlHe2Ne\nfsG3e4+e/PnilmVz6rnXNlZsLwobG5vHLZrPojIvruifrvzv/rM6G2+kZX68LX5kjxbj+7Ux\nSlQ1mZ6JAKA87iEEABhfSWnp1I9X6WSDGil37r8zb1VRMcurwuSc/eNO+WxQIzL+0qGz16oz\nHgAvHxJCAIDx7Tt6+rert/RUuHozZc/BE9UVDlAjqNVi/YFz+ut8eeh8QRH/KwHw9EgIAQDG\nF3P8p0rrHDj+YzVEAtQcV1Mf/JleyaNrs/IKz/5R8al1ADAECSEAwPh+v/Fn5XWuV14HeJnc\nSDPoKec37j563pEAeImREAIAjK+wqLjSOsUlJaVlZdUQDFBDFBp232xhSenzjgTAS4yEEABg\nfHVdXSqt417bxUzOny2YEDcng5624u7IQ1kAPD3+sgIAjM+vo68BdVpXQyRAzdG6obuVhVml\n1dr71KuGYAC8rEgIAQDGN3ZIP2srSz0VLC0txg9/o9riAWoChaX5kK7N9Nfp3aZhHWfb6okH\nwEuJhBAAYHxutZwWffC2ngqf/P0tHkwPEzSmd6sWS7ZwigAAHwJJREFU9V0fV+pV22HKwHbV\nGQ+Al4+5sQMAAEAIIfr36Kiwtvxk1db7D/7PkokuTg4fTx/Xp1t7YwX2rPxx8/Y30cfP/vp7\n+iOVg62ypU/DwX27dO/YythxoUazMJMvHd97bfTPR/51Xafo9WaeM4d0tlNYGSUwAC8NEkIA\nQE3Ro1Obw9uax506e/bXK49U2Y72tm1b+vTt1l6psDZ2aFWiVqs3RB5cv/2AZkvGo6wTiRdP\nJF7s1dn30/fDlNZ8p8djWVmYfTDs9WF+zX5I+jMlXVVaWuZR2+H1Zh6vetYydmgAXgYkhACA\nGsTayvJvvbv8rXcXYwfyLG3ec1g7G9T2fcLFDxZv/Oz/TZXLZdUcFV4s9d0c67s5GjsKAC8h\n7iEEAOA5+vPO/fU7YvRUOHXu0sH4n6stHgAAtJEQAgDwHH136FRxZY8X3xkTXy2xAACgi4QQ\nAIDn6Py/r1Za59IftwqLiqshGAAAdLxg9xAqlUq53GhJrEwmE0LY2tqq1WpjxVDDyeVyBwcH\nY0dRo5mbmzs5ORk7ihpKJpPJZDIrK1bXqJh09LO3t+cQ9DhyubwGzi9VTl6lddRqdZl4vsFL\nf8KsrKwsLCyeXy8vNLlcrlarFQqFsQOpoaRDkKMj9zE+Vs08BNUQ0iFIoVBYW7/Yi4S9lF6w\nhDAvL6+42Gj/Q1UoFDY2Njk5OUVFRcaKoYZzdHTMysoqKyszdiA1kUwmc3FxKSkpUalUxo6l\nhrKysjIzM8vLq/zbs2mytbW1trbOysoqKank+kOT5ezs/OjRo8rrVS87m8oTDJlMJlOXPtfg\nzc3NHR0dCwsLc3Jynl8vLzSlUllWVlZQUGDsQGooBwcHCwuLzMxM/idVIblcbm9vn5mZaexA\naihLS0t7e/v8/Pz8/Pyqv1utWiyx+yxxySgAAM/Ra81fqbROs1e8rK0sqyEYAAB0kBACAPAc\nDe3nZ2Fupr9OyIAe1RILAAC6SAgBAHiO6nu4TRgRqKdC5zbNBvTqVG3xAACgjYQQAIDnK3zk\nGxOG96+wqFu7Fis+fNvMeOulAQBM3Au2qAwAAC8cmUw2fdygXp19v913/MwvVzIeZdkqrVu9\n2nBIv269X28jLb4HAIBRkBACAFAdWjSpv+SD8UIItVpNEggAqCG4RgUAgGpFNggAqDlICAEA\nAADARJEQAgAAAICJIiEEAAAAABNFQggAAAAAJoqEEAAAAABMFAkhAAAAAJgoEkIAAAAAMFEk\nhAAAAABgokgIAQAAAMBEkRACAAAAgIkiIQQAAAAAE0VCCAAAAAAmioQQAAAAAEwUCSEAAAAA\nmCgSQgAAAAAwUSSEAAAAAGCiSAgBAAAAwESREAIAAACAiSIhBAAAAAATRUIIAAAAACaKhBAA\nAAAATBQJIQAAAACYKBJCAAAAADBRJIQAAAAAYKJICAEAAADARJEQAgAAAICJIiEEAAAAABNF\nQggAAAAAJoqEEAAAAABMFAkhAAAAAJgoEkIAAAAAMFEkhAAAAABgokgIAQAAAMBEkRACAAAA\ngIkiIQQAAAAAE0VCCAAAAAAmioQQAAAAL7yVK1fWfryUlBRNnT59+pSVlek0DwoK6t27t+bl\nw4cPV6xY0bNnzyZNmtSpU8fHx2fIkCExMTE63TVp0qSoqKh8MB999FHt2rW7desmvZwzZ07t\n2rVv3rxZvma3bt1ee+01w/uVpKenL1q0qGfPng0bNqxTp06zZs3eeuutEydO6FT77bffRo8e\n3ahRIw8Pj379+h05cqSGly5btqz8727IkCGaCkVFRcuWLWvQoEH5PYmnZm7sAAAAAIBnY9q0\naV5eXuW3Ozk5aX6+ePHili1bJkyY8Lg3efToUe/evTMyMkaOHNmpUydzc/MbN27s3Lnzrbfe\nWrFixdixY6Vqcrk8Kyvr0KFDQUFB2s2Li4v/+c9/WlhYPGnwBvb7448/jhs3Ljc3t1+/foMH\nDzY3N09OTo6Ojo6JiZk+ffq8efOkajdv3hwwYICrq+vcuXMtLCz27ds3ZsyYr7/+un///tVf\nun379pEjR1baNisrSy6Xr1ixQnu31KlTR/NzeHh4bGzsjBkznnTfQg8SQgAAALwkAgMD27Vr\np6eCubm5v7//4sWLBw4c6OrqWmGdXbt2paSkbNq0STvTGz9+fI8ePZYsWRISEmJpaSmEUKvV\nHTp02LFjh05CGBcX9/Dhw9deey03N/eJgjek3zt37owbN87CwuLo0aMtW7bUVJs7d+7YsWPX\nrl3bsWPHvn37CiGWLFlSUlKyb98+Nzc3IURISEjv3r0//vhjKfWq5tKPPvpISgj1t83KyrK1\ntR09enSF+yc+Pv7AgQPz58/XJL14JrhkFAAAAKaipKRk4cKFxcXFc+fOfVyde/fuCSFatWql\nvdHW1jY2NvbChQtSNiiEUKvVffv2jY+Pv3PnjnbNXbt2tWnTxsHB4UljM6TfdevWqVSq5cuX\na2eDUrX169d//PHHTZs2FUKUlpYeOnQoICBAyruEEObm5qNGjbp161ZSUlL1l968efPChQv6\n2wohsrOz7ezsHrd/du/ebWtrO378+CfdsdCPhBAAAAAmpG7duu+9997evXvj4+MrrCClZPPm\nzXv48KH2dldXVysrK+0tAwcOlMlku3fv1mx5+PBhXFzckCFD1Gr1kwZmSL9HjhxxcXEJDAws\n39zNzW3atGmenp5CiJs3b+bn57do0UK7QvPmzYUQly5dMkrpxYsXb9y4oaetECIrK0uTEBYU\nFOh8wDNnzrRv316TkONZISEEAADASyIzM/N+OTk5Odp11Gr1lClTGjduPHv27AqXhAkKCho6\ndOiRI0datmw5YsSI1atXJyYmlpSUlK/p7u7u5+e3Y8cOzZbvvvtOrVZrr4NiuEr7LSkpuXXr\nVvPmzeXySr7DZ2RkCCFq1aqlvbF27dpCiPT0dKOU3r9/Pz09XU9bIURWVlZhYWF4eHiTJk08\nPT19fHyWLFlSWFgohFCr1SkpKZ6ent98803jxo31f3w8Ee4hBAAAwEtCulFNx/jx45csWaK9\nxcLCYtmyZcHBwatXr541a5ZOfblcvn79+tDQ0F27dsXHx3///fdCCAcHh5CQkNmzZ+tc0zhy\n5Mi33347ISEhICBACBEZGenv7+/i4vIUwVfab15enhDC1tZWu1VOTs6jR480L2UymYeHh3R6\nTWdhG+k0Y0FBgVFK8/Pz9bcVQqhUquTk5M6dO69ataq0tDQmJmbFihVXrlz56quv8vPzy8rK\n4uPjL126tHDhQsP2KAxCQggAAICXxMKFC8ufPvLw8Chfs2vXrkOHDl2zZs3QoUMbNmxYYYWu\nXbsKIZKTk3/66ac9e/Z88cUXP/74Y1xcnLn5f79CBwYGOjg4bN++PSAg4PLly5cuXZo5c2ZV\nPoKefm1tbeVyuc4FpTt37oyIiNC8tLe3v379ukKhEELonP+Uki6FQmGUUqVSqb+tECI6Otrc\n3Fyz2M/AgQOtra0jIyMTExPbtGkjhMjJyTl58mT9+vUr3Y0wHAkhAAAAXhJt27bVv8qotvnz\n5x89enT27Nl79uyRyWSPq+bt7e3t7T1y5MgPP/zwyy+/jI+P135ioZWV1aBBg/bs2ZOTkxMZ\nGeno6Cgt8qlN+ySYjpycHJ0zfvr79fHxSUpKKiws1NxV2Ldv37p160o/f/HFF9LNeFJOJV3A\nqZGWliaEcHNzM0ppnTp1pLVkHtdWCKH5IBqDBg2KjIz85ZdfOnXqZGdn17Rp08ftLjw17iEE\nAACAKapdu3ZERER8fHxUVJTmOsbCwsKdO3ceOHCgfP0ePXoIIVJTU3W2h4SE5OXlHT58OCoq\nKjg4uPyqJ9IZrV9//VVne2pqampqqnRK08B+g4KCcnNzv/nmG02pl5dX4H9oEipvb287O7tf\nfvlF+30uXLgghGjdurVRStu2bVu/fn09bYUQ2dnZ2dnZ2qXS/Z/S+cMWLVpIC7Hi2SIhBAAA\ngIkKCwtr3br1vHnzNGu3WFparlmzZubMmdeuXdOuWVZWtmfPHiGEziKZQoh27do1btx42bJl\n9+7dGz58ePleAgMDFQrF0qVLtW/2KywsjIiIUKvV0mP3DOx3woQJ9erVmz9//rFjx3R6uXHj\nRlJSkrTejFwuDwoKiouLu337tlRaVFQUGRnZrFkzHx+f6i9t3rx5s2bN9LdNS0t75ZVXpk6d\nqv2hduzYIZPJunTpIoQIDg6+du3ayZMny+9hVAWXjAIAAOAlceDAgYsXL5bf3q5dO19f3/Lb\n5XL58uXL+/Xrl56eLj31QSaTrVy5ctSoUf7+/oMHD/b19bWwsEhLS4uJiUlKShozZkzbtm3L\nv09ISMiCBQsaNWpU4QWrbm5uy5cvnz59eqdOnYYPH+7h4ZGWlhYdHX379u3w8PBevXoZ3q+D\ng0NkZGRISMjIkSO7d+/epUsXOzu7jIyMc+fOnT592sXFZePGjVKnH3zwQWxs7KBBg8aMGaNU\nKvfu3ZuSkiLlltVfGh0dXWlbd3f3sLCwTZs2jRo1KiAgoLi4eP/+/QkJCRMnTmzUqJEQIjQ0\ndPv27WPHjv3ll1/mz59v0ICAAWRP8YwUI1KpVMXFxcbqXaFQ2NjYZGVlVbhCMYQQjo6OWVlZ\nZWVlxg6kJpLJZC4uLsXFxSqVytix1FBWVlZmZmbSEmooz9bW1traOjMzs8KlzyGEcHZ21llr\nARrm5uaOjo4FBQU66+9DQ6lUlpWVVXiXF4QQDg4OFhYWDx48qLFfHVeuXLl48eLHlUZERLz3\n3ntSndu3b+s8TnDWrFlbt25t3bq15rTb1atX161b9+OPP965c6e0tNTJycnX13fUqFEDBgzQ\n7k7zVvfu3WvVqtWcOXPeffddqcLw4cPv3r176tQpTS/nz5///PPPExMTHz58aG9v7+vrGxYW\nJq1NqlFpv5Ls7OxNmzYdOnTo+vXrBQUFTk5OTZs2DQgICA0Nla6ulFy7dm3+/PmnT58uLi72\n9fWdPXu2tFxN9Zf26tXL3t4+Nzc3Pz9fT9vS0tJt27bt2LHjxo0bRUVFTZs2HTduXGhoqOad\nVSrVp59+Ghsbe/fu3cf9rvGkSAifAAlhpUgI9SAhrBQJoX4khJUiIdSDhLBSJIT61fyE0Ljk\ncrm9vX1mZqaxA6mhLC0tNQlh1d9N50mGqCLuIQQAAAAAE0VCCAAAAAAmioQQAAAAwGOp1eqC\nIqPdtIXnjVVGAQAAAOgqLin9Li4x9od/XU2+W1Ja5mhn06l143EDezT2rmPs0PAskRACAAAA\n+D/upj96d+lX11LSpJdqITKzcw+fvnj49MVpof3HDexh1OjwLHHJKAAAAID/ys0vmLZ4iyYb\nFELItEo/237on3GJ1R8VnhMSQgAAAAD/9dW++Fup9/VUWLM99qGKZ9i8JEgIAQAAAPyltKxs\n7/Ez+uvk5RceOn2heuLB80ZCCAAAAOAvf97NyMzKrbTaL7/fev6xoDqQEAIAAAD4S1ZOviHV\nMrPznnckqB4khAAAAAD+4uRgY0g1Fwfb5x0JqgcJIQAAAIC/eLq5uDo7VFqtbbOG1RAMqgEJ\nIQAAAIC/yGSy4f1e11/H0U7Zr2ub6okHzxsJIQAAAID/Cn2jW8smXnoqfDhxiK3SutriwXNF\nQggAAADgvyzMzVbPfqtTq8bli6wtLRZMC+nVsUX1R4XnxNzYAQAAAACoWRxslZ99OP7EmX/H\nnjp/+cbtwqKSWk52XXxfHdH/dUPuMMQLhIQQAAAAgC6ZTNarYwtOBr70uGQUAAAAAEwUCSEA\nAAAAmCgSQgAAAAAwUSSEAAAAAGCiSAgBAAAAwESREAIAAACAiSIhBAAAAAATRUIIAAAAACaK\nhBAAAAAATBQJIQAAAACYKBJCAAAAADBRJIQAAAAAYKJICAEAAADARJEQAgAAAICJIiEEAAAA\nABNFQggAAAAAJoqEEAAAAABMFAkhAAAAAJgoEkIAAAAAMFEkhAAAAABgokgIAQAAAMBEkRAC\nAAAAgImSqdVqY8fwwkhISDhx4sSQIUN8fHyMHQtePCUlJcuWLfPy8ho9erSxY8ELKTY29uLF\nixMmTHB1dTV2LHjx3L9/f9OmTb6+voGBgcaOBS+k7du3Jycnz5o1y9zc3Nix4MXzxx9/fPfd\ndz179uzcubOxY4EuzhA+gatXr0ZFRaWmpho7ELyQysrKoqKiTp06ZexA8KK6ePFiVFSUSqUy\ndiB4IWVlZUVFRV24cMHYgeBFderUqaioqNLSUmMHghdSampqVFTUH3/8YexAUAESQgAAAAAw\nUSSEAAAAAGCiSAgBAAAAwESxqAwAAAAAmCjOEAIAAACAiSIhBAAAAAATRUIIAAAAACaKR4v+\nJScnZ+PGjb/++mtxcbGPj094eHj5Rz+npKR8/fXXly9fVqvVDRo0GDNmzKuvviqEmD59+q1b\ntzTVrK2td+/eXZ3BoyYwZAhpHD9+fM2aNR9++GGnTp2etC1eSlUZPxyCIAwbQo8bKhyCUJXx\nwyEIwuDDSGxs7N69ex88eFCvXr2xY8e2b9/e8LZ4flhU5i8LFy7MycmZNGmSlZXVjh07bt26\ntXbtWrn8v2dQS0pKJkyY0Lp16+HDh8vl8l27dv38889btmxRKBRhYWGDBw+WvpkJIeRyubOz\ns5E+B4ym0iGkkZmZOX369Ly8vPfff18aNoa3xcuqKuOHQxCEYUPocUOFQxCqMn44BEEYNoSO\nHz++bdu2adOmeXl5JSQkHDx4cPXq1UqlkkOQ0bGvhRAiIyPj7Nmzb7/9doMGDerWrRseHp6a\nmpqUlKRdJzc3NygoKDw8vF69enXq1Bk2bFhubu7du3eFENnZ2e7u7rX+g+OgCTJkCGls2LCh\nR48eSqXyKdripVSV8SM4BMHgIVThUOEQhKqMHz3bYToMHEK7du0aN25cu3btXF1dg4KCNm7c\nqFQqOQTVBCSEQghx9epVCwuLBg0aSC9tbW09PDx+//137ToODg7BwcEKhUIIkZ2dvX//fg8P\nD09Pz+Li4sLCwoSEhL///e/jx49fvHhxamqqET4DjMqQISRJSEi4fv36qFGjnqItXlZVGT8c\ngiAMG0KPGyocglCV8cMhCMKwIfTgwYO0tDQhxPTp04cNG/b+++9fuXLFwLZ43kgIhRAiKyvL\nzs5OJpNptjg4OKhUqvI1y8rKhgwZEhoampKSsmDBAgsLi7y8PEdHx5KSknfeeWf27NlFRUUR\nERG5ubnVGD6Mz8AhlJOTs2HDhilTplhbWz9pW7zEqjJ+OARBGDaEHjdUOAShKuOHQxCEYUPo\nwYMHQohjx47NmjVry5YtPj4+//jHP1QqFYegmoBFZf6iPRD1kMvla9asefTo0cGDBz/88MMV\nK1Y4ODhs27ZNU2HWrFnjxo376aef+vTp89yCRU1kyBDavHnza6+95uvr+xRt8XJ76vHDIQiS\nSofQ44aKIW3x0nvq8dOnTx8OQRAGH0ZGjBjh4eEhhAgLCztx4sS5c+cMb4vnhzOEQgjh6OiY\nlZWlvb6OSqVycnKqsLKHh0fLli1nzZqlUqlOnjypU6pQKGrXrp2RkfEcw0XNY8gQunjx4vnz\n58PCwp6iLV5uVRk/OjgEmaanOIxohgqHIFRl/Bi4HS83Q4aQdHOpjY2N9NLMzMzZ2fnRo0cc\ngmoCEkIhhGjcuHFxcfH169ell1lZWSkpKU2bNtWuc+HChbfffruwsFB6KZPJzM3NhRDJycn/\n+7//W1JSIm0vKChIT093d3evxvBhfIYMobi4uNzc3PDw8NDQ0NDQUJVKtWrVqsWLFxvSFi+3\nqowfDkEQhg2hxw0VDkGoyvjhEARh2BBydnZ2cnKS7hsUQhQVFaWnp7u5uXEIqgnMPvnkE2PH\nYHwKhSI5OfnEiRM+Pj55eXnr1q2zsbEJDQ2VyWRxcXG//fabj4+PnZ3dvn37bty44e3tnZ+f\nv3Pnzt9//33ChAkODg4bNmxITU2tX7++SqX64osvcnJyJk+eLKWLMBGGDKFWrVr11xIfH//W\nW28FBwc7Ojo+rq2xPxaqSVXGj1Kp5BAEQ4aQmZlZhUPFzs6OQ5CJq8r4sbS05BAEQ4aQTCYr\nLS397rvvGjZsaG5u/tVXX92/f3/SpEkcgmoCnkP4l7y8vI0bN164cKG0tLR58+bh4eHS2erl\ny5dnZWUtWLBACJGcnLx169bffvtNJpN5eXmNHj26devWQogbN25s3bpVWiXJx8dn4sSJbm5u\nRv48qHaGDCFtY8eOfeedd6QHNz2uLUxHVcYPhyAIw4bQ44YKhyBUZfxwCIIwbAiVlZV9++23\nx44dy8nJ8fHxeeeddzw9PfW0RbUhIQQAAAAAE8U9hAAAAABgokgIAQAAAMBEkRACAAAAgIki\nIQQAAAAAE0VCCAAAAAAmioQQAAAAAEwUCSEAAAAAmCgSQgDAi6RTp06vvvrqs6oGAICJMzd2\nAAAAPIGQkJD8/PxnVQ0AABMnU6vVxo4BAAAAAGAEXDIKAHgG2rZt27lz5++//75Dhw5KpdLZ\n2TksLEylUkmlXbt29fPzi4mJ8fT0fP3116WNJ0+e7NOnj729vVKpfO2117Zs2aL9hnFxcd27\nd7ezs3N3dx8+fPi1a9ek7drXgt69e3fixIne3t7W1tbu7u5Dhgy5cuVK+WpCiEOHDvn5+dnZ\n2SkUihYtWqxcuVLz/1A/P79u3bpduHDB39/f3t7e1dV15MiR9+/ff267CgCAGoRLRgEAz4CV\nldW1a9dmz569Zs2aJk2aHD58OCwsLDMzMyoqSirNyMj44IMPIiIivL29hRDHjx8PCAjo0qXL\njh07rKysoqKixo8f/+jRo5kzZwoh4uLiAgIC+vTps2HDhsLCwkWLFvn5+Z0/f97d3V2708GD\nB9+6dWvhwoUNGza8e/fukiVLunfvfvPmTaVSqV0tOjp68ODBAQEB3377ra2tbWxs7MyZM9PS\n0pYtWyaEsLS0/OOPPyZNmvTpp5+2atXq1KlTI0aMsLKy+uqrr6pt7wEAYDRqAACqrEuXLkKI\nH374QbNl/PjxQog///xTrVb7+/sLIaKiojSlbdq0eeWVV3JzczVbBg4caGdnl5+fr1ar27Vr\n16BBg+LiYqno559/trS0XLNmjVqt7tixo4+Pj1qtlk4/zpkzR/MO165d+/TTT1NTU7WrqdXq\nV1991cvLq7CwUFNz0KBBFhYWGRkZmthOnz6tKfX3969bt+6z2zcAANRcXDIKAHg2bGxsunbt\nqnnp5+cnhLh06ZL00tLScsCAAdLP9+/fv3DhwhtvvCGXywv+IzAwMDs7Oykp6cGDB+fOnevf\nv7+5+V+XsXTo0KGwsHD69Ona3SkUChcXl8jIyOPHj5eVlQkhGjVqFBERUbduXe1qd+7cuXLl\nSmBgoKWlpWbj3/72t+Li4sTEROmlUqmUElqJh4dHWlraM9orAADUaCSEAIBnw83NTSaTaV66\nuLgIIe7duye9rFWrloWFhfTznTt3hBBr1qxRaAkPDxdC3L59++7du0IIV1dX/d1ZWFjs27dP\nLpf37t3b1dV16NChO3bsKCkp0amWmpoqhKhXr572xjp16mjCEELUrl1bu9Tc3FzKMAEAeOlx\nDyEA4LmQcjO5/K//PGqyQY2wsLCJEyfqbHzllVekBV0MScm6dOly9erVkydPHjp0KDY2NjQ0\ndNWqVT/88INCodDUkXJUnXdTq9XasQEAYLJICAEAz8bdu3dLS0vNzMykl9K5QTc3t/I1vby8\nhBClpaWdOnUqX2plZSWESElJ0d6YnJysVCp1TuUJIczMzHr16tWrV6/ly5evX7/+nXfe2b17\n97hx4zQVPDw8xH/OE2pIL6UiAABMGf8cBQA8G/n5+UePHtW8PHTokJWVVYcOHcrXdHZ27tCh\nQ3R0dGZmpmbjtm3b5s6dW1JSYmdn17Jly5iYmOzsbKnoypUr9evXX7dunfab/Otf/woJCdF+\nPkTfvn2FEOnp6drV3N3dW7RoERMTU1BQoNkYFRWlVCo7d+5cpQ8MAMCLjzOEAIBnw9PT8+9/\n/3tycvIrr7xy5MiR6OjosWPHOjk5VVh52bJlffr06d69+8yZM93d3U+dOrV06dLQ0FBpIZnF\nixcPHDiwT58+M2bMyMnJ+Z//+R9XV9dJkyZpv0O9evViY2MvX748Y8YMLy+vBw8erF271t7e\nPjg4WKevpUuX/u1vfwsKCpoyZYqlpeX+/fsPHz68ePFie3v757QrAAB4UXCGEADwbNjY2OzY\nsSMyMjIoKGjz5s0TJ078/PPPH1e5e/fu33//vZub29SpUwcMGLB79+5FixZ9+eWXUukbb7xx\n4MABmUw2YcKEuXPnNm/e/PTp0zoPIXR3dz99+rS0smhgYOB7773n5uYWHx/fqFEjnb4CAwMP\nHz6cl5c3atSoQYMGJSYmbtmyZc6cOc98DwAA8MKRSTfWAwBQFV27ds3IyLhy5YqxAwEAAE+A\nM4QAAAAAYKJICAEAAADARJEQAgAAAICJ4h5CAAAAADBRnCEEAAAAABNFQggAAAAAJoqEEAAA\nAABMFAkhAAAAAJgoEkIAAAAAMFEkhAAAAABgokgIAQAAAMBE/X8l4laBt2QZlwAAAABJRU5E\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the nodes using their precision. recall and f1 scores to examine\n",
    "\n",
    "ggplot(markers_Ptgs2, aes(x = precision, \n",
    "                          y = recall, \n",
    "                          colour = f1, \n",
    "                          label = genes)) +\n",
    "    geom_point(size = 3) +\n",
    "    geom_text(vjust = 0, \n",
    "              nudge_y = 0.01, \n",
    "              colour = \"black\", \n",
    "              check_overlap = T)\n",
    "fig(8, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>gene</th><th scope=col>gene_id</th><th scope=col>node_id</th><th scope=col>node_num</th><th scope=col>coord</th><th scope=col>strand</th><th scope=col>type</th><th scope=col>gene_name</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>21953</th><td>ENSMUSG00000022425</td><td>ENSMUSG00000022425.16</td><td>ENSMUSG00000022425.16_5 </td><td>5 </td><td>chr15:54919962-54920002 </td><td>-</td><td>TS</td><td>Enpp2        </td></tr>\n",
       "\t<tr><th scope=row>38654</th><td>ENSMUSG00000028222</td><td>ENSMUSG00000028222.2 </td><td>ENSMUSG00000028222.2_8  </td><td>8 </td><td>chr4:15895629-15895685  </td><td>+</td><td>CE</td><td>Calb1        </td></tr>\n",
       "\t<tr><th scope=row>52536</th><td>ENSMUSG00000032366</td><td>ENSMUSG00000032366.15</td><td>ENSMUSG00000032366.15_8 </td><td>8 </td><td>chr9:67049192-67049201  </td><td>-</td><td>TS</td><td>Tpm1         </td></tr>\n",
       "\t<tr><th scope=row>56152</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_29</td><td>29</td><td>chr3:80690404-80690518  </td><td>-</td><td>CE</td><td>Gria2        </td></tr>\n",
       "\t<tr><th scope=row>66836</th><td>ENSMUSG00000039474</td><td>ENSMUSG00000039474.13</td><td>ENSMUSG00000039474.13_13</td><td>13</td><td>chr5:36966107-36968450  </td><td>-</td><td>TE</td><td>Wfs1         </td></tr>\n",
       "\t<tr><th scope=row>68218</th><td>ENSMUSG00000040183</td><td>ENSMUSG00000040183.13</td><td>ENSMUSG00000040183.13_30</td><td>30</td><td>chr4:32804035-32805739  </td><td>-</td><td>TE</td><td>Ankrd6       </td></tr>\n",
       "\t<tr><th scope=row>72478</th><td>ENSMUSG00000042671</td><td>ENSMUSG00000042671.12</td><td>ENSMUSG00000042671.12_26</td><td>26</td><td>chr1:153695579-153697667</td><td>+</td><td>TE</td><td>Rgs8         </td></tr>\n",
       "\t<tr><th scope=row>82625</th><td>ENSMUSG00000056296</td><td>ENSMUSG00000056296.17</td><td>ENSMUSG00000056296.17_5 </td><td>5 </td><td>chr14:13284877-13285002 </td><td>+</td><td>AD</td><td>Synpr        </td></tr>\n",
       "\t<tr><th scope=row>82996</th><td>ENSMUSG00000056820</td><td>ENSMUSG00000056820.7 </td><td>ENSMUSG00000056820.7_1  </td><td>1 </td><td>chr8:125012997-125013048</td><td>+</td><td>TS</td><td>Tsnax        </td></tr>\n",
       "\t<tr><th scope=row>95201</th><td>ENSMUSG00000089706</td><td>ENSMUSG00000089706.7 </td><td>ENSMUSG00000089706.7_8  </td><td>8 </td><td>chr1:98040239-98040313  </td><td>-</td><td>CE</td><td>B230216N24Rik</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & gene & gene\\_id & node\\_id & node\\_num & coord & strand & type & gene\\_name\\\\\n",
       "  & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <chr>\\\\\n",
       "\\hline\n",
       "\t21953 & ENSMUSG00000022425 & ENSMUSG00000022425.16 & ENSMUSG00000022425.16\\_5  & 5  & chr15:54919962-54920002  & - & TS & Enpp2        \\\\\n",
       "\t38654 & ENSMUSG00000028222 & ENSMUSG00000028222.2  & ENSMUSG00000028222.2\\_8   & 8  & chr4:15895629-15895685   & + & CE & Calb1        \\\\\n",
       "\t52536 & ENSMUSG00000032366 & ENSMUSG00000032366.15 & ENSMUSG00000032366.15\\_8  & 8  & chr9:67049192-67049201   & - & TS & Tpm1         \\\\\n",
       "\t56152 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_29 & 29 & chr3:80690404-80690518   & - & CE & Gria2        \\\\\n",
       "\t66836 & ENSMUSG00000039474 & ENSMUSG00000039474.13 & ENSMUSG00000039474.13\\_13 & 13 & chr5:36966107-36968450   & - & TE & Wfs1         \\\\\n",
       "\t68218 & ENSMUSG00000040183 & ENSMUSG00000040183.13 & ENSMUSG00000040183.13\\_30 & 30 & chr4:32804035-32805739   & - & TE & Ankrd6       \\\\\n",
       "\t72478 & ENSMUSG00000042671 & ENSMUSG00000042671.12 & ENSMUSG00000042671.12\\_26 & 26 & chr1:153695579-153697667 & + & TE & Rgs8         \\\\\n",
       "\t82625 & ENSMUSG00000056296 & ENSMUSG00000056296.17 & ENSMUSG00000056296.17\\_5  & 5  & chr14:13284877-13285002  & + & AD & Synpr        \\\\\n",
       "\t82996 & ENSMUSG00000056820 & ENSMUSG00000056820.7  & ENSMUSG00000056820.7\\_1   & 1  & chr8:125012997-125013048 & + & TS & Tsnax        \\\\\n",
       "\t95201 & ENSMUSG00000089706 & ENSMUSG00000089706.7  & ENSMUSG00000089706.7\\_8   & 8  & chr1:98040239-98040313   & - & CE & B230216N24Rik\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 8\n",
       "\n",
       "| <!--/--> | gene &lt;fct&gt; | gene_id &lt;fct&gt; | node_id &lt;fct&gt; | node_num &lt;fct&gt; | coord &lt;fct&gt; | strand &lt;fct&gt; | type &lt;fct&gt; | gene_name &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 21953 | ENSMUSG00000022425 | ENSMUSG00000022425.16 | ENSMUSG00000022425.16_5  | 5  | chr15:54919962-54920002  | - | TS | Enpp2         |\n",
       "| 38654 | ENSMUSG00000028222 | ENSMUSG00000028222.2  | ENSMUSG00000028222.2_8   | 8  | chr4:15895629-15895685   | + | CE | Calb1         |\n",
       "| 52536 | ENSMUSG00000032366 | ENSMUSG00000032366.15 | ENSMUSG00000032366.15_8  | 8  | chr9:67049192-67049201   | - | TS | Tpm1          |\n",
       "| 56152 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_29 | 29 | chr3:80690404-80690518   | - | CE | Gria2         |\n",
       "| 66836 | ENSMUSG00000039474 | ENSMUSG00000039474.13 | ENSMUSG00000039474.13_13 | 13 | chr5:36966107-36968450   | - | TE | Wfs1          |\n",
       "| 68218 | ENSMUSG00000040183 | ENSMUSG00000040183.13 | ENSMUSG00000040183.13_30 | 30 | chr4:32804035-32805739   | - | TE | Ankrd6        |\n",
       "| 72478 | ENSMUSG00000042671 | ENSMUSG00000042671.12 | ENSMUSG00000042671.12_26 | 26 | chr1:153695579-153697667 | + | TE | Rgs8          |\n",
       "| 82625 | ENSMUSG00000056296 | ENSMUSG00000056296.17 | ENSMUSG00000056296.17_5  | 5  | chr14:13284877-13285002  | + | AD | Synpr         |\n",
       "| 82996 | ENSMUSG00000056820 | ENSMUSG00000056820.7  | ENSMUSG00000056820.7_1   | 1  | chr8:125012997-125013048 | + | TS | Tsnax         |\n",
       "| 95201 | ENSMUSG00000089706 | ENSMUSG00000089706.7  | ENSMUSG00000089706.7_8   | 8  | chr1:98040239-98040313   | - | CE | B230216N24Rik |\n",
       "\n"
      ],
      "text/plain": [
       "      gene               gene_id               node_id                 \n",
       "21953 ENSMUSG00000022425 ENSMUSG00000022425.16 ENSMUSG00000022425.16_5 \n",
       "38654 ENSMUSG00000028222 ENSMUSG00000028222.2  ENSMUSG00000028222.2_8  \n",
       "52536 ENSMUSG00000032366 ENSMUSG00000032366.15 ENSMUSG00000032366.15_8 \n",
       "56152 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_29\n",
       "66836 ENSMUSG00000039474 ENSMUSG00000039474.13 ENSMUSG00000039474.13_13\n",
       "68218 ENSMUSG00000040183 ENSMUSG00000040183.13 ENSMUSG00000040183.13_30\n",
       "72478 ENSMUSG00000042671 ENSMUSG00000042671.12 ENSMUSG00000042671.12_26\n",
       "82625 ENSMUSG00000056296 ENSMUSG00000056296.17 ENSMUSG00000056296.17_5 \n",
       "82996 ENSMUSG00000056820 ENSMUSG00000056820.7  ENSMUSG00000056820.7_1  \n",
       "95201 ENSMUSG00000089706 ENSMUSG00000089706.7  ENSMUSG00000089706.7_8  \n",
       "      node_num coord                    strand type gene_name    \n",
       "21953 5        chr15:54919962-54920002  -      TS   Enpp2        \n",
       "38654 8        chr4:15895629-15895685   +      CE   Calb1        \n",
       "52536 8        chr9:67049192-67049201   -      TS   Tpm1         \n",
       "56152 29       chr3:80690404-80690518   -      CE   Gria2        \n",
       "66836 13       chr5:36966107-36968450   -      TE   Wfs1         \n",
       "68218 30       chr4:32804035-32805739   -      TE   Ankrd6       \n",
       "72478 26       chr1:153695579-153697667 +      TE   Rgs8         \n",
       "82625 5        chr14:13284877-13285002  +      AD   Synpr        \n",
       "82996 1        chr8:125012997-125013048 +      TS   Tsnax        \n",
       "95201 8        chr1:98040239-98040313   -      CE   B230216N24Rik"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OK... but the node names are confusing, who are they?\n",
    "# lets see what genes does these marker nodes belongs to\n",
    "\n",
    "nodeDetails(index.glu, as.character(markers_Ptgs2$genes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search enriched cell types by interested splicing events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 14 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>gene</th><th scope=col>gene_id</th><th scope=col>node_id</th><th scope=col>node_num</th><th scope=col>coord</th><th scope=col>strand</th><th scope=col>type</th><th scope=col>gene_name</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>56143</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_10</td><td>10</td><td>chr3:80740959-80741198</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56148</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_12</td><td>12</td><td>chr3:80726832-80726885</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56144</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_13</td><td>13</td><td>chr3:80722032-80722193</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56145</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_14</td><td>14</td><td>chr3:80710677-80710844</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56150</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_15</td><td>15</td><td>chr3:80710290-80710394</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56162</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_16</td><td>16</td><td>chr3:80709378-80709488</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56164</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_17</td><td>17</td><td>chr3:80707699-80707905</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56160</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_20</td><td>20</td><td>chr3:80706888-80707258</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56151</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_23</td><td>23</td><td>chr3:80702458-80702656</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56166</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_24</td><td>24</td><td>chr3:80692285-80692532</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56146</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_26</td><td>26</td><td>chr3:80691703-80691743</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56141</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_28</td><td>28</td><td>chr3:80691320-80691434</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56152</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_29</td><td>29</td><td>chr3:80690404-80690518</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "\t<tr><th scope=row>56158</th><td>ENSMUSG00000033981</td><td>ENSMUSG00000033981.14</td><td>ENSMUSG00000033981.14_30</td><td>30</td><td>chr3:80689103-80689351</td><td>-</td><td>CE</td><td>Gria2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 14 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & gene & gene\\_id & node\\_id & node\\_num & coord & strand & type & gene\\_name\\\\\n",
       "  & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <chr>\\\\\n",
       "\\hline\n",
       "\t56143 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_10 & 10 & chr3:80740959-80741198 & - & CE & Gria2\\\\\n",
       "\t56148 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_12 & 12 & chr3:80726832-80726885 & - & CE & Gria2\\\\\n",
       "\t56144 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_13 & 13 & chr3:80722032-80722193 & - & CE & Gria2\\\\\n",
       "\t56145 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_14 & 14 & chr3:80710677-80710844 & - & CE & Gria2\\\\\n",
       "\t56150 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_15 & 15 & chr3:80710290-80710394 & - & CE & Gria2\\\\\n",
       "\t56162 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_16 & 16 & chr3:80709378-80709488 & - & CE & Gria2\\\\\n",
       "\t56164 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_17 & 17 & chr3:80707699-80707905 & - & CE & Gria2\\\\\n",
       "\t56160 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_20 & 20 & chr3:80706888-80707258 & - & CE & Gria2\\\\\n",
       "\t56151 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_23 & 23 & chr3:80702458-80702656 & - & CE & Gria2\\\\\n",
       "\t56166 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_24 & 24 & chr3:80692285-80692532 & - & CE & Gria2\\\\\n",
       "\t56146 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_26 & 26 & chr3:80691703-80691743 & - & CE & Gria2\\\\\n",
       "\t56141 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_28 & 28 & chr3:80691320-80691434 & - & CE & Gria2\\\\\n",
       "\t56152 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_29 & 29 & chr3:80690404-80690518 & - & CE & Gria2\\\\\n",
       "\t56158 & ENSMUSG00000033981 & ENSMUSG00000033981.14 & ENSMUSG00000033981.14\\_30 & 30 & chr3:80689103-80689351 & - & CE & Gria2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 14 × 8\n",
       "\n",
       "| <!--/--> | gene &lt;fct&gt; | gene_id &lt;fct&gt; | node_id &lt;fct&gt; | node_num &lt;fct&gt; | coord &lt;fct&gt; | strand &lt;fct&gt; | type &lt;fct&gt; | gene_name &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 56143 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_10 | 10 | chr3:80740959-80741198 | - | CE | Gria2 |\n",
       "| 56148 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_12 | 12 | chr3:80726832-80726885 | - | CE | Gria2 |\n",
       "| 56144 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_13 | 13 | chr3:80722032-80722193 | - | CE | Gria2 |\n",
       "| 56145 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_14 | 14 | chr3:80710677-80710844 | - | CE | Gria2 |\n",
       "| 56150 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_15 | 15 | chr3:80710290-80710394 | - | CE | Gria2 |\n",
       "| 56162 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_16 | 16 | chr3:80709378-80709488 | - | CE | Gria2 |\n",
       "| 56164 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_17 | 17 | chr3:80707699-80707905 | - | CE | Gria2 |\n",
       "| 56160 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_20 | 20 | chr3:80706888-80707258 | - | CE | Gria2 |\n",
       "| 56151 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_23 | 23 | chr3:80702458-80702656 | - | CE | Gria2 |\n",
       "| 56166 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_24 | 24 | chr3:80692285-80692532 | - | CE | Gria2 |\n",
       "| 56146 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_26 | 26 | chr3:80691703-80691743 | - | CE | Gria2 |\n",
       "| 56141 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_28 | 28 | chr3:80691320-80691434 | - | CE | Gria2 |\n",
       "| 56152 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_29 | 29 | chr3:80690404-80690518 | - | CE | Gria2 |\n",
       "| 56158 | ENSMUSG00000033981 | ENSMUSG00000033981.14 | ENSMUSG00000033981.14_30 | 30 | chr3:80689103-80689351 | - | CE | Gria2 |\n",
       "\n"
      ],
      "text/plain": [
       "      gene               gene_id               node_id                 \n",
       "56143 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_10\n",
       "56148 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_12\n",
       "56144 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_13\n",
       "56145 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_14\n",
       "56150 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_15\n",
       "56162 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_16\n",
       "56164 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_17\n",
       "56160 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_20\n",
       "56151 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_23\n",
       "56166 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_24\n",
       "56146 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_26\n",
       "56141 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_28\n",
       "56152 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_29\n",
       "56158 ENSMUSG00000033981 ENSMUSG00000033981.14 ENSMUSG00000033981.14_30\n",
       "      node_num coord                  strand type gene_name\n",
       "56143 10       chr3:80740959-80741198 -      CE   Gria2    \n",
       "56148 12       chr3:80726832-80726885 -      CE   Gria2    \n",
       "56144 13       chr3:80722032-80722193 -      CE   Gria2    \n",
       "56145 14       chr3:80710677-80710844 -      CE   Gria2    \n",
       "56150 15       chr3:80710290-80710394 -      CE   Gria2    \n",
       "56162 16       chr3:80709378-80709488 -      CE   Gria2    \n",
       "56164 17       chr3:80707699-80707905 -      CE   Gria2    \n",
       "56160 20       chr3:80706888-80707258 -      CE   Gria2    \n",
       "56151 23       chr3:80702458-80702656 -      CE   Gria2    \n",
       "56166 24       chr3:80692285-80692532 -      CE   Gria2    \n",
       "56146 26       chr3:80691703-80691743 -      CE   Gria2    \n",
       "56141 28       chr3:80691320-80691434 -      CE   Gria2    \n",
       "56152 29       chr3:80690404-80690518 -      CE   Gria2    \n",
       "56158 30       chr3:80689103-80689351 -      CE   Gria2    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# since we are observing glutamatergic neurons, Gria2 (Glutamate Ionotropic Receptor AMPA Type Subunit 2) looks familiar\n",
    "# so what core exons we have for this gene?\n",
    "\n",
    "Gria2_nodes <- geneNodes(index.glu, \"Gria2\", \"gene_name\")\n",
    "Gria2_nodes <- Gria2_nodes[order(as.numeric(as.character(Gria2_nodes$node_num))), ]\n",
    "\n",
    "# select core exons to observe\n",
    "Gria2_CE <- Gria2_nodes[which(Gria2_nodes$type == \"CE\"), ]\n",
    "Gria2_CE <- Gria2_CE[order(as.numeric(as.character(Gria2_CE$node_num))), ]\n",
    "Gria2_CE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mean</th><th scope=col>SD</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ENSMUSG00000033981.14_28</th><td>0.7369618</td><td>0.3424049</td></tr>\n",
       "\t<tr><th scope=row>ENSMUSG00000033981.14_29</th><td>0.2722713</td><td>0.3452548</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & mean & SD\\\\\n",
       "  & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tENSMUSG00000033981.14\\_28 & 0.7369618 & 0.3424049\\\\\n",
       "\tENSMUSG00000033981.14\\_29 & 0.2722713 & 0.3452548\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 2\n",
       "\n",
       "| <!--/--> | mean &lt;dbl&gt; | SD &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| ENSMUSG00000033981.14_28 | 0.7369618 | 0.3424049 |\n",
       "| ENSMUSG00000033981.14_29 | 0.2722713 | 0.3452548 |\n",
       "\n"
      ],
      "text/plain": [
       "                         mean      SD       \n",
       "ENSMUSG00000033981.14_28 0.7369618 0.3424049\n",
       "ENSMUSG00000033981.14_29 0.2722713 0.3452548"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets investiate node 28 and 29, two adjacent core exons\n",
    "# What is the mean and standard deviation of PSI values for node 28 and 29?\n",
    "\n",
    "Gria2_flip_flop <- c(\"ENSMUSG00000033981.14_28\", \"ENSMUSG00000033981.14_29\")\n",
    "Gria2_stats <- index.glu@metadata$stats[which(rownames(index.glu@metadata$stats) %in% Gria2_flip_flop), ]\n",
    "Gria2_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verified all query nodes are in index, generating results...\n",
      "\n",
      "Verified all query nodes are in index, generating results...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 18 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cell_type</th><th scope=col>cell_hits</th><th scope=col>total_cells</th><th scope=col>pval</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>glutamatergic neuron.L2/3 Ptgs2   </td><td> 1</td><td>94</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L4 Arf5      </td><td>42</td><td>44</td><td>7.323694e-07</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L4 Ctxn3     </td><td>69</td><td>79</td><td>3.601008e-06</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L4 Scnn1a    </td><td>91</td><td>98</td><td>4.638623e-12</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5 Chrna6    </td><td> 7</td><td> 8</td><td>3.605902e-01</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5 Ucma      </td><td>13</td><td>16</td><td>4.421327e-01</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5a Batf3    </td><td>58</td><td>64</td><td>1.265488e-06</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5a Hsd11b1  </td><td>47</td><td>53</td><td>8.791229e-05</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5a Pde1c    </td><td>15</td><td>19</td><td>5.263691e-01</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5a Tcerg1l  </td><td>29</td><td>35</td><td>4.905504e-02</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5b Cdh13    </td><td>12</td><td>32</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L5b Tph2     </td><td>20</td><td>27</td><td>9.228856e-01</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L6a Car12    </td><td> 3</td><td>20</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L6a Mgp      </td><td>19</td><td>41</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L6a Sla      </td><td>39</td><td>62</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L6a Syt17    </td><td> 3</td><td>13</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L6b Rgs12    </td><td> 2</td><td>15</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><td>glutamatergic neuron.L6b Serpinb11</td><td> 4</td><td>20</td><td>1.000000e+00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 18 × 4\n",
       "\\begin{tabular}{llll}\n",
       " cell\\_type & cell\\_hits & total\\_cells & pval\\\\\n",
       " <fct> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t glutamatergic neuron.L2/3 Ptgs2    &  1 & 94 & 1.000000e+00\\\\\n",
       "\t glutamatergic neuron.L4 Arf5       & 42 & 44 & 7.323694e-07\\\\\n",
       "\t glutamatergic neuron.L4 Ctxn3      & 69 & 79 & 3.601008e-06\\\\\n",
       "\t glutamatergic neuron.L4 Scnn1a     & 91 & 98 & 4.638623e-12\\\\\n",
       "\t glutamatergic neuron.L5 Chrna6     &  7 &  8 & 3.605902e-01\\\\\n",
       "\t glutamatergic neuron.L5 Ucma       & 13 & 16 & 4.421327e-01\\\\\n",
       "\t glutamatergic neuron.L5a Batf3     & 58 & 64 & 1.265488e-06\\\\\n",
       "\t glutamatergic neuron.L5a Hsd11b1   & 47 & 53 & 8.791229e-05\\\\\n",
       "\t glutamatergic neuron.L5a Pde1c     & 15 & 19 & 5.263691e-01\\\\\n",
       "\t glutamatergic neuron.L5a Tcerg1l   & 29 & 35 & 4.905504e-02\\\\\n",
       "\t glutamatergic neuron.L5b Cdh13     & 12 & 32 & 1.000000e+00\\\\\n",
       "\t glutamatergic neuron.L5b Tph2      & 20 & 27 & 9.228856e-01\\\\\n",
       "\t glutamatergic neuron.L6a Car12     &  3 & 20 & 1.000000e+00\\\\\n",
       "\t glutamatergic neuron.L6a Mgp       & 19 & 41 & 1.000000e+00\\\\\n",
       "\t glutamatergic neuron.L6a Sla       & 39 & 62 & 1.000000e+00\\\\\n",
       "\t glutamatergic neuron.L6a Syt17     &  3 & 13 & 1.000000e+00\\\\\n",
       "\t glutamatergic neuron.L6b Rgs12     &  2 & 15 & 1.000000e+00\\\\\n",
       "\t glutamatergic neuron.L6b Serpinb11 &  4 & 20 & 1.000000e+00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 18 × 4\n",
       "\n",
       "| cell_type &lt;fct&gt; | cell_hits &lt;int&gt; | total_cells &lt;dbl&gt; | pval &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| glutamatergic neuron.L2/3 Ptgs2    |  1 | 94 | 1.000000e+00 |\n",
       "| glutamatergic neuron.L4 Arf5       | 42 | 44 | 7.323694e-07 |\n",
       "| glutamatergic neuron.L4 Ctxn3      | 69 | 79 | 3.601008e-06 |\n",
       "| glutamatergic neuron.L4 Scnn1a     | 91 | 98 | 4.638623e-12 |\n",
       "| glutamatergic neuron.L5 Chrna6     |  7 |  8 | 3.605902e-01 |\n",
       "| glutamatergic neuron.L5 Ucma       | 13 | 16 | 4.421327e-01 |\n",
       "| glutamatergic neuron.L5a Batf3     | 58 | 64 | 1.265488e-06 |\n",
       "| glutamatergic neuron.L5a Hsd11b1   | 47 | 53 | 8.791229e-05 |\n",
       "| glutamatergic neuron.L5a Pde1c     | 15 | 19 | 5.263691e-01 |\n",
       "| glutamatergic neuron.L5a Tcerg1l   | 29 | 35 | 4.905504e-02 |\n",
       "| glutamatergic neuron.L5b Cdh13     | 12 | 32 | 1.000000e+00 |\n",
       "| glutamatergic neuron.L5b Tph2      | 20 | 27 | 9.228856e-01 |\n",
       "| glutamatergic neuron.L6a Car12     |  3 | 20 | 1.000000e+00 |\n",
       "| glutamatergic neuron.L6a Mgp       | 19 | 41 | 1.000000e+00 |\n",
       "| glutamatergic neuron.L6a Sla       | 39 | 62 | 1.000000e+00 |\n",
       "| glutamatergic neuron.L6a Syt17     |  3 | 13 | 1.000000e+00 |\n",
       "| glutamatergic neuron.L6b Rgs12     |  2 | 15 | 1.000000e+00 |\n",
       "| glutamatergic neuron.L6b Serpinb11 |  4 | 20 | 1.000000e+00 |\n",
       "\n"
      ],
      "text/plain": [
       "   cell_type                          cell_hits total_cells pval        \n",
       "1  glutamatergic neuron.L2/3 Ptgs2     1        94          1.000000e+00\n",
       "2  glutamatergic neuron.L4 Arf5       42        44          7.323694e-07\n",
       "3  glutamatergic neuron.L4 Ctxn3      69        79          3.601008e-06\n",
       "4  glutamatergic neuron.L4 Scnn1a     91        98          4.638623e-12\n",
       "5  glutamatergic neuron.L5 Chrna6      7         8          3.605902e-01\n",
       "6  glutamatergic neuron.L5 Ucma       13        16          4.421327e-01\n",
       "7  glutamatergic neuron.L5a Batf3     58        64          1.265488e-06\n",
       "8  glutamatergic neuron.L5a Hsd11b1   47        53          8.791229e-05\n",
       "9  glutamatergic neuron.L5a Pde1c     15        19          5.263691e-01\n",
       "10 glutamatergic neuron.L5a Tcerg1l   29        35          4.905504e-02\n",
       "11 glutamatergic neuron.L5b Cdh13     12        32          1.000000e+00\n",
       "12 glutamatergic neuron.L5b Tph2      20        27          9.228856e-01\n",
       "13 glutamatergic neuron.L6a Car12      3        20          1.000000e+00\n",
       "14 glutamatergic neuron.L6a Mgp       19        41          1.000000e+00\n",
       "15 glutamatergic neuron.L6a Sla       39        62          1.000000e+00\n",
       "16 glutamatergic neuron.L6a Syt17      3        13          1.000000e+00\n",
       "17 glutamatergic neuron.L6b Rgs12      2        15          1.000000e+00\n",
       "18 glutamatergic neuron.L6b Serpinb11  4        20          1.000000e+00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the central operation carried out by scfindME is to identify the set of cells that express a set of nodes specified by the \n",
    "# lets query cell types using node 28 and node 29 of Gria2\n",
    "# p-values for significance are calculated by the hypergeometric test\n",
    "\n",
    "enriched_cell_types_28 <- hyperQueryCellTypesAS(index.glu, c(\"ENSMUSG00000033981.14_28\"))\n",
    "enriched_cell_types_29 <- hyperQueryCellTypesAS(index.glu, c(\"ENSMUSG00000033981.14_29\"))\n",
    "enriched_cell_types_28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some data manipulations for plotting\n",
    "enriched_cell_types_28$node <- as.character(rep(28))\n",
    "enriched_cell_types_29$node <- as.character(rep(29))\n",
    "\n",
    "Gria2_flipflop_cell_types <- rbind(enriched_cell_types_28, enriched_cell_types_29)\n",
    "Gria2_flipflop_cell_types$type <- gsub(\"glutamatergic neuron.\", \"\", as.character(Gria2_flipflop_cell_types$cell_type))\n",
    "\n",
    "# for very significant cell types, p value = 0, add 0.01 to plot them\n",
    "Gria2_flipflop_cell_types$logp <- -log10(Gria2_flipflop_cell_types$pval + 0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 19 rows containing missing values (geom_bar).”\n"
     ]
    },
    {
     "data": {
      "image/png": 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OFxkZuWPHDr6jNYhsUwehyM/PJzjUf+rUqQEBAQSONW0AABNHSURBVGFhYdxozOrq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\nIAQAAACAIvU3s1El+YoRoBQAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visulize cell types by their significance of splicing-in the two nodes\n",
    "\n",
    "ggplot(Gria2_flipflop_cell_types, aes(x = type, y = logp, fill = node)) + \n",
    "    geom_bar(stat = \"identity\", position = \"dodge\") + \n",
    "    labs(x = \"cell type\", \n",
    "         y = \"-log10 (pval)\", \n",
    "         title = \"Gria2 flip-flop exons inclusion patterns\") + \n",
    "    scale_x_discrete(guide = guide_axis(angle = 90)) + \n",
    "    ylim(0, 2) + \n",
    "    # add a 0.05 p value cutoff\n",
    "    geom_abline(intercept = -log10(0.05), \n",
    "                slope = 0, \n",
    "                colour = \"red\", \n",
    "                size = 0.5)\n",
    "fig(10, 6)\n",
    "\n",
    "# the query shows that L4 and L5 cells have an enrichment of splicing in node 28, while L2 and L6 cells choose node 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve actual PSI for discovered splicing patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>primary_type</th><th scope=col>cell_num</th><th scope=col>node_name</th><th scope=col>PSI</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1</td><td>Gria2_1</td><td>0.0000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1</td><td>Gria2_2</td><td>0.0000</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1</td><td>Gria2_3</td><td>0.0000</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1</td><td>Gria2_4</td><td>0.5064</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1</td><td>Gria2_5</td><td>0.0007</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1</td><td>Gria2_6</td><td>0.3832</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & primary\\_type & cell\\_num & node\\_name & PSI\\\\\n",
       "  & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1 & Gria2\\_1 & 0.0000\\\\\n",
       "\t2 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1 & Gria2\\_2 & 0.0000\\\\\n",
       "\t3 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1 & Gria2\\_3 & 0.0000\\\\\n",
       "\t4 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1 & Gria2\\_4 & 0.5064\\\\\n",
       "\t5 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1 & Gria2\\_5 & 0.0007\\\\\n",
       "\t6 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1 & Gria2\\_6 & 0.3832\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | primary_type &lt;chr&gt; | cell_num &lt;chr&gt; | node_name &lt;chr&gt; | PSI &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | L2/3_Ptgs2 | L2/3_Ptgs2_1 | Gria2_1 | 0.0000 |\n",
       "| 2 | L2/3_Ptgs2 | L2/3_Ptgs2_1 | Gria2_2 | 0.0000 |\n",
       "| 3 | L2/3_Ptgs2 | L2/3_Ptgs2_1 | Gria2_3 | 0.0000 |\n",
       "| 4 | L2/3_Ptgs2 | L2/3_Ptgs2_1 | Gria2_4 | 0.5064 |\n",
       "| 5 | L2/3_Ptgs2 | L2/3_Ptgs2_1 | Gria2_5 | 0.0007 |\n",
       "| 6 | L2/3_Ptgs2 | L2/3_Ptgs2_1 | Gria2_6 | 0.3832 |\n",
       "\n"
      ],
      "text/plain": [
       "  primary_type cell_num     node_name PSI   \n",
       "1 L2/3_Ptgs2   L2/3_Ptgs2_1 Gria2_1   0.0000\n",
       "2 L2/3_Ptgs2   L2/3_Ptgs2_1 Gria2_2   0.0000\n",
       "3 L2/3_Ptgs2   L2/3_Ptgs2_1 Gria2_3   0.0000\n",
       "4 L2/3_Ptgs2   L2/3_Ptgs2_1 Gria2_4   0.5064\n",
       "5 L2/3_Ptgs2   L2/3_Ptgs2_1 Gria2_5   0.0007\n",
       "6 L2/3_Ptgs2   L2/3_Ptgs2_1 Gria2_6   0.3832"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## now we retrieve the actual PSI values using pesudo-bulk data\n",
    "## here, 5 single cells are merged to create a pseudo-bulk\n",
    "\n",
    "Gria2_PSI <- readRDS(\"~/Gria2_PSI.rds\")\n",
    "head(Gria2_PSI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to visulize the ground-truth of PSI values for Gria2 node 28 and 29\n",
    "Gria2_PSI_flipflop <- Gria2_PSI[which(Gria2_PSI$node_name %in% c(\"Gria2_28\", \"Gria2_29\")), ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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duHFjR0dH2CsCHQAAIABJREFUe3t7T0/PmDFjjh8/jhBKSEi4ffv2rl27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\nxqNHj3LV1ry8v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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use boxplot to show the PSI values of the discovered flip-flop exon pair among cell types\n",
    "\n",
    "ggplot(Gria2_PSI_flipflop, \n",
    "       aes(x = primary_type, \n",
    "           y = PSI, \n",
    "           fill = node_name)) + \n",
    "    geom_boxplot(varwidth = T, \n",
    "                 na.rm = TRUE) + \n",
    "    scale_x_discrete(guide = guide_axis(angle = 90)) + \n",
    "    labs(title=\"Actual PSI values from 5-cell pseudobulks\", \n",
    "         x=\"primary cell type\",\n",
    "         y=\"PSI\")\n",
    "fig(18, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### comprehensive splicing patterns of interested gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>primary_type</th><th scope=col>cell_num</th><th scope=col>node_name</th><th scope=col>PSI</th><th scope=col>node_num</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1 </td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>28</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_10</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>55</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_11</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>82</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_12</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>109</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_13</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>136</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_14</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & primary\\_type & cell\\_num & node\\_name & PSI & node\\_num\\\\\n",
       "  & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1  & Gria2\\_1 & 0 & 1\\\\\n",
       "\t28 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_10 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t55 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_11 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t82 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_12 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t109 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_13 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t136 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_14 & Gria2\\_1 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | primary_type &lt;chr&gt; | cell_num &lt;chr&gt; | node_name &lt;chr&gt; | PSI &lt;dbl&gt; | node_num &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | L2/3_Ptgs2 | L2/3_Ptgs2_1  | Gria2_1 | 0 | 1 |\n",
       "| 28 | L2/3_Ptgs2 | L2/3_Ptgs2_10 | Gria2_1 | 0 | 1 |\n",
       "| 55 | L2/3_Ptgs2 | L2/3_Ptgs2_11 | Gria2_1 | 0 | 1 |\n",
       "| 82 | L2/3_Ptgs2 | L2/3_Ptgs2_12 | Gria2_1 | 0 | 1 |\n",
       "| 109 | L2/3_Ptgs2 | L2/3_Ptgs2_13 | Gria2_1 | 0 | 1 |\n",
       "| 136 | L2/3_Ptgs2 | L2/3_Ptgs2_14 | Gria2_1 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "    primary_type cell_num      node_name PSI node_num\n",
       "1   L2/3_Ptgs2   L2/3_Ptgs2_1  Gria2_1   0   1       \n",
       "28  L2/3_Ptgs2   L2/3_Ptgs2_10 Gria2_1   0   1       \n",
       "55  L2/3_Ptgs2   L2/3_Ptgs2_11 Gria2_1   0   1       \n",
       "82  L2/3_Ptgs2   L2/3_Ptgs2_12 Gria2_1   0   1       \n",
       "109 L2/3_Ptgs2   L2/3_Ptgs2_13 Gria2_1   0   1       \n",
       "136 L2/3_Ptgs2   L2/3_Ptgs2_14 Gria2_1   0   1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to visulize the actual splicing events of all nodes in Gria2 gene\n",
    "\n",
    "Gria2_PSI$node_num <- as.numeric(gsub(\"Gria2_\", \"\", Gria2_PSI$node_name))\n",
    "Gria2_PSI <- Gria2_PSI[order(Gria2_PSI$node_num), ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>primary_type</th><th scope=col>cell_num</th><th scope=col>node_name</th><th scope=col>PSI</th><th scope=col>node_num</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_1 </td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>28</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_10</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>55</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_11</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>82</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_12</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>109</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_13</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>136</th><td>L2/3_Ptgs2</td><td>L2/3_Ptgs2_14</td><td>Gria2_1</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & primary\\_type & cell\\_num & node\\_name & PSI & node\\_num\\\\\n",
       "  & <chr> & <chr> & <fct> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_1  & Gria2\\_1 & 0 & 1\\\\\n",
       "\t28 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_10 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t55 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_11 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t82 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_12 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t109 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_13 & Gria2\\_1 & 0 & 1\\\\\n",
       "\t136 & L2/3\\_Ptgs2 & L2/3\\_Ptgs2\\_14 & Gria2\\_1 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | primary_type &lt;chr&gt; | cell_num &lt;chr&gt; | node_name &lt;fct&gt; | PSI &lt;dbl&gt; | node_num &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | L2/3_Ptgs2 | L2/3_Ptgs2_1  | Gria2_1 | 0 | 1 |\n",
       "| 28 | L2/3_Ptgs2 | L2/3_Ptgs2_10 | Gria2_1 | 0 | 1 |\n",
       "| 55 | L2/3_Ptgs2 | L2/3_Ptgs2_11 | Gria2_1 | 0 | 1 |\n",
       "| 82 | L2/3_Ptgs2 | L2/3_Ptgs2_12 | Gria2_1 | 0 | 1 |\n",
       "| 109 | L2/3_Ptgs2 | L2/3_Ptgs2_13 | Gria2_1 | 0 | 1 |\n",
       "| 136 | L2/3_Ptgs2 | L2/3_Ptgs2_14 | Gria2_1 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "    primary_type cell_num      node_name PSI node_num\n",
       "1   L2/3_Ptgs2   L2/3_Ptgs2_1  Gria2_1   0   1       \n",
       "28  L2/3_Ptgs2   L2/3_Ptgs2_10 Gria2_1   0   1       \n",
       "55  L2/3_Ptgs2   L2/3_Ptgs2_11 Gria2_1   0   1       \n",
       "82  L2/3_Ptgs2   L2/3_Ptgs2_12 Gria2_1   0   1       \n",
       "109 L2/3_Ptgs2   L2/3_Ptgs2_13 Gria2_1   0   1       \n",
       "136 L2/3_Ptgs2   L2/3_Ptgs2_14 Gria2_1   0   1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make factor and reorder the node_names for clarity\n",
    "\n",
    "Gria2_PSI$node_name <- factor(Gria2_PSI$node_name, levels = unique((Gria2_PSI$node_name)[order(Gria2_PSI$node_num)]))\n",
    "head(Gria2_PSI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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QnFAfRYgoZPHxSAAAIABJREFUIFMkBY4MGc37YV3PvvIGRXtO1loW8vuU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\n2suEEJKenu7p6dm1jCESiby8vGpra8+dO8dRQYVWFYdFq8bAv5iYmIK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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finally, we can use heatmaps to show the splicing pattern of Gria2 across all pseudo-bulk cells\n",
    "\n",
    "ggplot(Gria2_PSI, aes(x = primary_type, \n",
    "                      y = node_name)) +\n",
    "    geom_tile(aes(fill = PSI), \n",
    "             na.rm = TRUE) + \n",
    "    scale_fill_gradient(low = \"white\",\n",
    "                        high = \"steelblue\") +\n",
    "    scale_x_discrete(guide = guide_axis(angle = 90)) + \n",
    "    labs(title=\"Actual PSI values from 5-cell pseudobulks of Gria2 gene\", \n",
    "         x=\"primary cell type\",\n",
    "         y=\"node name\")\n",
    "fig(18, 8)\n",
    "\n",
    "# some nodes are undetected in a cell type, resulting in NA values\n",
    "# this might be due to experimental artifacts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.6.3 (2020-02-29)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 18.04.3 LTS\n",
       "\n",
       "Matrix products: default\n",
       "BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1\n",
       "LAPACK: /usr/lib/x86_64-linux-gnu/openblas/liblapack.so.3\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] repr_1.1.0     ggplot2_3.3.2  tidyr_1.1.2    dplyr_1.0.0    tibble_3.0.4  \n",
       "[6] scfindME_0.1.0\n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] bitops_1.0-6                matrixStats_0.56.0         \n",
       " [3] fs_1.4.2                    usethis_1.6.3              \n",
       " [5] devtools_2.3.1              bit64_0.9-7.1              \n",
       " [7] progress_1.2.2              httr_1.4.2                 \n",
       " [9] rprojroot_2.0.2             GenomeInfoDb_1.22.1        \n",
       "[11] tools_3.6.3                 R6_2.5.0                   \n",
       "[13] colorspace_2.0-0            DBI_1.1.0                  \n",
       "[15] BiocGenerics_0.32.0         withr_2.3.0                \n",
       "[17] tidyselect_1.1.0            prettyunits_1.1.1          \n",
       "[19] processx_3.4.5              bit_1.1-15.2               \n",
       "[21] curl_4.3                    compiler_3.6.3             \n",
       "[23] cli_2.2.0                   Biobase_2.46.0             \n",
       "[25] desc_1.2.0                  DelayedArray_0.12.3        \n",
       "[27] labeling_0.4.2              scales_1.1.1               \n",
       "[29] callr_3.5.1                 askpass_1.1                \n",
       "[31] rappdirs_0.3.1              pbdZMQ_0.3-3               \n",
       "[33] stringr_1.4.0               digest_0.6.27              \n",
       "[35] XVector_0.26.0              base64enc_0.1-3            \n",
       "[37] pkgconfig_2.0.3             htmltools_0.5.0            \n",
       "[39] sessioninfo_1.1.1           dbplyr_1.4.4               \n",
       "[41] rlang_0.4.9                 RSQLite_2.2.0              \n",
       "[43] farver_2.0.3                generics_0.0.2             \n",
       "[45] jsonlite_1.7.1              BiocParallel_1.20.1        \n",
       "[47] RCurl_1.98-1.2              magrittr_2.0.1             \n",
       "[49] GenomeInfoDbData_1.2.2      Matrix_1.2-18              \n",
       "[51] Rcpp_1.0.5                  IRkernel_1.1.1             \n",
       "[53] munsell_0.5.0               S4Vectors_0.24.4           \n",
       "[55] fansi_0.4.1                 lifecycle_0.2.0            \n",
       "[57] stringi_1.4.6               SummarizedExperiment_1.16.1\n",
       "[59] zlibbioc_1.32.0             pkgbuild_1.1.0             \n",
       "[61] BiocFileCache_1.10.2        grid_3.6.3                 \n",
       "[63] blob_1.2.1                  parallel_3.6.3             \n",
       "[65] crayon_1.3.4                lattice_0.20-40            \n",
       "[67] IRdisplay_0.7.0             hash_2.2.6.1               \n",
       "[69] hms_0.5.3                   ps_1.4.0                   \n",
       "[71] pillar_1.4.7                GenomicRanges_1.38.0       \n",
       "[73] uuid_0.1-4                  codetools_0.2-16           \n",
       "[75] biomaRt_2.42.1              stats4_3.6.3               \n",
       "[77] pkgload_1.1.0               XML_3.99-0                 \n",
       "[79] glue_1.4.2                  evaluate_0.14              \n",
       "[81] remotes_2.2.0               vctrs_0.3.5                \n",
       "[83] testthat_3.0.0              gtable_0.3.0               \n",
       "[85] openssl_1.4.2               purrr_0.3.4                \n",
       "[87] assertthat_0.2.1            SingleCellExperiment_1.8.0 \n",
       "[89] AnnotationDbi_1.48.0        memoise_1.1.0              \n",
       "[91] IRanges_2.20.2              ellipsis_0.3.1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for reproducibility\n",
    "\n",
    "sessionInfo()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "R",
   "language": "R",
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   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
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