Make_AMR_Heatmap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain dataset\n",
    "\n",
    "If running this script using Jupyter notebook locally, download data as desribed in `Offline Analysis Data Download Workflow`. If you are running this script using Google colab, download data as described in `Google Colab Data Download Workflow`.\n",
    "\n",
    "### Offline Analysis Data Download Workflow\n",
    "\n",
    "1. Use the CZ ID bulk download workflow to initiate a download of the CZ ID Combined AMR Results (.csv)\n",
    "2. Navigate the CZ ID downloads page https://czid.org/bulk_downloads\n",
    "3. Download the resulting file to you local computer\n",
    "4. Unzip the resulting file, which should be a directory containing .csv reports.\n",
    "5. Skip to \"Set data directory...\" section below (ignoring the Google Colab instructions below)\n",
    "\n",
    "### Google Colab Data Download Workflow\n",
    "IF using google colab to run this on your own data\n",
    "\n",
    "1. Use the CZ ID bulk download workflow to initiate a download of the CZ ID Combined AMR Results (.csv)\n",
    "2. Navigate the CZ ID downloads page https://czid.org/bulk_downloads\n",
    "3. For the download you created, select \"Details\" -> \"Advanced Downloads\"\n",
    "4. Copy the \"curl\" command\n",
    "5. Modify the <b>EXAMPLE</b> lines (below) to include the parameters specific to your dataset/download. Note, the **bold** portions will remain the same and should not be changed.\n",
    "\n",
    "\n",
    "You will need 3 lines (example shown in the cell below): \n",
    "* line 1: <b>! </b> {curl command pasted from CZ ID}\n",
    "* line 2: <b>! mv </b> {name of the output after \"-C\" in the curl command}<b>/* .</b> \n",
    "* line 3: <b>! ls </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 41518  100 41518    0     0   154k      0 --:--:-- --:--:-- --:--:--  154k\n",
      "x combined_amr_results.csv\n",
      "\u001b[34mCombined AMR Results-05-9-2023 02-09-22pm\u001b[m\u001b[m\n",
      "Combined AMR Results-05-9-2023 02-09-22pm.tar.gz\n",
      "LICENSE\n",
      "Make_AMR_Heatmap.ipynb\n",
      "README.md\n",
      "combined_amr_results.csv\n"
     ]
    }
   ],
   "source": [
    "# specific to Google Colab workflow\n",
    "# EXAMPLE:\n",
    "! curl -L \"https://czi-infectious-disease-prod-samples.s3.us-west-2.amazonaws.com/downloads/8889/Combined%20AMR%20Results.tar.gz?response-content-disposition=attachment%3B%20filename%3D%22Combined%20AMR%20Results.tar.gz%22&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIA23EIRNXFASKU5CGF%2F20230509%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20230509T210918Z&X-Amz-Expires=86400&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGEaCXVzLXdlc3QtMiJIMEYCIQCNlaKQTUzxsSOvrsavOfOyV%2FOkiMwdjaZOOyfibwGPeQIhAPyKXRugrAoTYyP6neCznziWU7jzTSpoTAZH3z2h5TOoKvMDCHoQAxoMNzQ1NDYzMTgwNzQ2IgzCMs6RKfVECzXehD8q0AMCoKS01%2FhjOxes7CwGnohBDosi%2BtlYLwjhlSQpBVYFW6UuEGWg8RefOrt90hB6Bh3BmSObKxfnYySsbaF1szEgdwVE%2BswzGo0Dq0S9%2BGdpbcB9llUHUigaLTRCYwnyxcHeQQiP0SmZ6AqpgWtnOSCiLMMdIMuwCCBvvB7r2Uw%2FG5kFxTyqK3n9uz3icOY0QL3EtP%2BGf06QvU3laZ3QSPzmT2hYNkfuWQvNW029Usy43An%2Bi9eRZClUUH%2BFURToQfgfIuFlGHK1%2Fx2lGK%2BPgl6r5I68dxwlRQmoRg55gwTXF8jbghgnJ7qam%2Bg1eFzPLc6cfOaR7OsHIdXv2AkN7k%2FFNVvfXU4g6VHIWmnQp1IRx%2F3HvSQgEr%2FnRFkEQfnez2dTFcYYXg3UdhumXsv7%2Bs8rV7kov2iOwijvMizZ%2FPlHVvwV5MpvnqnRN%2FlydCebzbOuRTCV7b%2FI11yU5kzUXx71%2B6wTXQkyPVX8lio%2Fga%2Fw8nbf1WD%2FKrUIHch5ZjFrK4xTQloOB8xVPZUeRVvVY5Y4t7Gq%2B3adExhd52liFVyEa3nUgsJB2k9t7GuUPPdFFiBkkCP5%2BAU0dOamuz4xacJIK8x0m5ymGSpAhWNuq0A3qDC97OmiBjqkAdwiOIf2%2BcI%2FomCfvzX4N%2BvvPlgvpAc2ysuM0MRjvqgxED%2BFPus9rfw4yZ34lTaTybeMTN59nxDQgus3grQbTAwWNqmKjqMAVRfW2lmxusO7vMsenoGvFsASNh20CVC8%2FIEa7dhNMF9%2BKh0PZFZoQ2kxp4%2FVhzFmVQ%2BW%2FtscME7y5GYI1zQ31fSOy4oRCrVY4LiFI6u0DrUlmtIpqIB9Wpez%2BO6d&X-Amz-SignedHeaders=host&X-Amz-Signature=9b8827b228966b87def646a363c97c3e26f61f4f0b5e699fed398ef3a7572c8d\" > \"Combined AMR Results-05-9-2023 02-09-22pm.tar.gz\"        && mkdir \"Combined AMR Results-05-9-2023 02-09-22pm\"        && tar -zvxf \"Combined AMR Results-05-9-2023 02-09-22pm.tar.gz\" -C \"Combined AMR Results-05-9-2023 02-09-22pm\"\n",
    "! mv 'Combined AMR Results-05-9-2023 02-09-22pm/'* .\n",
    "! ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update this parameter (as needed) to specify your own input data filename.\n",
    "# For most cases, leaving this as-is should be fine.\n",
    "\n",
    "amr_results_file = 'combined_amr_results.csv' # this is the test data repo provided in GitHub. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define threshold filters and settings\n",
    "\n",
    "There are several parameters available for tweaking.\n",
    "\n",
    "Note, the AMR pipeline runs parallel workflows for reads and contigs and then summarizes the results across both workflows in the final output. This is important to consider when applying filters because filters to either reads or contigs will therefore require that the gene be identified by that particular workflow. For example, if you apply a filter requiring `MIN_CONTIG_PID` >= 99, you will inherently be requiring that there be at least one contig hitting each gene. Meanwhile, requiring `MIN_READ_DEPTH` >= 5 will inherently be requiring that at least some reads hit this gene. The defaults listed below are the most permissible.\n",
    "\n",
    "**Metrics that affect per-sample filtering:** specifying the metrics, format, and column upon which filtering is applied.\n",
    "\n",
    "* **METRIC** - a string, the metric (column header from the report) to use in the heatmap\n",
    "* **MIN_NUM_CONTIGS** - an integer, the minimum number of contigs, filters on `num_contigs` \n",
    "* **CUTOFF_LIST** - a list of strings, specifying the \"RGI cutoffs\" that you would like to include, filters on `cutoff`. ['Strict', 'Perfect', 0] will allow all rows\n",
    "* **MIN_CONTIG_COV** - a number (decimals ok), the minimum contig coverage breadth, filters on `contig_coverage_breadth`\n",
    "* **MIN_CONTIG_PID** - a number (decimals ok), the minimum contig percent ID, filters on `contig_percent_id`\n",
    "* **MIN_NUM_READS** - an integer, the minimum number of reads hitting a particular gene, filters on `num_reads`\n",
    "* **MIN_READ_COV** - a number (decimals ok), the minimum read coverage breadth, filters on `read_coverage_breadth`\n",
    "* **MIN_READ_DEPTH** - a number (decimals ok), the minimum read coverage depth, filters on `read_coverage_depth`  \n",
    "* **MIN_RPM** - a number, specifying the minimum number of reads per million (rPM) for a particular gene, filters on `rpm`\n",
    "* **MIN_DPM** - a number, specifying the minimum depth per million (dPM) for a particular gene, filters on `dpm`\n",
    "* **DRUG_CLASS_LIST** - a list of strings, specifying the particular drug classes of interest. If no limitation on drug class is desired, leave this string empty. Filters on `drug_class`.\n",
    "\n",
    "**Metrics that affect the final data displayed in the heatmap:**\n",
    "* **TOP_N** - integer, the top N genes (based on the metric specified above) to be included in the visualization, per sample\n",
    "* **log_transform** - True / False indicating whether to log-transform the heatmap data \n",
    "* **figure_size** - a tuple (X, X) indicating the (width, height) of the desired .pdf figure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "METRIC = 'max_breadth'\n",
    "\n",
    "MIN_NUM_CONTIGS = 0\n",
    "CUTOFF_LIST = ['Strict', 'Perfect', 0]\n",
    "MIN_CONTIG_COV = 0\n",
    "MIN_CONTIG_PID = 0\n",
    "MIN_NUM_READS = 0\n",
    "MIN_READ_COV = 0\n",
    "MIN_READ_DEPTH = 0\n",
    "MIN_RPM = 0\n",
    "MIN_DPM = 0\n",
    "DRUG_CLASS_LIST = [] # example: ['aminoglycoside antibiotic', 'aminocoumarin antibiotic']\n",
    "\n",
    "TOP_N = 10\n",
    "LOG_TRANSFORM = False\n",
    "FIGURE_SIZE = (15,15)\n",
    "\n",
    "pd.set_option('display.max_rows', 5) # set the # of data table rows will be shown in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_czid_report(filename, num_contigs_cutoff = 0, cutoff_list = ['Strict', 'Perfect', 0], \n",
    "                     contig_cov_breadth_cutoff = 0, contig_percent_id_cutoff = 0,\n",
    "                     num_reads_cutoff = 0, read_coverage_breadth_cutoff = 0, read_coverage_depth_cutoff = 0, \n",
    "                     rpm_cutoff = 0, dpm_cutoff = 0, drug_class_list = [], \n",
    "                     split_drug_class = True):\n",
    "    \n",
    "    # read in the AMR data from the .csv file\n",
    "    df = pd.read_csv(filename)\n",
    "    df.fillna(0, inplace=True)\n",
    "\n",
    "    # apply quality filters\n",
    "    df = df[df.num_contigs >= num_contigs_cutoff]\n",
    "    if('nan') in cutoff_list:\n",
    "        df = df[df['cutoff'].isin(cutoff_list) | df['cutoff'].isnull()]\n",
    "    else:\n",
    "        df = df.loc[df['cutoff'].isin(cutoff_list)]\n",
    "    df = df[df.contig_coverage_breadth >= contig_cov_breadth_cutoff]\n",
    "    df = df[df.contig_percent_id >= contig_percent_id_cutoff]\n",
    "    df = df[df.num_reads >= num_reads_cutoff]\n",
    "    df = df[df.read_coverage_breadth >= read_coverage_breadth_cutoff]\n",
    "    df = df[df.read_coverage_depth >= read_coverage_depth_cutoff]\n",
    "    df = df[df.rpm >= rpm_cutoff] \n",
    "    df = df[df.dpm >= dpm_cutoff] \n",
    "    \n",
    "    # filter by drug class\n",
    "    if len(drug_class_list) > 0:\n",
    "        print(df.shape)\n",
    "        df = df.loc[[(sum([dclass in i for dclass in drug_class_list]) > 0) for i in df['drug_class']]]\n",
    "        print(df.shape)\n",
    "    \n",
    "    # define a \"max_breadth\" field that combines information from reads and contigs\n",
    "    df['max_breadth'] = df[[\"contig_coverage_breadth\", \"read_coverage_breadth\"]].max(axis=1)\n",
    "    \n",
    "    return(df)\n",
    "\n",
    "def format_drug_class(df):\n",
    "    '''\n",
    "    The drug_class output by CARD contains semicolon-delimited class names. This function will\n",
    "    separate these out and duplicate lines of the matrix to properly format pivot tables.\n",
    "    '''\n",
    "    \n",
    "    print(df.shape)\n",
    "    tally_dc = 0\n",
    "    new_df = []\n",
    "    for i in df.index:\n",
    "        drug_class_split = [this.strip() for this in df.loc[i,'drug_class'].split(';')]\n",
    "        for dc in drug_class_split:\n",
    "            tally_dc += 1\n",
    "            df.loc[i,'drug_class_split'] = dc\n",
    "            new_df.append(df.loc[i])\n",
    "\n",
    "            \n",
    "    final_df = pd.DataFrame(new_df)\n",
    "    df = final_df\n",
    "    print(tally_dc)\n",
    "    print(df.shape)\n",
    "    return(df)\n",
    "\n",
    "def plot_czid_heatmap(df, top_n=25, figure_size=(8,8), \n",
    "                      log=False, sort_values = True, output_filename = 'output.pdf'):\n",
    "    '''\n",
    "    Plot the final heatmap\n",
    "    '''\n",
    "    \n",
    "    plot_df = df\n",
    "    \n",
    "    x = plot_df.unstack().groupby(level=0, group_keys=False).nlargest(top_n).to_frame()\n",
    "    all_top_n_taxa = set([i[1] for i in x.index])\n",
    "    print(plot_df.shape)\n",
    "    plot_df = plot_df.loc[all_top_n_taxa]  # filter the plot data to only include taxa in the top_n\n",
    "    print(plot_df.shape)\n",
    "    \n",
    "    if sort_values:\n",
    "        plot_df.sort_index(inplace=True)\n",
    "    \n",
    "    # apply log-scale to value\n",
    "    if(log):\n",
    "        plot_df = np.log(plot_df + 1)\n",
    "        \n",
    "    # set CZ ID color pallette - yellow to oranage to red\n",
    "    czid_colors = sns.color_palette(\"YlOrRd\", as_cmap=True)  \n",
    "    \n",
    "    # plot heatmap\n",
    "    plt.figure(figsize=figure_size)\n",
    "    sns.heatmap(plot_df, cmap=czid_colors, linewidths=.1, linecolor='grey', \n",
    "                square=True, xticklabels=True, yticklabels=True) \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(output_filename)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_name</th>\n",
       "      <th>sample_name</th>\n",
       "      <th>gene_family</th>\n",
       "      <th>drug_class</th>\n",
       "      <th>resistance_mechanism</th>\n",
       "      <th>model_type</th>\n",
       "      <th>num_contigs</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>contig_coverage_breadth</th>\n",
       "      <th>contig_percent_id</th>\n",
       "      <th>contig_species</th>\n",
       "      <th>num_reads</th>\n",
       "      <th>read_coverage_breadth</th>\n",
       "      <th>read_coverage_depth</th>\n",
       "      <th>read_species</th>\n",
       "      <th>total_reads</th>\n",
       "      <th>rpm</th>\n",
       "      <th>dpm</th>\n",
       "      <th>max_breadth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adef</td>\n",
       "      <td>sample-11</td>\n",
       "      <td>resistance-nodulation-cell division (RND) anti...</td>\n",
       "      <td>fluoroquinolone antibiotic; tetracycline antib...</td>\n",
       "      <td>antibiotic efflux</td>\n",
       "      <td>protein homolog</td>\n",
       "      <td>1</td>\n",
       "      <td>Strict</td>\n",
       "      <td>97.8302</td>\n",
       "      <td>42.97</td>\n",
       "      <td>Phocaeicola dorei (chromosome)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>97.8302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bifidobacterium adolescentis rpob mutants conf...</td>\n",
       "      <td>sample-11</td>\n",
       "      <td>rifamycin-resistant beta-subunit of RNA polyme...</td>\n",
       "      <td>rifamycin antibiotic</td>\n",
       "      <td>antibiotic target alteration; antibiotic targe...</td>\n",
       "      <td>protein homolog</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>56.47</td>\n",
       "      <td>1.14</td>\n",
       "      <td>Bifidobacterium longum (chromosome): 4;</td>\n",
       "      <td>10000000</td>\n",
       "      <td>15.68</td>\n",
       "      <td>0.22</td>\n",
       "      <td>56.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>teta(60)</td>\n",
       "      <td>sample-6</td>\n",
       "      <td>ATP-binding cassette (ABC) antibiotic efflux pump</td>\n",
       "      <td>tetracycline antibiotic</td>\n",
       "      <td>antibiotic efflux</td>\n",
       "      <td>protein homolog</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.24</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.06</td>\n",
       "      <td>17.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>teta(p)</td>\n",
       "      <td>sample-6</td>\n",
       "      <td>major facilitator superfamily (MFS) antibiotic...</td>\n",
       "      <td>tetracycline antibiotic</td>\n",
       "      <td>antibiotic efflux</td>\n",
       "      <td>protein homolog</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>10000000</td>\n",
       "      <td>4.95</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.5400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>468 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             gene_name sample_name  \\\n",
       "0                                                 adef   sample-11   \n",
       "1    bifidobacterium adolescentis rpob mutants conf...   sample-11   \n",
       "..                                                 ...         ...   \n",
       "466                                           teta(60)    sample-6   \n",
       "467                                            teta(p)    sample-6   \n",
       "\n",
       "                                           gene_family  \\\n",
       "0    resistance-nodulation-cell division (RND) anti...   \n",
       "1    rifamycin-resistant beta-subunit of RNA polyme...   \n",
       "..                                                 ...   \n",
       "466  ATP-binding cassette (ABC) antibiotic efflux pump   \n",
       "467  major facilitator superfamily (MFS) antibiotic...   \n",
       "\n",
       "                                            drug_class  \\\n",
       "0    fluoroquinolone antibiotic; tetracycline antib...   \n",
       "1                                 rifamycin antibiotic   \n",
       "..                                                 ...   \n",
       "466                            tetracycline antibiotic   \n",
       "467                            tetracycline antibiotic   \n",
       "\n",
       "                                  resistance_mechanism       model_type  \\\n",
       "0                                    antibiotic efflux  protein homolog   \n",
       "1    antibiotic target alteration; antibiotic targe...  protein homolog   \n",
       "..                                                 ...              ...   \n",
       "466                                  antibiotic efflux  protein homolog   \n",
       "467                                  antibiotic efflux  protein homolog   \n",
       "\n",
       "     num_contigs  cutoff  contig_coverage_breadth  contig_percent_id  \\\n",
       "0              1  Strict                  97.8302              42.97   \n",
       "1              0       0                   0.0000               0.00   \n",
       "..           ...     ...                      ...                ...   \n",
       "466            0       0                   0.0000               0.00   \n",
       "467            0       0                   0.0000               0.00   \n",
       "\n",
       "                     contig_species  num_reads  read_coverage_breadth  \\\n",
       "0    Phocaeicola dorei (chromosome)        0.0                   0.00   \n",
       "1                                 0       81.0                  56.47   \n",
       "..                              ...        ...                    ...   \n",
       "466                               0        2.0                  17.24   \n",
       "467                               0       13.0                   5.54   \n",
       "\n",
       "     read_coverage_depth                              read_species  \\\n",
       "0                   0.00                                         0   \n",
       "1                   1.14  Bifidobacterium longum (chromosome): 4;    \n",
       "..                   ...                                       ...   \n",
       "466                 0.17                                         0   \n",
       "467                 0.04                                         0   \n",
       "\n",
       "     total_reads    rpm   dpm  max_breadth  \n",
       "0       10000000   0.00  0.00      97.8302  \n",
       "1       10000000  15.68  0.22      56.4700  \n",
       "..           ...    ...   ...          ...  \n",
       "466     10000000   0.76  0.06      17.2400  \n",
       "467     10000000   4.95  0.02       5.5400  \n",
       "\n",
       "[468 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_amr_df = read_czid_report(amr_results_file, \n",
    "                                   num_contigs_cutoff = MIN_NUM_CONTIGS, \n",
    "                                   cutoff_list = CUTOFF_LIST,\n",
    "                                   contig_cov_breadth_cutoff = MIN_CONTIG_COV,\n",
    "                                   contig_percent_id_cutoff = MIN_CONTIG_PID,\n",
    "                                   num_reads_cutoff = MIN_NUM_READS,\n",
    "                                   read_coverage_breadth_cutoff = MIN_READ_COV,\n",
    "                                   read_coverage_depth_cutoff = MIN_READ_DEPTH,\n",
    "                                   rpm_cutoff = MIN_RPM,\n",
    "                                   dpm_cutoff = MIN_DPM,\n",
    "                                   drug_class_list = DRUG_CLASS_LIST,\n",
    "                                   split_drug_class = True)\n",
    "\n",
    "\n",
    "filtered_amr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sample_name</th>\n",
       "      <th>sample-1</th>\n",
       "      <th>sample-10</th>\n",
       "      <th>sample-11</th>\n",
       "      <th>sample-2</th>\n",
       "      <th>sample-3</th>\n",
       "      <th>sample-4</th>\n",
       "      <th>sample-5</th>\n",
       "      <th>sample-6</th>\n",
       "      <th>sample-7</th>\n",
       "      <th>sample-8</th>\n",
       "      <th>sample-9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aac(3)-iid</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aad(6)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yoji</th>\n",
       "      <td>NaN</td>\n",
       "      <td>79.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.27</td>\n",
       "      <td>33.39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yrc-1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_name  sample-1  sample-10  sample-11  sample-2  sample-3  sample-4  \\\n",
       "gene_name                                                                   \n",
       "aac(3)-iid        NaN        NaN        NaN       NaN       NaN       NaN   \n",
       "aad(6)            NaN        NaN        NaN       NaN     45.37       NaN   \n",
       "...               ...        ...        ...       ...       ...       ...   \n",
       "yoji              NaN      79.01        NaN       NaN       NaN     15.21   \n",
       "yrc-1             NaN        NaN        NaN       NaN       NaN       NaN   \n",
       "\n",
       "sample_name  sample-5  sample-6  sample-7  sample-8  sample-9  \n",
       "gene_name                                                      \n",
       "aac(3)-iid        NaN       NaN       NaN     14.63       NaN  \n",
       "aad(6)          29.84       NaN     50.42       NaN     42.96  \n",
       "...               ...       ...       ...       ...       ...  \n",
       "yoji              NaN       NaN     15.27     33.39       NaN  \n",
       "yrc-1             NaN       NaN       NaN      1.48       NaN  \n",
       "\n",
       "[155 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data_by_genes = pd.pivot_table(filtered_amr_df, values=METRIC, index='gene_name', columns='sample_name')\n",
    "plot_data_by_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(155, 11)\n",
      "(36, 11)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_czid_heatmap(plot_data_by_genes, top_n = TOP_N, sort_values = True, \n",
    "                  figure_size = FIGURE_SIZE, \n",
    "                  log = LOG_TRANSFORM, output_filename = 'output_genes.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(468, 19)\n",
      "907\n",
      "(907, 20)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sample_name</th>\n",
       "      <th>sample-1</th>\n",
       "      <th>sample-10</th>\n",
       "      <th>sample-11</th>\n",
       "      <th>sample-2</th>\n",
       "      <th>sample-3</th>\n",
       "      <th>sample-4</th>\n",
       "      <th>sample-5</th>\n",
       "      <th>sample-6</th>\n",
       "      <th>sample-7</th>\n",
       "      <th>sample-8</th>\n",
       "      <th>sample-9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drug_class_split</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aminocoumarin antibiotic</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aminoglycoside antibiotic</th>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulfonamide antibiotic</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tetracycline antibiotic</th>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_name                sample-1  sample-10  sample-11  sample-2  sample-3  \\\n",
       "drug_class_split                                                                \n",
       "aminocoumarin antibiotic        1.0        8.0        NaN       NaN       NaN   \n",
       "aminoglycoside antibiotic       2.0        9.0        NaN       2.0       4.0   \n",
       "...                             ...        ...        ...       ...       ...   \n",
       "sulfonamide antibiotic          NaN        1.0        NaN       NaN       NaN   \n",
       "tetracycline antibiotic        13.0       27.0       14.0      17.0      13.0   \n",
       "\n",
       "sample_name                sample-4  sample-5  sample-6  sample-7  sample-8  \\\n",
       "drug_class_split                                                              \n",
       "aminocoumarin antibiotic        3.0       NaN       7.0       3.0       2.0   \n",
       "aminoglycoside antibiotic       3.0       3.0       5.0       2.0       7.0   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "sulfonamide antibiotic          NaN       NaN       1.0       NaN       NaN   \n",
       "tetracycline antibiotic        22.0      11.0      22.0      13.0      19.0   \n",
       "\n",
       "sample_name                sample-9  \n",
       "drug_class_split                     \n",
       "aminocoumarin antibiotic        NaN  \n",
       "aminoglycoside antibiotic       5.0  \n",
       "...                             ...  \n",
       "sulfonamide antibiotic          1.0  \n",
       "tetracycline antibiotic        16.0  \n",
       "\n",
       "[30 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data_by_drug_class = pd.pivot_table(format_drug_class(filtered_amr_df), values='gene_name', index='drug_class_split', columns='sample_name', aggfunc = 'count')\n",
    "plot_data_by_drug_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 11)\n",
      "(22, 11)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_czid_heatmap(plot_data_by_drug_class.loc[plot_data_by_drug_class.index.sort_values()],\n",
    "                  top_n = TOP_N, sort_values = True, figure_size = FIGURE_SIZE, log = LOG_TRANSFORM, \n",
    "                  output_filename = 'output_drugclasses.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the heatmaps\n",
    "\n",
    "If running locally, the resulting heatmap .pdf files should be available in your local working directory. \n",
    "\n",
    "If you are running the Google colab option, then the heatmap .pdf files will need to be downloaded from the Colab instance. This can be done by clicking on the folder icon in the upper left menu of the Colab interface, which will then open to a list of files associated with the script. When hovering over the file names, three dots appear with the option to download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}