{
 "metadata": {
  "name": "",
  "signature": "sha256:83be7ace8a14ab8b8c50db4ed779b6d6d5863e450cb3d38e6c2f9ffac2a5edbe"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* American Gut Project\n",
      "* License: BSD\n",
      "* Author of this notebook: Daniel McDonald\n",
      "\n",
      "For this notebook, we're going to explore how the number of shared OTUs differs based on metadata. What these plots will show are the number of OTUs that are shared in an increasing number of samples. For instance, we expect to see a very large number of OTUs shared in at least 1% of the samples, fewer that are shared in at least 10%, even fewer at 50%, and almost none in 100% of the samples. This makes sense as we harbor unique microbial communities."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This Notebook assumes the following dependencies are in your environment:\n",
      "\n",
      "* [BIOM](http://biom-format.org) >= 2.1.0\n",
      "* [matplotlib](http://matplotlib.org/) >= 1.1.0\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from biom import load_table\n",
      "from biom.parse import MetadataMap\n",
      "from collections import Counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!curl -OL https://github.com/biocore/American-Gut/raw/master/data/AG/AG_100nt_even10k.txt\n",
      "!curl -OL https://github.com/biocore/American-Gut/raw/master/data/AG/AG_100nt_even10k.biom"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
        "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100   156    0   156    0     0    268      0 --:--:-- --:--:-- --:--:--   268\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0 3812k    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 13 3812k   13  528k    0     0   239k      0  0:00:15  0:00:02  0:00:13  784k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 46 3812k   46 1760k    0     0   547k      0  0:00:06  0:00:03  0:00:03 1044k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 74 3812k   74 2832k    0     0   675k      0  0:00:05  0:00:04  0:00:01 1065k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 97 3812k   97 3704k    0     0   711k      0  0:00:05  0:00:05 --:--:-- 1007k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100 3812k  100 3812k    0     0   712k      0  0:00:05  0:00:05 --:--:--  999k\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
        "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100   157    0   157    0     0    317      0 --:--:-- --:--:-- --:--:--   317\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:02 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      0 --:--:--  0:00:03 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:04 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:05 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:06 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:07 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:08 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:--  0:00:09 --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  8 10.8M    8  936k    0     0  89256      0  0:02:07  0:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "00:10  0:01:57  187k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 17 10.8M   17 1936k    0     0   164k      0  0:01:07  0:00:11  0:00:56  386k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 22 10.8M   22 2499k    0     0   196k      0  0:00:56  0:00:12  0:00:44  503k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 27 10.8M   27 3080k    0     0   223k      0  0:00:49  0:00:13  0:00:36  617k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 32 10.8M   32 3577k    0     0   242k      0  0:00:45  0:00:14  0:00:31  714k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 35 10.8M   35 4000k    0     0   254k      0  0:00:43  0:00:15  0:00:28  615k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 38 10.8M   38 4327k    0     0   258k      0  0:00:42  0:00:16  0:00:26  482k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 42 10.8M   42 4679k    0     0   263k      0  0:00:42  0:00:17  0:00:25  435k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 45 10.8M   45 5046k    0     0   269k      0  0:00:41  0:00:18  0:00:23  394k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 49 10.8M   49 5496k    0     0   278k      0  0:00:39  0:00:19  0:00:20  383k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 53 10.8M   53 5912k    0     0   285k      0  0:00:38  0:00:20  0:00:18  382k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 57 10.8M   57 6384k    0     0   293k      0  0:00:37  0:00:21  0:00:16  409k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 60 10.8M   60 6768k    0     0   297k      0  0:00:37  0:00:22  0:00:15  413k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 63 10.8M   63 7072k    0     0   298k      0  0:00:37  0:00:23  0:00:14  405k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 66 10.8M   66 7405k    0     0   299k      0  0:00:37  0:00:24  0:00:13  383k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 70 10.8M   70 7835k    0     0   303k      0  0:00:36  0:00:25  0:00:11  380k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 74 10.8M   74 8280k    0     0   309k   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   0  0:00:35  0:00:26  0:00:09  379k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 78 10.8M   78 8772k    0     0   316k      0  0:00:35  0:00:27  0:00:08  402k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 82 10.8M   82 9171k    0     0   316k      0  0:00:35  0:00"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ":28  0:00:07  399k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 87 10.8M   87 9776k    0     0   328k      0  0:00:33  0:00:29  0:00:04  474k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 93 10.8M   93 10.1M    0     0   338k      0  0:00:32  0:00:30  0:00:02  518k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 99 10.8M   99 10.7M    0     0   347k      0  0:00:31  0:00:31 --:--:--  553k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100 10.8M  100 10.8M    0     0   348k      0  0:00:31  0:00:31 --:--:--  570k\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metadata = MetadataMap.from_file('AG_100nt_even10k.txt')\n",
      "table = load_table('AG_100nt_even10k.biom')\n",
      "table.add_metadata(metadata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first function that we're going to define will compute the percentage of shared OTUs over the samples for a specific metadata category and value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compute_shared_otus(table_full, category, values):\n",
      "    \"\"\"Compute shared OTUs for a given category and values\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    table_full : Table\n",
      "        A BIOM table. It is assumed that this table has sample metadata associated with it.\n",
      "    category : str\n",
      "        The metadata category\n",
      "    values : list of str\n",
      "        The values of interest within the category\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    np.array\n",
      "        Observed percentages\n",
      "    np.array\n",
      "        Observed top OTU IDs\n",
      "    list\n",
      "        Observed top taxa\n",
      "    int\n",
      "        Number of samples in the filtered table\n",
      "    \n",
      "    \"\"\"\n",
      "    def _get_taxa(t, id_):\n",
      "        md = t.metadata(id_, axis='observation')\n",
      "        if md is None:\n",
      "            taxa = ''\n",
      "        else:\n",
      "            taxa = '; '.join(md['taxonomy'])\n",
      "        return taxa\n",
      "    \n",
      "    table = table_full.pa(inplace=False)\n",
      "    \n",
      "    # filter to just those samples that meet our metadata category and values\n",
      "    cat_fn = lambda v, i, md: md[category] in values\n",
      "    site = table.filter(cat_fn, inplace=False)\n",
      "\n",
      "    # normalize\n",
      "    norm_fn = lambda v, i, md: v / len(site.ids(axis='sample'))\n",
      "    site.transform(norm_fn)\n",
      "    \n",
      "    # compute percents\n",
      "    obs_full = site.sum('observation')\n",
      "    obs_percents = np.array([(obs_full > i).sum() for i in np.arange(0.0, 1.0, 0.01)], dtype=float)\n",
      "    \n",
      "    # determine the taxa that appear to be highly shared\n",
      "    top_ids = site.ids(axis='observation')[np.argwhere(obs_full > 0.95)].squeeze()\n",
      "    top_taxa = []\n",
      "    if len(np.atleast_1d(top_ids)) > 0:\n",
      "        if not top_ids.shape: # array of scalar value\n",
      "            top_ids = np.array(str(top_ids))\n",
      "            top_taxa.append(_get_taxa(site, str(top_ids)))\n",
      "        else:\n",
      "            for id_ in top_ids:\n",
      "                top_taxa.append(_get_taxa(site, id_))\n",
      "    \n",
      "    return obs_percents, top_ids, top_taxa, len(site.ids(axis='sample'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the next cell, we're going to first define a helper function for filtering to just fecal samples, and then define a more complex method that will allow us to compute shared OTUs over multiple category values within a metadata category. This method will additionally subsample the number of samples being examined to normalize for the number of samples used in the compute within each category value. Since we're subsampling the category values that have a larger number of samples associated, we're going to perform the subsample multiple times and return the mean shared OTUs observed. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fecal_filter = lambda v, i, md: md['BODY_SITE'] == 'UBERON:feces'\n",
      "def compute_multiple_values(table_full, category, min_count=25, prefilter=fecal_filter, iterations=10, \n",
      "                            discretize=None):\n",
      "    \"\"\"Compute shared OTUs over multiple category values\n",
      "    \n",
      "    This method will print summary details about the category being examined which can\n",
      "    be used to inform a good min_count.\n",
      "        \n",
      "    Parameters\n",
      "    ----------\n",
      "    table_full : Table\n",
      "        The full table to operate on.\n",
      "    category : str\n",
      "        The metadata category to examine.\n",
      "    min_count : int, optional\n",
      "        The minimum number of samples that must be associated with a category value.\n",
      "        Defaults to 25.\n",
      "    prefilter : func\n",
      "        Prefilter, e.g., down to just fecal samples\n",
      "    iterations : int, optional\n",
      "        Number of rarefactions to perform\n",
      "    discretize : list of tuple, optional\n",
      "        Bins to discretize the data. Expected form: [(min, max)]\n",
      "        \n",
      "    Returns\n",
      "    -------\n",
      "    np.array\n",
      "        The mean observed percents in row order with observed values\n",
      "    np.array\n",
      "        The stderr observed percents in row order with observed values\n",
      "    list of str\n",
      "        The observed category values\n",
      "    int\n",
      "        Sample depth used\n",
      "        \n",
      "    Notes\n",
      "    -----\n",
      "    This method ignores NA and no_data category values\n",
      "    \n",
      "    \"\"\"\n",
      "    def make_filter_f(md_field, md_val):\n",
      "        \"\"\"Construct a filter function based on a metadata category and value\n",
      "        \n",
      "        If md_val is a tuple, a discretization is attempted.\n",
      "        \"\"\"\n",
      "        if isinstance(md_val, tuple):\n",
      "            min_, max_ = md_val\n",
      "            def f(v, i, samp_md):\n",
      "                \"\"\"Return True if the sample is value is min_ <= foo < max_\"\"\"\n",
      "                try:\n",
      "                    return min_ <= float(samp_md[md_field]) < max_\n",
      "                except ValueError:\n",
      "                    return False\n",
      "        else:\n",
      "            def f(v, i, samp_md):\n",
      "                \"\"\"Return true if the sample is associated with our desired value\"\"\"\n",
      "                return samp_md[md_field] == md_val\n",
      "        return f\n",
      "    \n",
      "    pa = table_full.filter(prefilter, inplace=False).pa()\n",
      "    \n",
      "    # get the counts within each category value\n",
      "    metadata_counts = Counter()\n",
      "    for md in pa.metadata(axis='sample'):\n",
      "        # ignore missing data\n",
      "        if md[category] in ['NA', 'no_data', '']:\n",
      "            continue\n",
      "            \n",
      "        if discretize is not None:\n",
      "            for min_, max_ in discretize:\n",
      "                if min_ <= float(md[category]) < max_:\n",
      "                    metadata_counts[(min_, max_)] += 1\n",
      "                    break\n",
      "        else:\n",
      "            metadata_counts[md[category]] += 1\n",
      "    \n",
      "    metadata_counts = {k: v for k, v in metadata_counts.items() if v > 1}\n",
      "        \n",
      "    print \"Category counts for %s:\" % category\n",
      "    for k, v in sorted(metadata_counts.items(), key=lambda item: item[1], reverse=True):\n",
      "        print \"  %s: %d\" % (k, v)\n",
      "        \n",
      "    # determine the minimum sampling depth\n",
      "    sample_depth = min(filter(lambda v: v > min_count, metadata_counts.values()))        \n",
      "    metadata_values = [k for k, v in metadata_counts.items() if v >= sample_depth]\n",
      "    \n",
      "    observed_means = []\n",
      "    observed_stderrs = []\n",
      "    observed_values = []\n",
      "    for obs_v in sorted(metadata_values):\n",
      "        if discretize is not None:\n",
      "            observed_values.append(\"%d <= x < %d\" % obs_v)\n",
      "        else:\n",
      "            observed_values.append(obs_v)\n",
      "    \n",
      "        # filter to the specific category value\n",
      "        md_specific = pa.filter(make_filter_f(category, obs_v), inplace=False)\n",
      "        ids = md_specific.ids(axis='sample').copy() \n",
      "\n",
      "        results = np.zeros((iterations, 100), dtype=float)\n",
      "        for idx in range(iterations):\n",
      "            # subsample the table\n",
      "            np.random.shuffle(ids)\n",
      "            subsampled_ids = set(ids[:sample_depth])\n",
      "            filter_f = lambda v, i, md: i in subsampled_ids\n",
      "            ss = md_specific.filter(filter_f, inplace=False)\n",
      "            ss.filter(lambda v, i, md: sum(v) > 0, axis='observation')\n",
      "\n",
      "            # normalize\n",
      "            norm_f = lambda v, i, md: v / len(ss.ids(axis='sample'))\n",
      "            ss.transform(norm_f)\n",
      "            \n",
      "            # compute percents\n",
      "            obs_full = ss.sum('observation')\n",
      "            results[idx] = np.array([(obs_full > j).sum() for j in np.arange(0.0, 1.0, 0.01)], dtype=float)        \n",
      "        \n",
      "        observed_means.append(results.mean(axis=0))\n",
      "        observed_stderrs.append(results.std(axis=0) / np.sqrt(iterations))\n",
      "    \n",
      "    return np.asarray(observed_means), np.asarray(observed_stderrs), observed_values, sample_depth"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, we're going to construct a method to plot the results of out shared OTUs functions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_shared_otus(n_samples, percents, labels, title=None, depth=None, stderrs=None, start=0):\n",
      "    \"\"\"Plot the shared OTUs\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    n_samples : int\n",
      "        The number of samples reflected\n",
      "    percents : np.array\n",
      "        The percents to plot\n",
      "    title : str, optional\n",
      "        Additional title name\n",
      "    depth : int, optional\n",
      "        The subsample depth\n",
      "    \"\"\" \n",
      "    if len(percents.shape) == 1:\n",
      "        percents = np.array([percents])\n",
      "    \n",
      "    fig = plt.figure()\n",
      "    ax_count = plt.gca()\n",
      "    \n",
      "    title_str = 'Shared OTUs'\n",
      "    \n",
      "    if title is not None:\n",
      "        title_str += ': %s' % title\n",
      "    if depth is not None:\n",
      "        title_str += ', n=%d' % depth\n",
      "    \n",
      "    ax_count.set_title(title_str)\n",
      "    ax_count.set_yscale('log')\n",
      "    ax_count.set_ylabel('Number of OTUs')\n",
      "    ax_count.set_xlabel('Number of samples')\n",
      "    \n",
      "    x_ticks = np.arange(0.0, 1.0, 0.01) * n_samples\n",
      "    \n",
      "    for idx, row in enumerate(percents):\n",
      "        if stderrs is not None:\n",
      "            ax_count.errorbar(x_ticks[start:], row[start:], yerr=stderrs[idx][start:], fmt='o', \n",
      "                              markersize=5, markeredgewidth=0.25)\n",
      "        else:\n",
      "            ax_count.plot(x_ticks[start:], row[start:])\n",
      "\n",
      "    plt.legend(labels, numpoints=1)\n",
      "            \n",
      "    plt.tight_layout()\n",
      "    ax_count.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, we're going to define a few helper methods just to make the subsequent calls very easy. The compute methods above return other interesting bits of information though which you may like to explore."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def helper_single(category, values, label):\n",
      "    percents, top_ids, top_taxa, n_samples = compute_shared_otus(table, category, values)\n",
      "    plot_shared_otus(n_samples, percents, [label])\n",
      "    print \"Top observed shared taxa:\"\n",
      "    for t in top_taxa:\n",
      "        print '    %s' % t\n",
      "\n",
      "def helper_multiple(category, title, min_count=None, discretize=None):\n",
      "    means, stderrs, values, depth = compute_multiple_values(table, category, min_count, discretize=discretize)\n",
      "    plot_shared_otus(depth, means, values, title=title, depth=depth)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Plots**\n",
      "\n",
      "Now lets go and make some plots using the functions we just created. The first three plots will be high level, and just of the body sites. What you can see is that there are a lot of shared OTUs between people, but the number that are shared begin to drop off quickly as you increase the number of samples (_note that the y-axis is in a log-scale_). It does appear that a handful of OTUs are shared over all samples within the oral and skin sites but the over all sample size is much lower than the fecal site."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "helper_single('BODY_SITE', ['UBERON:feces'], 'Fecal')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top observed shared taxa:\n",
        "    k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__\n",
        "    k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__\n",
        "    k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii\n",
        "    k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIyWP8HJW5UAeibdFYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107b0cf90>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "helper_single('BODY_SITE', ['UBERON:skin', 'UBERON:hand'], 'Skin')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top observed shared taxa:\n",
        "    k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium; s__\n",
        "    k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Pseudomonadales; f__Pseudomonadaceae; g__Pseudomonas; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; f__Staphylococcaceae; g__Staphylococcus; s__aureus\n",
        "    k__Bacteria; p__Cyanobacteria; c__Chloroplast; o__Streptophyta; f__; g__; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__\n",
        "    k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Pasteurellales; f__Pasteurellaceae; g__Haemophilus; s__parainfluenzae\n",
        "    k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Propionibacteriaceae; g__Propionibacterium; s__acnes\n",
        "    k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium; s__\n",
        "    k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Enterobacteriales; f__Enterobacteriaceae; g__Enterobacter; s__radicincitans\n",
        "    k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIj0FosUERHpLRYpIiLSW/8HRB+nt9woANUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a60f710>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "helper_single('BODY_SITE', ['UBERON:tongue'], 'Oral')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top observed shared taxa:\n",
        "    k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Pasteurellales; f__Pasteurellaceae; g__Haemophilus; s__parainfluenzae\n",
        "    k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Carnobacteriaceae; g__Granulicatella; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__\n",
        "    k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUoQQQjQWFSlCCCEai4oUIYQQjfV/LVO1cGWDFlgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f10e590>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, lets slice and dice the data further. The next set of plots will look just within the fecal samples, and then within a given metadata category. Since we have uneven numbers of samples associated with each category (e.g., the number of people who have taken antibiotics recently is much less than the number of people who have not taken antibiotics in the last year), we're going to randomly select an even number of samples within each group and then compute shared OTUs from there. Since we're randomly subsampling, we're going to do this procedure 10 times and plot the mean number of shared OTUs.\n",
      "\n",
      "In the first plot, we're taking a look at the number of shared OTUs stratified by how recently antibiotics were used.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "helper_multiple('ANTIBIOTIC_SELECT', 'Antibiotic use')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Category counts for ANTIBIOTIC_SELECT:\n",
        "  Not in the last year: 1672\n",
        "  In the past year: 323\n",
        "  In the past 6 months: 289\n",
        "  In the past month: 73\n",
        "  In the past week: 50\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npMWiSooQQkiLRZUUIYSQFuv/zV0Lq/tbxaEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107b0cf90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the next plot, we're looking at whether seasonal allergies appear to impact the number of shared OTUs. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "helper_multiple('SEASONAL_ALLERGIES', 'Seasonal allergies')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Category counts for SEASONAL_ALLERGIES:\n",
        "  no: 1395\n",
        "  yes: 958\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACoLShSAAAA1BYUKQAAAGrrv5qzxnJez+y5AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106cbee50>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Diet interestingly appears to show a difference, with vegans having the most shared OTUs. This could potentially be explained by the number of plants consumed (see below), which appear to be associated with the number of shared OTUs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "helper_multiple('DIET_TYPE', 'Diet type')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Category counts for DIET_TYPE:\n",
        "  Omnivore: 1983\n",
        "  Omnivore but no red meat: 154\n",
        "  Vegetarian but eat seafood: 149\n",
        "  Vegetarian: 85\n",
        "  Vegan: 47\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1031eab10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The number of plants consumed in a given week looks like it may have an affect as well, where if you consume less than 5 different types of plants per week, you appear to have fewer shared OTUs with the rest of the population."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "helper_multiple('TYPES_OF_PLANTS', 'Number of different plants')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Category counts for TYPES_OF_PLANTS:\n",
        "  11 to 20: 744\n",
        "  6 to 10: 552\n",
        "  21 to 30: 445\n",
        "  More than 30: 364\n",
        "  Less than 5: 186\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUoQQQtotSlKEEELarf8DhuNhHZinN4QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a86cad0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now lets take a look at BMI. Interestingly, both the severely underweight and severely obese groups show a reduced number of shared OTUs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "BMI_ranges = [(0, 18),\n",
      "              (18, 25),\n",
      "              (25, 30),\n",
      "              (30, 35),\n",
      "              (35, 40)]\n",
      "helper_multiple('BMI', 'BMI', discretize=BMI_ranges)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Category counts for BMI:\n",
        "  (18, 25): 1390\n",
        "  (25, 30): 471\n",
        "  (30, 35): 134\n",
        "  (0, 18): 131\n",
        "  (35, 40): 40\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1031e1b50>"
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}