class09_similarity_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CS446/546 - Class Session 13 - Similarity and Hierarchical Clustering\n",
    "\n",
    "In this class session we are going to hierachically cluster (based on Sorensen-Dice similarity) vertices in a directed graph from a landmark paper on human gene regulation (Neph et al., Cell, volume 150, pages 1274-1286, 2012; see PDF on Canvas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by having a look at the Neph et al. data file, `neph_gene_network.txt`.  It is in edge-list format, with no header and no \"interaction\" column.  Just two columns, first column contains the \"regulator gene\" and the second column contains the \"target gene\":\n",
    "\n",
    "    head neph_gene_network.txt\n",
    "    AHR     BCL6\n",
    "    AHR     BHLHE41\n",
    "    AHR     BPTF\n",
    "    AHR     CEBPA\n",
    "    AHR     CNOT3\n",
    "    AHR     CREB1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load the packages that we will need for this exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(igraph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `read.table`, read the file `shared/neph_gene_network.txt`; name the two columns of the resulting data frame, `regulator` and `target`.  Since there is no header, we will use `header=FALSE`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_list_neph <- read.table(\"shared/neph_gene_network.txt\",\n",
    "                            header=FALSE,\n",
    "                            sep=\"\\t\",\n",
    "                            stringsAsFactors=FALSE,\n",
    "                            col.names=c(\"regulator\",\"target\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the edge-list data into a Graph object in igraph, using `graph_from_data_frame`. Make the graph undirected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IGRAPH c6d5c31 UN-- 538 47945 -- \n",
      "+ attr: name (v/c)\n"
     ]
    }
   ],
   "source": [
    "neph_graph <- graph_from_data_frame(edge_list_neph, directed=FALSE)\n",
    "summary(neph_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the adjacency matrix for the graph, using `get.adjacency`, and assign to matrix `g_matrix`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_matrix <- get.adjacency(neph_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the Sorensen-Dice similarity of this graph, and assign to similarity matrix `S`, using the igraph `similarity` function and specifying `method=\"dice\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "S = similarity(neph_graph, method=\"dice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute a distance matrix by calling `as.dist` on the object 1-S; assign to object `D`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = as.dist(1 - S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform average-linkage hierarchical clustering on this distance object, using `hclust` with `method=average`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc = hclust(D, method=\"average\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the dendrogram using `plot` and make the labels really small using `cex=0.01`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAMAAABKCk6nAAADAFBMVEUAAAABAQECAgIDAwME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\nlfxPnOx87eM0LOAGGAas8DBghef/AcYI9EtM625pAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Cluster Dendrogram\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(cc, cex=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}