{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial of singscore using built-in data\n",
    "\n",
    "#### Contributor: Antonio Mora, Chengshu Xie \n",
    "#### Date of first version: 2019-01-20\n",
    "#### Date of last review: 2020-05-26 \n",
    "#### Summary:\n",
    "\n",
    "This is the tutorial about using how to use R package `singscore`. We use the built-in data for this tutorial.\n",
    " . \n",
    "\n",
    "#### Contents:\n",
    "* [1. Data Preparation](#link1)    <br>\n",
    "    * [1.1 Prerequisites](#link2)     <br>\n",
    "    * [1.2 Load and check data](#link3)   <br>\n",
    "* [2. Method application](#link4)  <br>\n",
    "    * [2.1 Sample Scoring](#link5)     <br>\n",
    "* [3. Visualisation and diagnostic functions](#link6)\n",
    "    * [3.1 Plot Rank Densities](#link7)     <br>\n",
    "    * [3.2 Plot dispersions of scores](#link8) <br>\n",
    "    * [3.3 Plot score landscape](#link9)\n",
    "        \n",
    "    \n",
    "## <a id=link1>1. Data Preparation</a>\n",
    "\n",
    "### <a id=link2>1.1 Prerequisites</a>\n",
    "\n",
    "R package, `singscore`, needs to be installed and loaded in the R session, this can be done easily with the following chunk of code: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "install.packages(\"BiocManager\")\n",
    "BiocManager::install(\"singscore\")\n",
    "BiocManager::install(\"GSEABase\")\n",
    "\n",
    "suppressPackageStartupMessages(library(singscore))\n",
    "suppressPackageStartupMessages(library(GSEABase))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=link3>1.2 Load and check data</a>\n",
    "\n",
    "The datasets used in this vignette have been built within the package. The `tgfb_expr_10_se` dataset was obtained from (Foroutan et al. 2017) and it is a ten-sample subset of the original dataset. We are going to score the integrated TGFb-treated gene expression dataset (4 cases and 6 controls) against a TGFb gene signature with an up-regulated and down-regulated gene-set pair (Foroutan et al. 2017)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: SummarizedExperiment \n",
       "dim: 11900 10 \n",
       "metadata(0):\n",
       "assays(1): counts\n",
       "rownames(11900): 2 9 ... 729164 752014\n",
       "rowData names(0):\n",
       "colnames(10): D_Ctrl_R1 D_TGFb_R1 ... Hil_Ctrl_R1 Hil_Ctrl_R2\n",
       "colData names(1): Treatment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "setName: NA \n",
       "geneIds: 19, 87, ..., 402055 (total: 193)\n",
       "geneIdType: Null\n",
       "collectionType: Null \n",
       "details: use 'details(object)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "setName: NA \n",
       "geneIds: 136, 220, ..., 161291 (total: 108)\n",
       "geneIdType: Null\n",
       "collectionType: Null \n",
       "details: use 'details(object)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "193"
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       "193"
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       "193"
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      "text/plain": [
       "[1] 193"
      ]
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     "output_type": "display_data"
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      "text/html": [
       "108"
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       "108"
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       "108"
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       "[1] 108"
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     "metadata": {},
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   "source": [
    "# The example expression dataset and gene signatures are included in the package\n",
    "# distribution, one can directly access them using the variable names\n",
    "\n",
    "# To see the description of 'tgfb_expr_10_se','tgfb_gs_up','tgfb_gs_dn', look at \n",
    "# Have a look at the object `tgfb_expr_10_se` containing gene expression data for 10 samples \n",
    "tgfb_expr_10_se\n",
    "\n",
    "# View what tgfb_gs_up/dn contains\n",
    "tgfb_gs_up           \n",
    "tgfb_gs_dn\n",
    "\n",
    "# Get the size of the gene sets\n",
    "length(GSEABase::geneIds(tgfb_gs_up))\n",
    "length(GSEABase::geneIds(tgfb_gs_dn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=link4>2. Method application</a>\n",
    "\n",
    "### <a id=link5>2.1 Sample Scoring</a>\n",
    "\n",
    "   To get the sample scores, rank the expression data via `rankGenes()` at first. And then use `simpleScore()` to compute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 10 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>D_Ctrl_R1</th><th scope=col>D_TGFb_R1</th><th scope=col>D_Ctrl_R2</th><th scope=col>D_TGFb_R2</th><th scope=col>Hes_Ctrl_R1</th><th scope=col>Hes_TGFb_R1</th><th scope=col>Hes_Ctrl_R2</th><th scope=col>Hes_TGFb_R2</th><th scope=col>Hil_Ctrl_R1</th><th scope=col>Hil_Ctrl_R2</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2</th><td> 1065</td><td> 1255</td><td> 1428</td><td>1269</td><td> 1252</td><td> 1570</td><td> 1188</td><td> 1122</td><td>1055</td><td> 1227</td></tr>\n",
       "\t<tr><th scope=row>9</th><td> 6688</td><td> 7611</td><td> 6454</td><td>6975</td><td> 6244</td><td> 7330</td><td> 7110</td><td> 7052</td><td>6247</td><td> 7315</td></tr>\n",
       "\t<tr><th scope=row>10</th><td> 1741</td><td> 1599</td><td> 1541</td><td>1686</td><td> 1777</td><td> 1638</td><td> 1713</td><td> 1418</td><td>1558</td><td> 1849</td></tr>\n",
       "\t<tr><th scope=row>12</th><td> 5441</td><td> 3682</td><td> 6495</td><td>5105</td><td> 3906</td><td> 5533</td><td> 4344</td><td> 7036</td><td>4845</td><td> 4830</td></tr>\n",
       "\t<tr><th scope=row>13</th><td> 3352</td><td> 3599</td><td> 3276</td><td>3459</td><td> 3337</td><td> 3042</td><td> 3288</td><td> 3906</td><td>3480</td><td> 3062</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>10442</td><td>10013</td><td>10146</td><td>9917</td><td>10382</td><td>10085</td><td>10061</td><td>10029</td><td>9731</td><td>10268</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 10 of type int\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & D\\_Ctrl\\_R1 & D\\_TGFb\\_R1 & D\\_Ctrl\\_R2 & D\\_TGFb\\_R2 & Hes\\_Ctrl\\_R1 & Hes\\_TGFb\\_R1 & Hes\\_Ctrl\\_R2 & Hes\\_TGFb\\_R2 & Hil\\_Ctrl\\_R1 & Hil\\_Ctrl\\_R2\\\\\n",
       "\\hline\n",
       "\t2 &  1065 &  1255 &  1428 & 1269 &  1252 &  1570 &  1188 &  1122 & 1055 &  1227\\\\\n",
       "\t9 &  6688 &  7611 &  6454 & 6975 &  6244 &  7330 &  7110 &  7052 & 6247 &  7315\\\\\n",
       "\t10 &  1741 &  1599 &  1541 & 1686 &  1777 &  1638 &  1713 &  1418 & 1558 &  1849\\\\\n",
       "\t12 &  5441 &  3682 &  6495 & 5105 &  3906 &  5533 &  4344 &  7036 & 4845 &  4830\\\\\n",
       "\t13 &  3352 &  3599 &  3276 & 3459 &  3337 &  3042 &  3288 &  3906 & 3480 &  3062\\\\\n",
       "\t14 & 10442 & 10013 & 10146 & 9917 & 10382 & 10085 & 10061 & 10029 & 9731 & 10268\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 10 of type int\n",
       "\n",
       "| <!--/--> | D_Ctrl_R1 | D_TGFb_R1 | D_Ctrl_R2 | D_TGFb_R2 | Hes_Ctrl_R1 | Hes_TGFb_R1 | Hes_Ctrl_R2 | Hes_TGFb_R2 | Hil_Ctrl_R1 | Hil_Ctrl_R2 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2 |  1065 |  1255 |  1428 | 1269 |  1252 |  1570 |  1188 |  1122 | 1055 |  1227 |\n",
       "| 9 |  6688 |  7611 |  6454 | 6975 |  6244 |  7330 |  7110 |  7052 | 6247 |  7315 |\n",
       "| 10 |  1741 |  1599 |  1541 | 1686 |  1777 |  1638 |  1713 |  1418 | 1558 |  1849 |\n",
       "| 12 |  5441 |  3682 |  6495 | 5105 |  3906 |  5533 |  4344 |  7036 | 4845 |  4830 |\n",
       "| 13 |  3352 |  3599 |  3276 | 3459 |  3337 |  3042 |  3288 |  3906 | 3480 |  3062 |\n",
       "| 14 | 10442 | 10013 | 10146 | 9917 | 10382 | 10085 | 10061 | 10029 | 9731 | 10268 |\n",
       "\n"
      ],
      "text/plain": [
       "   D_Ctrl_R1 D_TGFb_R1 D_Ctrl_R2 D_TGFb_R2 Hes_Ctrl_R1 Hes_TGFb_R1 Hes_Ctrl_R2\n",
       "2   1065      1255      1428     1269       1252        1570        1188      \n",
       "9   6688      7611      6454     6975       6244        7330        7110      \n",
       "10  1741      1599      1541     1686       1777        1638        1713      \n",
       "12  5441      3682      6495     5105       3906        5533        4344      \n",
       "13  3352      3599      3276     3459       3337        3042        3288      \n",
       "14 10442     10013     10146     9917      10382       10085       10061      \n",
       "   Hes_TGFb_R2 Hil_Ctrl_R1 Hil_Ctrl_R2\n",
       "2   1122       1055         1227      \n",
       "9   7052       6247         7315      \n",
       "10  1418       1558         1849      \n",
       "12  7036       4845         4830      \n",
       "13  3906       3480         3062      \n",
       "14 10029       9731        10268      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TotalScore</th><th scope=col>TotalDispersion</th><th scope=col>UpScore</th><th scope=col>UpDispersion</th><th scope=col>DownScore</th><th scope=col>DownDispersion</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&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>D_Ctrl_R1</th><td>-0.088097993</td><td>2867.348</td><td>0.06096415</td><td>3119.390</td><td>-0.14906214</td><td>2615.306</td></tr>\n",
       "\t<tr><th scope=row>D_TGFb_R1</th><td> 0.286994210</td><td>2217.970</td><td>0.24931565</td><td>2352.886</td><td> 0.03767856</td><td>2083.053</td></tr>\n",
       "\t<tr><th scope=row>D_Ctrl_R2</th><td>-0.098964086</td><td>2861.418</td><td>0.06841242</td><td>3129.769</td><td>-0.16737650</td><td>2593.067</td></tr>\n",
       "\t<tr><th scope=row>D_TGFb_R2</th><td> 0.270721958</td><td>2378.832</td><td>0.25035661</td><td>2470.012</td><td> 0.02036534</td><td>2287.652</td></tr>\n",
       "\t<tr><th scope=row>Hes_Ctrl_R1</th><td>-0.002084788</td><td>2746.146</td><td>0.08046490</td><td>3134.216</td><td>-0.08254969</td><td>2358.075</td></tr>\n",
       "\t<tr><th scope=row>Hes_TGFb_R1</th><td> 0.176122839</td><td>2597.515</td><td>0.22894035</td><td>2416.638</td><td>-0.05281751</td><td>2778.392</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & TotalScore & TotalDispersion & UpScore & UpDispersion & DownScore & DownDispersion\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tD\\_Ctrl\\_R1 & -0.088097993 & 2867.348 & 0.06096415 & 3119.390 & -0.14906214 & 2615.306\\\\\n",
       "\tD\\_TGFb\\_R1 &  0.286994210 & 2217.970 & 0.24931565 & 2352.886 &  0.03767856 & 2083.053\\\\\n",
       "\tD\\_Ctrl\\_R2 & -0.098964086 & 2861.418 & 0.06841242 & 3129.769 & -0.16737650 & 2593.067\\\\\n",
       "\tD\\_TGFb\\_R2 &  0.270721958 & 2378.832 & 0.25035661 & 2470.012 &  0.02036534 & 2287.652\\\\\n",
       "\tHes\\_Ctrl\\_R1 & -0.002084788 & 2746.146 & 0.08046490 & 3134.216 & -0.08254969 & 2358.075\\\\\n",
       "\tHes\\_TGFb\\_R1 &  0.176122839 & 2597.515 & 0.22894035 & 2416.638 & -0.05281751 & 2778.392\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 6\n",
       "\n",
       "| <!--/--> | TotalScore &lt;dbl&gt; | TotalDispersion &lt;dbl&gt; | UpScore &lt;dbl&gt; | UpDispersion &lt;dbl&gt; | DownScore &lt;dbl&gt; | DownDispersion &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| D_Ctrl_R1 | -0.088097993 | 2867.348 | 0.06096415 | 3119.390 | -0.14906214 | 2615.306 |\n",
       "| D_TGFb_R1 |  0.286994210 | 2217.970 | 0.24931565 | 2352.886 |  0.03767856 | 2083.053 |\n",
       "| D_Ctrl_R2 | -0.098964086 | 2861.418 | 0.06841242 | 3129.769 | -0.16737650 | 2593.067 |\n",
       "| D_TGFb_R2 |  0.270721958 | 2378.832 | 0.25035661 | 2470.012 |  0.02036534 | 2287.652 |\n",
       "| Hes_Ctrl_R1 | -0.002084788 | 2746.146 | 0.08046490 | 3134.216 | -0.08254969 | 2358.075 |\n",
       "| Hes_TGFb_R1 |  0.176122839 | 2597.515 | 0.22894035 | 2416.638 | -0.05281751 | 2778.392 |\n",
       "\n"
      ],
      "text/plain": [
       "            TotalScore   TotalDispersion UpScore    UpDispersion DownScore  \n",
       "D_Ctrl_R1   -0.088097993 2867.348        0.06096415 3119.390     -0.14906214\n",
       "D_TGFb_R1    0.286994210 2217.970        0.24931565 2352.886      0.03767856\n",
       "D_Ctrl_R2   -0.098964086 2861.418        0.06841242 3129.769     -0.16737650\n",
       "D_TGFb_R2    0.270721958 2378.832        0.25035661 2470.012      0.02036534\n",
       "Hes_Ctrl_R1 -0.002084788 2746.146        0.08046490 3134.216     -0.08254969\n",
       "Hes_TGFb_R1  0.176122839 2597.515        0.22894035 2416.638     -0.05281751\n",
       "            DownDispersion\n",
       "D_Ctrl_R1   2615.306      \n",
       "D_TGFb_R1   2083.053      \n",
       "D_Ctrl_R2   2593.067      \n",
       "D_TGFb_R2   2287.652      \n",
       "Hes_Ctrl_R1 2358.075      \n",
       "Hes_TGFb_R1 2778.392      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The recommended method for dealing with ties in ranking is 'min', \n",
    "# you can change by specifying 'tiesMethod' parameter for rankGenes function.\n",
    "rankData = rankGenes(tgfb_expr_10_se)\n",
    "head(rankData)\n",
    "\n",
    "# Given the ranked data and gene signature, simpleScore returns the scores and \n",
    "# dispersions for each sample\n",
    "scoredf = simpleScore(rankData, upSet = tgfb_gs_up, downSet = tgfb_gs_dn)\n",
    "head(scoredf)\n",
    "\n",
    "# To view more details of the simpleScore, use ?simpleScore\n",
    "# Note that, when only one gene set is available in a gene signature, one can \n",
    "# only input values for the upSet argument. In addition, a knownDirection \n",
    "# argument can be set to FALSE if the direction of the gene set is unknown.\n",
    "\n",
    "# simpleScore(rankData, upSet = tgfb_gs_up, knownDirection = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=link6>3. Visualisation and diagnostic functions</a> \n",
    "\n",
    "### <a id=link7>3.1 Plot Rank Densities</a> \n",
    "\n",
    "`plotRankDensity()` plots the ranks of genes in the gene-sets for a specific sample. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 1 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>D_TGFb_R1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2</th><td> 1255</td></tr>\n",
       "\t<tr><th scope=row>9</th><td> 7611</td></tr>\n",
       "\t<tr><th scope=row>10</th><td> 1599</td></tr>\n",
       "\t<tr><th scope=row>12</th><td> 3682</td></tr>\n",
       "\t<tr><th scope=row>13</th><td> 3599</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>10013</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 1 of type int\n",
       "\\begin{tabular}{r|l}\n",
       "  & D\\_TGFb\\_R1\\\\\n",
       "\\hline\n",
       "\t2 &  1255\\\\\n",
       "\t9 &  7611\\\\\n",
       "\t10 &  1599\\\\\n",
       "\t12 &  3682\\\\\n",
       "\t13 &  3599\\\\\n",
       "\t14 & 10013\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 1 of type int\n",
       "\n",
       "| <!--/--> | D_TGFb_R1 |\n",
       "|---|---|\n",
       "| 2 |  1255 |\n",
       "| 9 |  7611 |\n",
       "| 10 |  1599 |\n",
       "| 12 |  3682 |\n",
       "| 13 |  3599 |\n",
       "| 14 | 10013 |\n",
       "\n"
      ],
      "text/plain": [
       "   D_TGFb_R1\n",
       "2   1255    \n",
       "9   7611    \n",
       "10  1599    \n",
       "12  3682    \n",
       "13  3599    \n",
       "14 10013    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 10 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>D_Ctrl_R1</th><th scope=col>D_TGFb_R1</th><th scope=col>D_Ctrl_R2</th><th scope=col>D_TGFb_R2</th><th scope=col>Hes_Ctrl_R1</th><th scope=col>Hes_TGFb_R1</th><th scope=col>Hes_Ctrl_R2</th><th scope=col>Hes_TGFb_R2</th><th scope=col>Hil_Ctrl_R1</th><th scope=col>Hil_Ctrl_R2</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>6688</td><td>7611</td><td>6454</td><td>6975</td><td>6244</td><td>7330</td><td>7110</td><td>7052</td><td>6247</td><td>7315</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 10 of type int\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & D\\_Ctrl\\_R1 & D\\_TGFb\\_R1 & D\\_Ctrl\\_R2 & D\\_TGFb\\_R2 & Hes\\_Ctrl\\_R1 & Hes\\_TGFb\\_R1 & Hes\\_Ctrl\\_R2 & Hes\\_TGFb\\_R2 & Hil\\_Ctrl\\_R1 & Hil\\_Ctrl\\_R2\\\\\n",
       "\\hline\n",
       "\t9 & 6688 & 7611 & 6454 & 6975 & 6244 & 7330 & 7110 & 7052 & 6247 & 7315\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 10 of type int\n",
       "\n",
       "| <!--/--> | D_Ctrl_R1 | D_TGFb_R1 | D_Ctrl_R2 | D_TGFb_R2 | Hes_Ctrl_R1 | Hes_TGFb_R1 | Hes_Ctrl_R2 | Hes_TGFb_R2 | Hil_Ctrl_R1 | Hil_Ctrl_R2 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | 6688 | 7611 | 6454 | 6975 | 6244 | 7330 | 7110 | 7052 | 6247 | 7315 |\n",
       "\n"
      ],
      "text/plain": [
       "  D_Ctrl_R1 D_TGFb_R1 D_Ctrl_R2 D_TGFb_R2 Hes_Ctrl_R1 Hes_TGFb_R1 Hes_Ctrl_R2\n",
       "9 6688      7611      6454      6975      6244        7330        7110       \n",
       "  Hes_TGFb_R2 Hil_Ctrl_R1 Hil_Ctrl_R2\n",
       "9 7052        6247        7315       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: text\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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jCJMjZ3TWUYIjnfOSC43pS2KapofbIF1/Q2IYg04q5QIfXmO7ES5WzrTmgBiLTB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\nUZ09RCIE3puSUZ09RCIE3puSUZ09bn5CCLw3JaM6e4hECLw3JaM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STVVfaXh1bdyl8832yxXlCWzV0ZZm1LHegR/PuU6xOu/0ZdeVg5IV\nYiEkkXbWl9+mY8qDeGYP+c18Otafcfm5xDOmdfkSCnbHRk3m7O/4WO2FPRcj4lJOIgdEMm/O\nrsw+NkbRiznPef/UtzmzXq65nnZR44RsO+o456D/7Sk7eHZ4tvZa4mjXykHJCnEQkkiEt7pp\n159CBw2aN2iRDtXcoH06HeAEIoUs0kttz0u0+k1mggYiBSvSLXmL64M+C1+AvjkgUrAimZ3h\n+oT+nivMABMQKViRdtGusRHqvsIMcAGRghUJgGWBSAAwAJEAYAAiAcAARAKAAYgEAAMQCQAG\nIBIADEAkABiASAAwAJEAYAAiAcAARAKAAYgEAAMQCQAGIBIADPx/WZPRf994TNcAAAAASUVO\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  You can provide the upSet alone when working with unpaired gene-sets \n",
    "# We plot the second sample in rankData, view it by \n",
    "head(rankData[, 2, drop = FALSE])\n",
    "head(rankData[2, , drop = FALSE])\n",
    "\n",
    "plotRankDensity(rankData[,2,drop = FALSE], upSet = tgfb_gs_up, \n",
    "                downSet = tgfb_gs_dn, isInteractive = FALSE)\n",
    "\n",
    "## Setting `isInteractive = TRUE` generates an interactive plot using the `plotly` package.\n",
    "## Hovering over the bars in the interactive plot allows you to get information \n",
    "## such as the normalised rank (between 0 and 1) and ID of the gene represented by the bar. \n",
    "## For the rest of the plotting functions, the isInteractive = TRUE argument has the same behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=link8>3.2 Plot dispersions of scores</a> \n",
    "\n",
    "`plotDispersion()` generates the scatter plots of the `score VS. dispersions` for the total scores, the up scores and the down score of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>'Control'</li><li>'TGFb'</li><li>'Control'</li><li>'TGFb'</li><li>'Control'</li><li>'TGFb'</li><li>'Control'</li><li>'TGFb'</li><li>'Control'</li><li>'Control'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Control'\n",
       "\\item 'TGFb'\n",
       "\\item 'Control'\n",
       "\\item 'TGFb'\n",
       "\\item 'Control'\n",
       "\\item 'TGFb'\n",
       "\\item 'Control'\n",
       "\\item 'TGFb'\n",
       "\\item 'Control'\n",
       "\\item 'Control'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Control'\n",
       "2. 'TGFb'\n",
       "3. 'Control'\n",
       "4. 'TGFb'\n",
       "5. 'Control'\n",
       "6. 'TGFb'\n",
       "7. 'Control'\n",
       "8. 'TGFb'\n",
       "9. 'Control'\n",
       "10. 'Control'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Control\" \"TGFb\"    \"Control\" \"TGFb\"    \"Control\" \"TGFb\"    \"Control\"\n",
       " [8] \"TGFb\"    \"Control\" \"Control\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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bKl9zOPSsIs8vK8XK7O+/NF2oYeIdL7JtgRKRlfWrpF1jj2jEGNQ6Sb6Gztf9Lu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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Get the annotations of samples by their sample names\n",
    "tgfbAnnot = data.frame(SampleID = colnames(tgfb_expr_10_se),\n",
    "                       Type = NA)\n",
    "tgfbAnnot$Type[grepl(\"Ctrl\", tgfbAnnot$SampleID)] = \"Control\"\n",
    "tgfbAnnot$Type[grepl(\"TGFb\", tgfbAnnot$SampleID)] = \"TGFb\"\n",
    "\n",
    "# Sample annotations\n",
    "tgfbAnnot$Type\n",
    "\n",
    "plotDispersion(scoredf, annot = tgfbAnnot$Type, isInteractive = FALSE)\n",
    "\n",
    "# To see an interactive version powered by 'plotly', simply set the \n",
    "# 'isInteractive' = TRUE, i.e :\n",
    "#\n",
    "# plotDispersion(scoredf,annot = tgfbAnnot$Type,isInteractive = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=link9>3.3 Plot score landscape</a> \n",
    "\n",
    "`plotScoreLandscape()` plots the scores of the samples against two different gene signatures in a landscape for exploring their relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iv7yEg0eillPaN2GrVUUmy1DYjUw0SR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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScoreLandscape(scoredf_ccle_epi, scoredf_ccle_mes, \n",
    "                   scorenames = c('ccle-EPI','ccle-MES'),hexMin = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, pre-computed scores for the TCGA breast cancer RNA-seq dataset against epithelial and mesenchymal gene signatures are stored in scoredf_tcga_epi and scoredf_tcga_mes respectively (Tan et al. 2014). The utility of this function is enhanced when the number of samples is large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y+n0Z2nUmD0d7+gp1E70Fs5Grmjv7/b4o7+XlriaSRHf/fJjDUaL9LjXqTTYanr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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tcgaLandscape = plotScoreLandscape(scoredf_tcga_epi, scoredf_tcga_mes, \n",
    "                   scorenames = c('tcga_EPI','tcga_MES'), isInteractive = FALSE)\n",
    "\n",
    "tcgaLandscape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also project new data points onto the landscape plot generated above by using the projectScoreLandscape function. For example, the plot below overlays 3 CCLE samples onto the TCGA epithelial-mesenchymal landscape. Points are labeled with their sample names by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: text\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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9nTyIlymnVdpGtUq2ROeyOXNqfZI5P8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\nACIBkACIBEACIBIACYBIACTg/wP4DvMyxo5rAwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Project on the above generated 'tcgaLandscape' plot\n",
    "projectScoreLandscape(plotObj = tcgaLandscape, scoredf_ccle_epi, \n",
    "                      scoredf_ccle_mes,\n",
    "                      subSamples = rownames(scoredf_ccle_epi)[c(1,4,5)],\n",
    "                      annot = rownames(scoredf_ccle_epi)[c(1,4,5)], \n",
    "                      sampleLabels = NULL,\n",
    "                      isInteractive = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: text\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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pCCSBMzr0i3/g9Ct505WkNNI2/HRTXBEW3eXyVE+oy+HVc6aJHkkejo04X6VnAq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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom labels can be provided by passing a character vector to the sampleLabels argument.\n",
    "projectScoreLandscape(plotObj = tcgaLandscape, scoredf_ccle_epi, scoredf_ccle_mes,\n",
    "                      subSamples = rownames(scoredf_ccle_epi)[c(1,4,5,8,9)],\n",
    "                      sampleLabels = c('l1','l2','l3','l4','l5'),\n",
    "                      annot = rownames(scoredf_ccle_epi)[c(1,4,5,8,9)], \n",
    "                      isInteractive = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 4.0.0 (2020-04-24)\n",
       "Platform: x86_64-w64-mingw32/x64 (64-bit)\n",
       "Running under: Windows 10 x64 (build 18363)\n",
       "\n",
       "Matrix products: default\n",
       "\n",
       "locale:\n",
       "[1] LC_COLLATE=Chinese (Simplified)_China.936 \n",
       "[2] LC_CTYPE=Chinese (Simplified)_China.936   \n",
       "[3] LC_MONETARY=Chinese (Simplified)_China.936\n",
       "[4] LC_NUMERIC=C                              \n",
       "[5] LC_TIME=Chinese (Simplified)_China.936    \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] singscore_1.8.0\n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] ggrepel_0.8.2               locfit_1.5-9.4             \n",
       " [3] Rcpp_1.0.4.6                lattice_0.20-41            \n",
       " [5] tidyr_1.1.0                 assertthat_0.2.1           \n",
       " [7] digest_0.6.25               IRdisplay_0.7.0            \n",
       " [9] R6_2.4.1                    GenomeInfoDb_1.24.0        \n",
       "[11] plyr_1.8.6                  repr_1.1.0                 \n",
       "[13] stats4_4.0.0                RSQLite_2.2.0              \n",
       "[15] evaluate_0.14               ggplot2_3.3.0              \n",
       "[17] pillar_1.4.4                zlibbioc_1.34.0            \n",
       "[19] rlang_0.4.6                 uuid_0.1-4                 \n",
       "[21] annotate_1.66.0             hexbin_1.28.1              \n",
       "[23] blob_1.2.1                  S4Vectors_0.26.1           \n",
       "[25] Matrix_1.2-18               labeling_0.3               \n",
       "[27] stringr_1.4.0               RCurl_1.98-1.2             \n",
       "[29] bit_1.1-15.2                munsell_0.5.0              \n",
       "[31] DelayedArray_0.14.0         compiler_4.0.0             \n",
       "[33] pkgconfig_2.0.3             BiocGenerics_0.34.0        \n",
       "[35] base64enc_0.1-3             htmltools_0.4.0            \n",
       "[37] tidyselect_1.1.0            SummarizedExperiment_1.18.1\n",
       "[39] tibble_3.0.1                GenomeInfoDbData_1.2.3     \n",
       "[41] edgeR_3.30.0                IRanges_2.22.2             \n",
       "[43] matrixStats_0.56.0          XML_3.99-0.3               \n",
       "[45] crayon_1.3.4                dplyr_0.8.5                \n",
       "[47] bitops_1.0-6                grid_4.0.0                 \n",
       "[49] jsonlite_1.6.1              xtable_1.8-4               \n",
       "[51] GSEABase_1.50.0             gtable_0.3.0               \n",
       "[53] lifecycle_0.2.0             DBI_1.1.0                  \n",
       "[55] magrittr_1.5                scales_1.1.1               \n",
       "[57] graph_1.66.0                stringi_1.4.6              \n",
       "[59] farver_2.0.3                XVector_0.28.0             \n",
       "[61] reshape2_1.4.4              limma_3.44.1               \n",
       "[63] ellipsis_0.3.1              vctrs_0.3.0                \n",
       "[65] IRkernel_1.1                RColorBrewer_1.1-2         \n",
       "[67] tools_4.0.0                 bit64_0.9-7                \n",
       "[69] Biobase_2.48.0              glue_1.4.1                 \n",
       "[71] purrr_0.3.4                 parallel_4.0.0             \n",
       "[73] AnnotationDbi_1.50.0        colorspace_1.4-1           \n",
       "[75] GenomicRanges_1.40.0        memoise_1.1.0              \n",
       "[77] pbdZMQ_0.3-3               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
 ],
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