docs/alignment-accuracy-mq-plots.ipynb

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    "Alignment report\n",
    "========="
   ]
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       "$( document ).ready(code_toggle);\n",
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   "source": [
    "# From SO: https://stackoverflow.com/a/28073228/2512851\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import bokeh\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import output_notebook, gridplot\n",
    "from bokeh.models import ColumnDataSource, HoverTool\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = json.load(open('inputs.json'))\n",
    "fname = d['csvA']\n",
    "df = pd.DataFrame.from_csv(fname, index_col=None, sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read distribution by MQ\n",
    "============"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "    <div class=\"bk-plotdiv\" id=\"6dc7f0af-371f-4606-aad2-8d75cc2a6f7a\"></div>\n",
       "</div>"
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0\",\"@perr_0\"],[\"perr 50\",\"@perr_50\"],[\"perr ideal\",\"@perr_ideal\"],[\"Reads\",\"@total\"]]},\"id\":\"34fc521c-1d96-45a7-877a-1b4787ee8ea0\",\"type\":\"HoverTool\"}],\"root_ids\":[\"b8aa3a16-e948-431b-b57f-eeae841cdcbe\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.9\"}};\n",
       "    var render_items = [{\"docid\":\"31289e6f-74f4-4150-a216-c3176805e299\",\"elementid\":\"6dc7f0af-371f-4606-aad2-8d75cc2a6f7a\",\"modelid\":\"b8aa3a16-e948-431b-b57f-eeae841cdcbe\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "b8aa3a16-e948-431b-b57f-eeae841cdcbe"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def aa_mq(df):    \n",
    "    correct_0 = df[df['derr']=='d = 0'][['MQ', 'count']].groupby('MQ').sum()\n",
    "    correct_0.columns = ['correct_0']\n",
    "    \n",
    "    correct_50 = df[(df['derr']=='0 < d <= 50') | (df['derr']=='d = 0')][['MQ', 'count']].groupby('MQ').sum()\n",
    "    correct_50.columns = ['correct_50']\n",
    "    \n",
    "    total = df[['MQ', 'count']].groupby('MQ').sum()    \n",
    "    total.columns = ['total']\n",
    "    \n",
    "    data = pd.concat((correct_0, correct_50, total), axis=1)\n",
    "\n",
    "    data['perr_0'] = 1 - data['correct_0'] / data['total']\n",
    "    data['perr_50'] = 1 - data['correct_50'] / data['total']\n",
    "    data['perr_ideal'] = 10 ** (-data.index / 10)\n",
    "    \n",
    "    return data\n",
    "\n",
    "def plot_aa_mq(data):\n",
    "    max_y = 10 ** np.ceil(np.log10(data['perr_0'].max()))\n",
    "    min_y = 10 ** np.floor(np.log10(data['perr_50'].min()))\n",
    "\n",
    "    source = ColumnDataSource(data)\n",
    "\n",
    "    hover = HoverTool(tooltips=[\n",
    "        ('perr 0', '@perr_0'),\n",
    "        ('perr 50', '@perr_50'),\n",
    "        ('perr ideal', '@perr_ideal'),        \n",
    "        ('Reads', '@total')        \n",
    "    ])\n",
    "        \n",
    "    p = figure(plot_height=200, plot_width=500, \n",
    "               x_axis_label='MQ',\n",
    "               y_axis_label='p_err',\n",
    "               tools=[hover, 'reset'], \n",
    "               y_axis_type=\"log\", y_range=[min_y, max_y])\n",
    "\n",
    "    h1 = p.circle(x='MQ', y='perr_0', size=2, source=source)\n",
    "    h1 = p.circle(x='MQ', y='perr_0', size=10, alpha=0.5, color='yellow', source=source)    \n",
    "    h3 = p.line(x='MQ', y='perr_ideal', source=source)\n",
    "\n",
    "    return p\n",
    "\n",
    "def plot_read_hist(data):\n",
    "    max_y = 10 ** np.ceil(np.log10(data['total'].max()))\n",
    "    min_y = 10 ** np.floor(np.log10(data['total'].min()))\n",
    "\n",
    "    source = ColumnDataSource(data)\n",
    "\n",
    "    hover = HoverTool(tooltips=[\n",
    "        ('Reads', '@total')        \n",
    "    ])\n",
    "        \n",
    "    p = figure(plot_height=200, plot_width=500, \n",
    "               x_axis_label='MQ',\n",
    "               y_axis_label='Reads',\n",
    "               tools=[hover, 'reset'], \n",
    "               y_axis_type=\"log\", y_range=[min_y, max_y])\n",
    "\n",
    "    h1 = p.vbar(x='MQ', bottom=min_y, top='total', width=0.7, source=source)\n",
    "\n",
    "    return p\n",
    "    \n",
    "\n",
    "data = aa_mq(df)\n",
    "s = [\n",
    "    [plot_aa_mq(data)],    \n",
    "    [plot_read_hist(data)]    \n",
    "]\n",
    "\n",
    "p = gridplot(s)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read_count_mq = df.groupby('MQ').sum()\n",
    "# max_y = 10 ** np.ceil(np.log10(read_count_mq['count'].max()))\n",
    "# min_y = 10 ** np.floor(np.log10(read_count_mq['count'].min()))\n",
    "\n",
    "# source = ColumnDataSource(read_count_mq)\n",
    "\n",
    "\n",
    "# tools = [\"reset\"]\n",
    "# p = figure(plot_height=200, plot_width=500, \n",
    "#            x_axis_label='MQ',\n",
    "#            y_axis_label='Reads',\n",
    "#            tools=tools, \n",
    "#            y_axis_type=\"log\", y_range=[min_y, max_y])\n",
    "\n",
    "# h1 = p.vbar(x='MQ', bottom=min_y, top='count', width=0.7, source=source)\n",
    "\n",
    "# p.add_tools(HoverTool(renderers=[h1], tooltips=[(\"reads\", \"@count\")]))\n",
    "\n",
    "# show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read distribution by alignment fate\n",
    "==================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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   "source": [
    "read_count_by_fate = df.groupby('derr').sum()\n",
    "read_count_by_fate['y'] = read_count_by_fate.index\n",
    "\n",
    "max_x = 10 ** np.ceil(np.log10(read_count_by_fate['count'].max()))\n",
    "min_x = 10 ** np.floor(np.log10(read_count_by_fate['count'].min()))\n",
    "\n",
    "source = ColumnDataSource(read_count_by_fate)\n",
    "\n",
    "\n",
    "tools = [\"reset\"]\n",
    "p = figure(plot_height=200, plot_width=500, \n",
    "           x_axis_label='Reads',\n",
    "           y_axis_label='Read fate',\n",
    "           tools=tools, \n",
    "           y_range=read_count_by_fate.index.tolist(),\n",
    "           x_axis_type=\"log\", \n",
    "           x_range=[min_x, max_x])\n",
    "\n",
    "h1 = p.hbar(y='y', left=min_x, right='count', height=0.7, source=source)\n",
    "\n",
    "p.add_tools(HoverTool(renderers=[h1], tooltips=[(\"reads\", \"@count\")]))\n",
    "\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mapped rate and Alignment accuracy parametrized by MQ\n",
    "============================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplotlib version of the plots\n",
    "\n",
    "def heng_li_plot(df, category, ax):\n",
    "    sub_df = df[df['category']==category]\n",
    "\n",
    "    #correct = sub_df[(sub_df['derr']=='0 < d <= 50') | (sub_df['derr']=='d = 0')][['MQ', 'count']].groupby('MQ').sum()\n",
    "    correct = sub_df[sub_df['derr']=='d = 0'][['MQ', 'count']].groupby('MQ').sum()\n",
    "    correct.columns = ['correct']\n",
    "    mapped = sub_df[sub_df['derr']!='unmapped'][['MQ', 'count']].groupby('MQ').sum()\n",
    "    mapped.columns = ['mapped']\n",
    "    total = sub_df[['MQ', 'count']].groupby('MQ').sum()\n",
    "    total.columns = ['total']\n",
    "\n",
    "    data = pd.concat((correct, mapped, total), axis=1)\n",
    "\n",
    "    x = np.zeros(61, dtype=float)\n",
    "    y = np.zeros(61, dtype=float)\n",
    "    for mq in range(61):\n",
    "        data_sub = data.iloc[mq:70]\n",
    "        x[mq] = 100 * data_sub['mapped'].sum() / total.sum()\n",
    "        y[mq] = 100 * data_sub['correct'].sum() / data_sub['mapped'].sum()\n",
    "    ax.plot(x, y)\n",
    "    ax.plot(x[0], y[0], 'ko')\n",
    "    plt.setp(ax, \n",
    "             xlim=(95, 101), xticks=range(96,101), \n",
    "             ylim=(79, 101), yticks=range(80,101, 5),\n",
    "             title=category)\n",
    "    \n",
    "    \n",
    "def plot_heng_li_panels(df):\n",
    "    fig = plt.figure(figsize=(10, 20))\n",
    "    for n, cat in enumerate(['Ref', 'SNP', 'Multi',\n",
    "                'INS <= 10', 'INS 11-50', 'INS > 50',\n",
    "                'DEL <= 10', 'DEL 11-50', 'DEL > 50']):\n",
    "        ax = plt.subplot(4, 3, n + 1)\n",
    "        heng_li_plot(df, cat, ax)\n",
    "        #plt.setp(ax, yscale='log')\n",
    "        if n != 6:\n",
    "            plt.setp(ax, xticklabels=[], yticklabels=[])\n",
    "        else:\n",
    "            plt.setp(ax, xlabel='% Mapped', ylabel='% Correct')\n",
    "\n",
    "#plot_heng_li_panels(df)"
   ]
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       "    var render_items = [{\"docid\":\"ad93679b-5c83-43c3-ba90-d7ba2cdeea7b\",\"elementid\":\"ef21a6bb-d998-40f1-93d0-40823230d913\",\"modelid\":\"389987be-60c1-41b1-96d4-82b6cf6b1f9f\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
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       "id": "389987be-60c1-41b1-96d4-82b6cf6b1f9f"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "def heng_li_plot(df, category):\n",
    "    sub_df = df[df['category']==category]\n",
    "\n",
    "    #correct = sub_df[(sub_df['derr']=='0 < d <= 50') | (sub_df['derr']=='d = 0')][['MQ', 'count']].groupby('MQ').sum()\n",
    "    correct = sub_df[sub_df['derr']=='d = 0'][['MQ', 'count']].groupby('MQ').sum()\n",
    "    correct.columns = ['correct']\n",
    "    mapped = sub_df[sub_df['derr']!='unmapped'][['MQ', 'count']].groupby('MQ').sum()\n",
    "    mapped.columns = ['mapped']\n",
    "    total = sub_df[['MQ', 'count']].groupby('MQ').sum()\n",
    "    total.columns = ['total']\n",
    "\n",
    "    data = pd.concat((correct, mapped, total), axis=1)\n",
    "\n",
    "    x = np.zeros(61, dtype=float)\n",
    "    y = np.zeros(61, dtype=float)\n",
    "    for mq in range(61):\n",
    "        data_sub = data.iloc[mq:70]\n",
    "        x[mq] = 100 * data_sub['mapped'].sum() / total.sum()\n",
    "        y[mq] = 100 * data_sub['correct'].sum() / data_sub['mapped'].sum()\n",
    "    \n",
    "    source = ColumnDataSource(data=dict(\n",
    "        mapped=x,\n",
    "        correct=y,\n",
    "        mq=range(61)\n",
    "    ))\n",
    "    \n",
    "    hover = HoverTool(tooltips=[\n",
    "        ('MQ', '≥ @mq'),\n",
    "        ('Map', '@mapped %'),\n",
    "        ('Correct', '@correct %')\n",
    "    ])\n",
    "    \n",
    "    s1 = figure(width=250, plot_height=250, \n",
    "                tools=[hover, 'pan', 'reset'],\n",
    "                title=category)\n",
    "    \n",
    "    s1.circle(x[0], y[0], size=10)\n",
    "    s1.line('mapped', 'correct', source=source)\n",
    "    \n",
    "    return s1\n",
    "    \n",
    "def plot_heng_li_panels(df):\n",
    "    s = []\n",
    "    row_cnt = 3\n",
    "    row_s = []\n",
    "    for n, cat in enumerate(['Ref', 'SNP', 'Multi',\n",
    "                'INS <= 10', 'INS 11-50', 'INS > 50',\n",
    "                'DEL <= 10', 'DEL 11-50', 'DEL > 50']):\n",
    "        if n % row_cnt == 0:\n",
    "            if len(row_s):\n",
    "                s.append(row_s)\n",
    "            row_s = []\n",
    "        row_s.append(heng_li_plot(df, cat))\n",
    "\n",
    "    if len(row_s):\n",
    "        s.append(row_s)\n",
    "\n",
    "    p = gridplot(s)\n",
    "    show(p)\n",
    "    \n",
    "plot_heng_li_panels(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTES\n",
    "===\n",
    "\n",
    "AAF_CSV -> PLOT\n",
    "--------------\n",
    "Takes in a summary CSV file produced from an Alignment Analysis Format (AAF) file and creates alignment analysis plots. \n",
    "\n",
    "Expects a file called `inputs.json` located in the working directory of the form\n",
    "\n",
    "```\n",
    "{\n",
    "    \"csvA\": <path to my csv file>\n",
    "}\n",
    "```"
   ]
  }
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