eval_aligners.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf5ed2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "from scipy.stats import spearmanr\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from matplotlib.ticker import Locator\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "# from https://stackoverflow.com/questions/20470892/how-to-place-minor-ticks-on-symlog-scale\n",
    "class MinorSymLogLocator(Locator):\n",
    "    \"\"\"\n",
    "    Dynamically find minor tick positions based on the positions of\n",
    "    major ticks for a symlog scaling.\n",
    "    \"\"\"\n",
    "    def __init__(self, linthresh, nints=10):\n",
    "        \"\"\"\n",
    "        Ticks will be placed between the major ticks.\n",
    "        The placement is linear for x between -linthresh and linthresh,\n",
    "        otherwise its logarithmically. nints gives the number of\n",
    "        intervals that will be bounded by the minor ticks.\n",
    "        \"\"\"\n",
    "        self.linthresh = linthresh\n",
    "        self.nintervals = nints\n",
    "\n",
    "    def __call__(self):\n",
    "        # Return the locations of the ticks\n",
    "        majorlocs = self.axis.get_majorticklocs()\n",
    "\n",
    "        if len(majorlocs) == 1:\n",
    "            return self.raise_if_exceeds(np.array([]))\n",
    "\n",
    "        # add temporary major tick locs at either end of the current range\n",
    "        # to fill in minor tick gaps\n",
    "        dmlower = majorlocs[1] - majorlocs[0]    # major tick difference at lower end\n",
    "        dmupper = majorlocs[-1] - majorlocs[-2]  # major tick difference at upper end\n",
    "\n",
    "        # add temporary major tick location at the lower end\n",
    "        if majorlocs[0] != 0. and ((majorlocs[0] != self.linthresh and dmlower > self.linthresh) or (dmlower == self.linthresh and majorlocs[0] < 0)):\n",
    "            majorlocs = np.insert(majorlocs, 0, majorlocs[0]*10.)\n",
    "        else:\n",
    "            majorlocs = np.insert(majorlocs, 0, majorlocs[0]-self.linthresh)\n",
    "\n",
    "        # add temporary major tick location at the upper end\n",
    "        if majorlocs[-1] != 0. and ((np.abs(majorlocs[-1]) != self.linthresh and dmupper > self.linthresh) or (dmupper == self.linthresh and majorlocs[-1] > 0)):\n",
    "            majorlocs = np.append(majorlocs, majorlocs[-1]*10.)\n",
    "        else:\n",
    "            majorlocs = np.append(majorlocs, majorlocs[-1]+self.linthresh)\n",
    "\n",
    "        # iterate through minor locs\n",
    "        minorlocs = []\n",
    "\n",
    "        # handle the lowest part\n",
    "        for i in range(1, len(majorlocs)):\n",
    "            if majorlocs[i] <= self.linthresh:\n",
    "                continue\n",
    "            majorstep = majorlocs[i] - majorlocs[i-1]\n",
    "            if abs(majorlocs[i-1] + majorstep/2) < self.linthresh:\n",
    "                ndivs = self.nintervals\n",
    "            else:\n",
    "                ndivs = self.nintervals - 1.\n",
    "\n",
    "            minorstep = majorstep / ndivs\n",
    "            locs = np.arange(majorlocs[i-1], majorlocs[i], minorstep)[1:]\n",
    "            minorlocs.extend(locs)\n",
    "\n",
    "        return self.raise_if_exceeds(np.array(minorlocs))\n",
    "\n",
    "    def tick_values(self, vmin, vmax):\n",
    "        raise NotImplementedError('Cannot get tick locations for a '\n",
    "                          '%s type.' % type(self))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e70e421",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bd2d5fae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Human (chr22) Illumina [40, 0, 0, 0, 0, 0, 0] [2006, 2006, 2006, 2006, 2006, 2006, 2006] [0.014242115971515769, 1.3594217347956132, 0.01794616151545364, 0.0, 0.22083748753738783, 0.01794616151545364, 0.0009970089730807576]\n",
      "Human (chr22) PacBio CLR [0, 0, 0, 0, 0, 0, 0] [204, 204, 0, 204, 204, 204, 204] [6.504901960784314, 228.7549019607843, nan, 8901.446078431372, 13.632352941176471, 3213.950980392157, 13.823529411764707]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_480808/4056399941.py:123: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n",
      "  ax.grid(b=True,axis='both', which='both')\n",
      "/usr/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/usr/lib/python3.10/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/tmp/ipykernel_480808/4056399941.py:123: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n",
      "  ax.grid(b=True,axis='both', which='both')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E. coli (NC_000913.3) Illumina [0, 0, 0, 0, 0, 0, 0] [2560, 2560, 2560, 2560, 2560, 2560, 2560] [0.019140625, 0.068359375, 0.0015625, 0.0, 0.131640625, 0.0, 0.0]\n",
      "E. coli (NC_000913.3) PacBio CLR [0, 0, 0, 0, 0, 0, 0] [199, 199, 0, 199, 199, 199, 199] [6.849246231155779, 52.90954773869347, nan, 9322.96984924623, 1.6130653266331658, 47.89949748743719, 2.020100502512563]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_480808/4056399941.py:123: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n",
      "  ax.grid(b=True,axis='both', which='both')\n",
      "/usr/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/usr/lib/python3.10/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/tmp/ipykernel_480808/4056399941.py:123: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n",
      "  ax.grid(b=True,axis='both', which='both')\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set this to True to set a zero penalty for indels at alignment ends\n",
    "# for some alignment applications, this is the default\n",
    "local_align = False\n",
    "\n",
    "# use for local alignment score\n",
    "def score_cigar(cigar):\n",
    "    match = 0\n",
    "    mismatch = 1\n",
    "    gap_open = 1\n",
    "    gap_ext = 1\n",
    "    cur_num = 0\n",
    "    score = 0\n",
    "    num_ops = 0\n",
    "    for i, c in enumerate(cigar):\n",
    "        if c.isnumeric():\n",
    "            cur_num = cur_num * 10 + int(c)\n",
    "        else:\n",
    "            if c == \"=\":\n",
    "                score += cur_num * match\n",
    "            elif c == \"X\":\n",
    "                score += cur_num * mismatch\n",
    "            elif (c == \"D\" or c == \"I\") and num_ops > 0 and i + 1 != len(cigar):\n",
    "                score += gap_open + (cur_num - 1) * gap_ext\n",
    "            cur_num = 0\n",
    "            num_ops += 1\n",
    "    return score\n",
    "\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "data_types = [\n",
    "    (\"Illumina\", \"illumina\", 150),\n",
    "    (\"PacBio CLR\",\"pacbio\", 10000),\n",
    "]\n",
    "program = [\n",
    "    (\"Minimap2\", \"minimap2.sam\", \"#b2182b\"),\n",
    "    (\"BLAST\", \"blast.out\", \"#ef8a62\"),\n",
    "    (\"PuffAligner\", \"pufferfish.sam\", \"#fddbc7\"),\n",
    "    (\"vg\", \"vg.sam\", \"#d1e5f0\"),    \n",
    "    (\"GraphAligner\", \"graphaligner.gaf\",\"green\"),\n",
    "    (\"MetaGraph\\nAligner\", \"metagraph_base.out\", \"#67a9cf\"),\n",
    "    (\"TCG-Aligner\\n(this work)\", \"metagraph_coord.out\", \"#2166ac\"),    \n",
    "]\n",
    "experiments = [\n",
    "    (\"Human (chr22)\", \"hg38/chr22\"),\n",
    "    (\"E. coli (NC_000913.3)\",\"NC_000913.3/NC_000913.3\"),\n",
    "]\n",
    "\n",
    "base_size=4\n",
    "alpha=0.2\n",
    "\n",
    "fig,all_axs = plt.subplots(len(experiments),\n",
    "                           len(data_types),\n",
    "                           figsize=(1.5*base_size*len(data_types), base_size*len(experiments)))\n",
    "cm=plt.cm.get_cmap('tab10')\n",
    "for idx, ((exp_title,exp_path),axs) in enumerate(zip(experiments,all_axs[::-1])):\n",
    "    for jdx, ((read_name,read_prefix,read_length),ax) in enumerate(zip(data_types,axs)):\n",
    "        prog_scores = []\n",
    "        num_unmapped = []\n",
    "        num_reads = []\n",
    "        for prog_name,prog_prefix,linestyle in program:\n",
    "            infile = \"{}.sim.{}.{}.results.sam\".format(exp_path,read_prefix,prog_prefix)            \n",
    "            with open(infile, 'r') as f:\n",
    "                unmapped = 0\n",
    "                nreads = 0\n",
    "                lines = (line.rstrip().split(\"\\t\") for line in f if line[0] != '@')\n",
    "                scores = []\n",
    "                for l1,l2 in itertools.zip_longest(*[lines]*2):\n",
    "                    nreads += 1                    \n",
    "                    s1_sp = l1[-1].split(\":\")\n",
    "                    if s1_sp[0] == \"NM\":\n",
    "                        if local_align:\n",
    "                            c1 = l1[5]\n",
    "                            s1 = score_cigar(c1)\n",
    "                        else:\n",
    "                            s1 = int(s1_sp[-1])\n",
    "                    else:\n",
    "                        assert(s1_sp[0] == \"*\")\n",
    "                        s1 = np.inf\n",
    "                    s2_sp = l2[-1].split(\":\")\n",
    "                    if s2_sp[0] == \"NM\":\n",
    "                        if local_align:\n",
    "                            c2 = l2[5]\n",
    "                            s2 = score_cigar(c2)\n",
    "                        else:\n",
    "                            s2 = int(s2_sp[-1])\n",
    "                    else:\n",
    "                        assert(s2_sp[0] == \"*\")\n",
    "                        s2 = np.inf\n",
    "                        \n",
    "                    score = min(s1,s2)\n",
    "                    if score == np.inf:\n",
    "                        score = read_length\n",
    "                        unmapped += 1\n",
    "                        continue\n",
    "                    scores.append(score)\n",
    "                num_unmapped.append(unmapped)\n",
    "                num_reads.append(nreads)\n",
    "                prog_scores.append(np.array(scores))\n",
    "        print(exp_title,read_name,num_unmapped,num_reads, [np.mean(a) for a in prog_scores])\n",
    "        \n",
    "        labels = [a[0] for a in program]\n",
    "        styles = [a[2] for a in program]\n",
    "        df = pd.concat([pd.DataFrame(data={a:b}) for a,b in zip(labels, prog_scores)], ignore_index=True, axis=1)\n",
    "        df.fillna(read_length,inplace=True)\n",
    "        df.columns = labels\n",
    "#         sns.boxplot(data=df,ax=ax)\n",
    "#         for patch in ax.artists:\n",
    "#             r, g, b, a = patch.get_facecolor()\n",
    "#             patch.set_facecolor((r, g, b, alpha))\n",
    "#         sns.stripplot(data=df,ax=ax,alpha=alpha)\n",
    "#         sns.ecdfplot(data=df,ax=ax,linewidth=4,palette='Set1')\n",
    "        for kdx,(label,linestyle) in enumerate(zip(labels,styles)):\n",
    "            #color = list(cm(kdx if kdx != 5 else kdx + 5))\n",
    "            color = list(cm(kdx if kdx < 4 else (7 if kdx == 4 else 4 if kdx == 5 else 6)))\n",
    "            sns.ecdfplot(data=df[label],ax=ax,linewidth=4,label=label if idx == 0 and jdx == 0 else None,\n",
    "                         #linestyle=[\"-\",\":\"][kdx % 2],\n",
    "#                          color=cm.colors[kdx]\n",
    "                         color=color\n",
    "                         #cmap=\"Set1\",\n",
    "                        )\n",
    "            ax.scatter(x=df[label].max(),y=[1],color=color,s=10**2)\n",
    "        ax.set_xscale(\"symlog\",linthresh=1)\n",
    "        ax.xaxis.set_minor_locator(MinorSymLogLocator(1))\n",
    "        ax.grid(b=True,axis='both', which='both')\n",
    "        #ax.set_yscale(\"symlog\",linthresh=0.01)\n",
    "        #ax.set_ylim([-0.1,ax.get_ylim()[1]])\n",
    "        \n",
    "        #ax.grid(which=\"both\",axis=\"y\")\n",
    "        #ax.set_title(\"{} (read length: {})\".format(read_name,read_length))\n",
    "        #ax.set_ylabel(\"Edit distance to ground truth\")\n",
    "        if idx != 0:\n",
    "            ax.set_title(\"{} (read length: {})\".format(read_name, read_length))\n",
    "        ax.set_xlabel(\"Maximum edit distance to ground truth\")\n",
    "        #ax.set_yscale(\"symlog\",linthres=0.01)\n",
    "        #ax.legend(loc='best')\n",
    "        \n",
    "        ax.set_ylabel(\"Proportion of reads\")\n",
    "        \n",
    "        if jdx == 0:\n",
    "            ax.set_ylabel(\"\\n\\n\".join([exp_title, ax.get_ylabel()]))\n",
    "    \n",
    "plt.tick_params(axis='y', which='both')\n",
    "#fig.legend(loc='upper right',bbox_to_anchor=(1.2, 0.95))\n",
    "fig.legend(loc=\"upper center\", ncol=len(program),bbox_to_anchor=(0.5, 0),fontsize=16,edgecolor='w')\n",
    "#all_axs[0][0].set_visible(False)\n",
    "#all_axs[0][1].set_visible(False)\n",
    "#all_axs[0][0].set_ylim([0.99, 1.0005])\n",
    "all_axs[0][0].set_ylim([0.96, 1.001])\n",
    "all_axs[1][0].set_ylim([0.96, 1.001])\n",
    "#all_axs[1][0].set_ylim([0.97, 1.001])\n",
    "all_axs[0][1].set_ylim([0, 1.05])\n",
    "all_axs[1][1].set_ylim([0, 1.05])\n",
    "#all_axs[0][0].set_yscale(\"symlog\",linthresh=0.9)\n",
    "#all_axs[1][0].set_yscale(\"symlog\",linthresh=0.9)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"alignment_accuracy.pdf\",bbox_inches='tight')\n",
    "fig.savefig(\"Karasikov_Fig4.pdf\",bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}