class15_MCODE_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Class 15 - MCODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(sna))\n",
    "library(readr)\n",
    "suppressPackageStartupMessages(library(igraph))\n",
    "suppressPackageStartupMessages(library(expm))\n",
    "suppressPackageStartupMessages(library(ProNet))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IGRAPH 59155dd UN-- 13 26 -- \n",
      "+ attr: name (v/c)\n"
     ]
    }
   ],
   "source": [
    "test_adjmat <- read.dot(\"testr.dot\")\n",
    "test_adjmat <- test_adjmat + t(test_adjmat)\n",
    "test_igraph <- igraph::graph_from_adjacency_matrix(test_adjmat, mode=\"undirected\")\n",
    "summary(test_igraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WPGl19+aWLts3ADKdX00rQzToMbP/ptlvbAuaLO4kSq35rH/8cz0tQ003Pf0bz5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",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(test_igraph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run ProNet::mcode on your test graph with the default parameters; what cluster sizes do you get back?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$COMPLEX</dt>\n",
       "\t\t<dd><ol>\n",
       "\t<li><ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>2</li>\n",
       "\t<li>3</li>\n",
       "\t<li>4</li>\n",
       "\t<li>5</li>\n",
       "\t<li>6</li>\n",
       "\t<li>7</li>\n",
       "</ol>\n",
       "</li>\n",
       "\t<li><ol class=list-inline>\n",
       "\t<li>9</li>\n",
       "\t<li>10</li>\n",
       "\t<li>11</li>\n",
       "\t<li>12</li>\n",
       "\t<li>13</li>\n",
       "</ol>\n",
       "</li>\n",
       "</ol>\n",
       "</dd>\n",
       "\t<dt>$score</dt>\n",
       "\t\t<dd><ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>4.5</li>\n",
       "</ol>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$COMPLEX] \\begin{enumerate}\n",
       "\\item \\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
       "\\item 4\n",
       "\\item 5\n",
       "\\item 6\n",
       "\\item 7\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item \\begin{enumerate*}\n",
       "\\item 9\n",
       "\\item 10\n",
       "\\item 11\n",
       "\\item 12\n",
       "\\item 13\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{enumerate}\n",
       "\n",
       "\\item[\\$score] \\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 4.5\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$COMPLEX\n",
       ":   1. 1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "5. 5\n",
       "6. 6\n",
       "7. 7\n",
       "\n",
       "\n",
       "\n",
       "2. 1. 9\n",
       "2. 10\n",
       "3. 11\n",
       "4. 12\n",
       "5. 13\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "$score\n",
       ":   1. 5\n",
       "2. 4.5\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$COMPLEX\n",
       "$COMPLEX[[1]]\n",
       "[1] 1 2 3 4 5 6 7\n",
       "\n",
       "$COMPLEX[[2]]\n",
       "[1]  9 10 11 12 13\n",
       "\n",
       "\n",
       "$score\n",
       "[1] 5.0 4.5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ProNet::mcode(test_igraph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Krogan et al. network edge-list as a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>protein1</th><th scope=col>protein2</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>AAC3 </td><td>PMR1 </td></tr>\n",
       "\t<tr><td>AAP1 </td><td>GUD1 </td></tr>\n",
       "\t<tr><td>AAP1 </td><td>TRS31</td></tr>\n",
       "\t<tr><td>AAP1 </td><td>GCN3 </td></tr>\n",
       "\t<tr><td>AAP1 </td><td>MDY2 </td></tr>\n",
       "\t<tr><td>AAR2 </td><td>SMD1 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " protein1 & protein2\\\\\n",
       "\\hline\n",
       "\t AAC3  & PMR1 \\\\\n",
       "\t AAP1  & GUD1 \\\\\n",
       "\t AAP1  & TRS31\\\\\n",
       "\t AAP1  & GCN3 \\\\\n",
       "\t AAP1  & MDY2 \\\\\n",
       "\t AAR2  & SMD1 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "protein1 | protein2 | \n",
       "|---|---|---|---|---|---|\n",
       "| AAC3  | PMR1  | \n",
       "| AAP1  | GUD1  | \n",
       "| AAP1  | TRS31 | \n",
       "| AAP1  | GCN3  | \n",
       "| AAP1  | MDY2  | \n",
       "| AAR2  | SMD1  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  protein1 protein2\n",
       "1 AAC3     PMR1    \n",
       "2 AAP1     GUD1    \n",
       "3 AAP1     TRS31   \n",
       "4 AAP1     GCN3    \n",
       "5 AAP1     MDY2    \n",
       "6 AAR2     SMD1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_list_df <- unique(readr::read_tsv(\"krogan.sif\", \n",
    "                              col_names=c(\"protein1\",\"protein2\"),\n",
    "                              col_types=cols()))\n",
    "\n",
    "head(edge_list_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make an igraph graph out of your edge-list data frame (undirected); print a summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IGRAPH 2294070 UN-- 2674 7079 -- \n",
      "+ attr: name (v/c)\n"
     ]
    }
   ],
   "source": [
    "big_graph <- igraph::graph_from_data_frame(edge_list_df, directed=FALSE)\n",
    "summary(big_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the degree distribution of your graph, on log-log scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l7vyU8Hfis2gZAPjOOCOw8xv+Bm8hiraalt0RE2DNNEd7kV7L694QpCXtIlkt/c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\nOCiYcf4fJdImTCelytYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressWarnings(plot(degree_distribution(big_graph), log=\"xy\", xlab=\"k\", ylab=\"Pk\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run ProNet::mcode on your graph, with vwp=0.2 and fdt=0.1; print out the cluster sizes that you get bakc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1308</li>\n",
       "\t<li>80</li>\n",
       "\t<li>70</li>\n",
       "\t<li>61</li>\n",
       "\t<li>43</li>\n",
       "\t<li>41</li>\n",
       "\t<li>40</li>\n",
       "\t<li>33</li>\n",
       "\t<li>28</li>\n",
       "\t<li>23</li>\n",
       "\t<li>22</li>\n",
       "\t<li>22</li>\n",
       "\t<li>19</li>\n",
       "\t<li>17</li>\n",
       "\t<li>17</li>\n",
       "\t<li>16</li>\n",
       "\t<li>16</li>\n",
       "\t<li>14</li>\n",
       "\t<li>13</li>\n",
       "\t<li>13</li>\n",
       "\t<li>13</li>\n",
       "\t<li>11</li>\n",
       "\t<li>10</li>\n",
       "\t<li>10</li>\n",
       "\t<li>10</li>\n",
       "\t<li>10</li>\n",
       "\t<li>10</li>\n",
       "\t<li>9</li>\n",
       "\t<li>9</li>\n",
       "\t<li>9</li>\n",
       "\t<li>9</li>\n",
       "\t<li>9</li>\n",
       "\t<li>8</li>\n",
       "\t<li>8</li>\n",
       "\t<li>8</li>\n",
       "\t<li>7</li>\n",
       "\t<li>7</li>\n",
       "\t<li>7</li>\n",
       "\t<li>7</li>\n",
       "\t<li>7</li>\n",
       "\t<li>7</li>\n",
       "\t<li>6</li>\n",
       "\t<li>6</li>\n",
       "\t<li>6</li>\n",
       "\t<li>6</li>\n",
       "\t<li>6</li>\n",
       "\t<li>6</li>\n",
       "\t<li>6</li>\n",
       "\t<li>6</li>\n",
       "\t<li>5</li>\n",
       "\t<li>5</li>\n",
       "\t<li>5</li>\n",
       "\t<li>5</li>\n",
       "\t<li>5</li>\n",
       "\t<li>5</li>\n",
       "\t<li>5</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>4</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1308\n",
       "\\item 80\n",
       "\\item 70\n",
       "\\item 61\n",
       "\\item 43\n",
       "\\item 41\n",
       "\\item 40\n",
       "\\item 33\n",
       "\\item 28\n",
       "\\item 23\n",
       "\\item 22\n",
       "\\item 22\n",
       "\\item 19\n",
       "\\item 17\n",
       "\\item 17\n",
       "\\item 16\n",
       "\\item 16\n",
       "\\item 14\n",
       "\\item 13\n",
       "\\item 13\n",
       "\\item 13\n",
       "\\item 11\n",
       "\\item 10\n",
       "\\item 10\n",
       "\\item 10\n",
       "\\item 10\n",
       "\\item 10\n",
       "\\item 9\n",
       "\\item 9\n",
       "\\item 9\n",
       "\\item 9\n",
       "\\item 9\n",
       "\\item 8\n",
       "\\item 8\n",
       "\\item 8\n",
       "\\item 7\n",
       "\\item 7\n",
       "\\item 7\n",
       "\\item 7\n",
       "\\item 7\n",
       "\\item 7\n",
       "\\item 6\n",
       "\\item 6\n",
       "\\item 6\n",
       "\\item 6\n",
       "\\item 6\n",
       "\\item 6\n",
       "\\item 6\n",
       "\\item 6\n",
       "\\item 5\n",
       "\\item 5\n",
       "\\item 5\n",
       "\\item 5\n",
       "\\item 5\n",
       "\\item 5\n",
       "\\item 5\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 4\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1308\n",
       "2. 80\n",
       "3. 70\n",
       "4. 61\n",
       "5. 43\n",
       "6. 41\n",
       "7. 40\n",
       "8. 33\n",
       "9. 28\n",
       "10. 23\n",
       "11. 22\n",
       "12. 22\n",
       "13. 19\n",
       "14. 17\n",
       "15. 17\n",
       "16. 16\n",
       "17. 16\n",
       "18. 14\n",
       "19. 13\n",
       "20. 13\n",
       "21. 13\n",
       "22. 11\n",
       "23. 10\n",
       "24. 10\n",
       "25. 10\n",
       "26. 10\n",
       "27. 10\n",
       "28. 9\n",
       "29. 9\n",
       "30. 9\n",
       "31. 9\n",
       "32. 9\n",
       "33. 8\n",
       "34. 8\n",
       "35. 8\n",
       "36. 7\n",
       "37. 7\n",
       "38. 7\n",
       "39. 7\n",
       "40. 7\n",
       "41. 7\n",
       "42. 6\n",
       "43. 6\n",
       "44. 6\n",
       "45. 6\n",
       "46. 6\n",
       "47. 6\n",
       "48. 6\n",
       "49. 6\n",
       "50. 5\n",
       "51. 5\n",
       "52. 5\n",
       "53. 5\n",
       "54. 5\n",
       "55. 5\n",
       "56. 5\n",
       "57. 4\n",
       "58. 4\n",
       "59. 4\n",
       "60. 4\n",
       "61. 4\n",
       "62. 4\n",
       "63. 4\n",
       "64. 4\n",
       "65. 4\n",
       "66. 4\n",
       "67. 4\n",
       "68. 4\n",
       "69. 4\n",
       "70. 4\n",
       "71. 4\n",
       "72. 4\n",
       "73. 4\n",
       "74. 3\n",
       "75. 3\n",
       "76. 3\n",
       "77. 3\n",
       "78. 3\n",
       "79. 3\n",
       "80. 3\n",
       "81. 3\n",
       "82. 3\n",
       "83. 3\n",
       "84. 3\n",
       "85. 3\n",
       "86. 3\n",
       "87. 3\n",
       "88. 3\n",
       "89. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 1308   80   70   61   43   41   40   33   28   23   22   22   19   17   17\n",
       "[16]   16   16   14   13   13   13   11   10   10   10   10   10    9    9    9\n",
       "[31]    9    9    8    8    8    7    7    7    7    7    7    6    6    6    6\n",
       "[46]    6    6    6    6    5    5    5    5    5    5    5    4    4    4    4\n",
       "[61]    4    4    4    4    4    4    4    4    4    4    4    4    4    3    3\n",
       "[76]    3    3    3    3    3    3    3    3    3    3    3    3    3    3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- ProNet::mcode(big_graph, haircut=TRUE, fluff=TRUE, vwp=0.2, fdt=0.1)\n",
    "s_vals <- sapply(res$COMPLEX, length)\n",
    "sort(s_vals, decreasing=TRUE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print a histogram of log N(S) vs. log cluster size S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in xy.coords(x, y, xlabel, ylabel, log):\n",
      "\"3 y values <= 0 omitted from logarithmic plot\""
     ]
    },
    {
     "data": {
      "image/png": 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NN8SUEwH+oDHXZ9jPKHw6zfMYRgNjAv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\nn2Te3EkAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_bins <- (0:15)*80/15\n",
    "hist_res <- hist(s_vals[-1],breaks=s_bins, plot=FALSE)\n",
    "plot(hist_res$mids, hist_res$counts, log=\"y\", xlab=\"numprot\", ylab=\"Nclust(numprot)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}