seriate_dataframe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(seriation)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return a dataframe where 'col1' and 'col2' are factors with levels in order.\n",
    "seriate_dataframe <- function(d, col1, col2, value.var = \"percent\", fun.aggregate = NULL, method = \"BEA_TSP\") {\n",
    "  mat <- as.data.frame(data.table::dcast(\n",
    "    data      = d,\n",
    "    formula   = as.formula(sprintf(\"%s ~ %s\", col1, col2)),\n",
    "    value.var = value.var,\n",
    "    fun.aggregate = fun.aggregate\n",
    "  ))\n",
    "  rownames(mat) <- mat[[1]]\n",
    "  mat[[1]] <- NULL\n",
    "  mat <- as.matrix(mat)\n",
    "  mat[is.na(mat)] <- 0\n",
    "  mat_order <- seriation::seriate(mat, method = method)\n",
    "  d[[col1]] <- factor(as.character(d[[col1]]), rownames(mat)[mat_order[[1]]])\n",
    "  d[[col2]] <- factor(as.character(d[[col2]]), colnames(mat)[mat_order[[2]]])\n",
    "  return(d)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>gene</th><th scope=col>coef</th><th scope=col>logfc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>ABC      </td><td>C1       </td><td>0.9148060</td></tr>\n",
       "\t<tr><td>DEF      </td><td>C1       </td><td>0.9370754</td></tr>\n",
       "\t<tr><td>GHI      </td><td>C1       </td><td>0.2861395</td></tr>\n",
       "\t<tr><td>JKL      </td><td>C1       </td><td>0.8304476</td></tr>\n",
       "\t<tr><td>MNO      </td><td>C1       </td><td>0.6417455</td></tr>\n",
       "\t<tr><td>ABC      </td><td>C2       </td><td>0.5190959</td></tr>\n",
       "\t<tr><td>DEF      </td><td>C2       </td><td>0.7365883</td></tr>\n",
       "\t<tr><td>GHI      </td><td>C2       </td><td>0.1346666</td></tr>\n",
       "\t<tr><td>JKL      </td><td>C2       </td><td>0.6569923</td></tr>\n",
       "\t<tr><td>MNO      </td><td>C2       </td><td>0.7050648</td></tr>\n",
       "\t<tr><td>ABC      </td><td>C3       </td><td>0.4577418</td></tr>\n",
       "\t<tr><td>DEF      </td><td>C3       </td><td>0.7191123</td></tr>\n",
       "\t<tr><td>GHI      </td><td>C3       </td><td>0.9346722</td></tr>\n",
       "\t<tr><td>JKL      </td><td>C3       </td><td>0.2554288</td></tr>\n",
       "\t<tr><td>MNO      </td><td>C3       </td><td>0.4622928</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " gene & coef & logfc\\\\\n",
       "\\hline\n",
       "\t ABC       & C1        & 0.9148060\\\\\n",
       "\t DEF       & C1        & 0.9370754\\\\\n",
       "\t GHI       & C1        & 0.2861395\\\\\n",
       "\t JKL       & C1        & 0.8304476\\\\\n",
       "\t MNO       & C1        & 0.6417455\\\\\n",
       "\t ABC       & C2        & 0.5190959\\\\\n",
       "\t DEF       & C2        & 0.7365883\\\\\n",
       "\t GHI       & C2        & 0.1346666\\\\\n",
       "\t JKL       & C2        & 0.6569923\\\\\n",
       "\t MNO       & C2        & 0.7050648\\\\\n",
       "\t ABC       & C3        & 0.4577418\\\\\n",
       "\t DEF       & C3        & 0.7191123\\\\\n",
       "\t GHI       & C3        & 0.9346722\\\\\n",
       "\t JKL       & C3        & 0.2554288\\\\\n",
       "\t MNO       & C3        & 0.4622928\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "gene | coef | logfc | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| ABC       | C1        | 0.9148060 | \n",
       "| DEF       | C1        | 0.9370754 | \n",
       "| GHI       | C1        | 0.2861395 | \n",
       "| JKL       | C1        | 0.8304476 | \n",
       "| MNO       | C1        | 0.6417455 | \n",
       "| ABC       | C2        | 0.5190959 | \n",
       "| DEF       | C2        | 0.7365883 | \n",
       "| GHI       | C2        | 0.1346666 | \n",
       "| JKL       | C2        | 0.6569923 | \n",
       "| MNO       | C2        | 0.7050648 | \n",
       "| ABC       | C3        | 0.4577418 | \n",
       "| DEF       | C3        | 0.7191123 | \n",
       "| GHI       | C3        | 0.9346722 | \n",
       "| JKL       | C3        | 0.2554288 | \n",
       "| MNO       | C3        | 0.4622928 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene coef logfc    \n",
       "1  ABC  C1   0.9148060\n",
       "2  DEF  C1   0.9370754\n",
       "3  GHI  C1   0.2861395\n",
       "4  JKL  C1   0.8304476\n",
       "5  MNO  C1   0.6417455\n",
       "6  ABC  C2   0.5190959\n",
       "7  DEF  C2   0.7365883\n",
       "8  GHI  C2   0.1346666\n",
       "9  JKL  C2   0.6569923\n",
       "10 MNO  C2   0.7050648\n",
       "11 ABC  C3   0.4577418\n",
       "12 DEF  C3   0.7191123\n",
       "13 GHI  C3   0.9346722\n",
       "14 JKL  C3   0.2554288\n",
       "15 MNO  C3   0.4622928"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(42)\n",
    "d <- data.frame(\n",
    "  gene  = rep(c(\"ABC\", \"DEF\", \"GHI\", \"JKL\", \"MNO\"), 3),\n",
    "  coef  = rep(c(\"C1\", \"C2\", \"C3\"), each = 5),\n",
    "  logfc = runif(3 * 5)\n",
    ")\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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xf1POs+bW/H6oSQ1JO3CcWXdJ14amH9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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "options(repr.plot.width = 3, repr.plot.height = 3)\n",
    "\n",
    "ggplot(d, aes(x = coef, y = gene, fill = logfc)) +\n",
    "geom_tile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "d <- seriate_dataframe(d, \"gene\", \"coef\", \"logfc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LGDe5/Rq2TF+wo/ehwAxvimllPHui66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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "options(repr.plot.width = 3, repr.plot.height = 3)\n",
    "\n",
    "ggplot(d, aes(x = coef, y = gene, fill = logfc)) +\n",
    "geom_tile()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "",
   "name": ""
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}