multiqc_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MultiQC Jupyter Notebook Example\n",
    "\n",
    "This notebook has some example code showing how MultiQC can be used within an interactive analysis, such as a Jupyter Notebook.\n",
    "\n",
    "MultiQC is written in Python, so must be used in a Python environment. MultiQC can be installed in a variety of ways: see [the documentation](https://multiqc.info/docs/#installing-multiqc) for more information. Note that MultiQC must be installed into the notebook _kernel_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's install MultiQC using pip from source. Note the `%` magic which installs the package into the kernel, and not the jupyter environment. In order for the environment to pick the updated package, we call the reset command to restart kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:36.788159Z",
     "start_time": "2024-05-06T16:09:36.181145Z"
    }
   },
   "outputs": [],
   "source": [
    "%pip install --quiet multiqc pandas\n",
    "%reset -f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's import the `multiqc` package into your workbook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import multiqc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now let's check that it's working properly by printing the version that we're using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:36.795682Z",
     "start_time": "2024-05-06T16:09:36.788878Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.22.dev0\n"
     ]
    }
   ],
   "source": [
    "print(multiqc.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can use any outputs from MultiQC, we must first run it on some data. Let's grab results from a test run of the [nf-core/viralrecon](https://github.com/nf-core/viralrecon) pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:03:34.191210Z",
     "start_time": "2024-05-06T16:03:30.540939Z"
    }
   },
   "outputs": [],
   "source": [
    "!test -d \"data\" || (wget https://multiqc.info/examples/jupyter/data.zip && unzip -qq data.zip && rm data.zip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should now see a folder called `data/` in your notebook work directory with a bunch of log files from read processing, assembly, alignment, and variant calling. Now let's parse some of those logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:03:49.411732Z",
     "start_time": "2024-05-06T16:03:49.274482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36massembly\u001b[m\u001b[m/                         \u001b[1m\u001b[36mpipeline_info\u001b[m\u001b[m/\n",
      "\u001b[1m\u001b[36mfastp\u001b[m\u001b[m/                            summary_assembly_metrics_mqc.csv\n",
      "\u001b[1m\u001b[36mfastqc\u001b[m\u001b[m/                           summary_variants_metrics_mqc.csv\n",
      "\u001b[1m\u001b[36mkraken2\u001b[m\u001b[m/                          \u001b[1m\u001b[36mvariants\u001b[m\u001b[m/\n"
     ]
    }
   ],
   "source": [
    "%ls data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's parse some `fastp` logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:41.311556Z",
     "start_time": "2024-05-06T16:09:40.946261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;208m///\u001b[0m \u001b[1mhttps://multiqc.info\u001b[0m 🔍 \u001b[2mv1.22.dev0\u001b[0m\n",
      "\u001b[34m       file_search\u001b[0m | Search path: /Users/vlad/git/example-notebook/data/fastp\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: custom_content\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: fastp\n",
      "\u001b[34m             fastp\u001b[0m | Found 3 reports\n"
     ]
    }
   ],
   "source": [
    "multiqc.parse_logs('./data/fastp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MultiQC parsed and loaded the `fastp` results into memory. Let's inspect them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:41.446027Z",
     "start_time": "2024-05-06T16:09:41.437054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fastp']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiqc.list_modules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:41.653128Z",
     "start_time": "2024-05-06T16:09:41.649560Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SAMPLE1_PE', 'SAMPLE2_PE', 'SAMPLE3_SE']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiqc.list_samples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:42.009662Z",
     "start_time": "2024-05-06T16:09:41.969801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/vlad/git/example-notebook/data/fastp/SAMPLE3_SE.fastp.json',\n",
       " '/Users/vlad/git/example-notebook/data/fastp/SAMPLE1_PE.fastp.json',\n",
       " '/Users/vlad/git/example-notebook/data/fastp/SAMPLE2_PE.fastp.json']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiqc.list_data_sources()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:42.166462Z",
     "start_time": "2024-05-06T16:09:42.160147Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fastp_version': '0.23.2',\n",
       " 'sequencing': 'paired end (301 cycles + 301 cycles)',\n",
       " 'before_filtering': {'total_reads': 55442,\n",
       "  'total_bases': 16571632,\n",
       "  'q20_bases': 16267224,\n",
       "  'q30_bases': 15853021,\n",
       "  'q20_rate': 0.981631,\n",
       "  'q30_rate': 0.956636,\n",
       "  'read1_mean_length': 298,\n",
       "  'read2_mean_length': 298,\n",
       "  'gc_content': 0.38526},\n",
       " 'after_filtering': {'total_reads': 48270,\n",
       "  'total_bases': 14363465,\n",
       "  'q20_bases': 14323363,\n",
       "  'q30_bases': 14199841,\n",
       "  'q20_rate': 0.997208,\n",
       "  'q30_rate': 0.988608,\n",
       "  'read1_mean_length': 298,\n",
       "  'read2_mean_length': 296,\n",
       "  'gc_content': 0.383991}}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1_result = multiqc.get_module_data(module=\"fastp\", sample=\"SAMPLE1_PE\")\n",
    "sample1_result[\"summary\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MultiQC module for `fastp` implements several plots. The function below lists plot sections, groupped by module name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:43.570259Z",
     "start_time": "2024-05-06T16:09:43.558472Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fastp': ['Filtered Reads',\n",
       "  'Insert Sizes',\n",
       "  {'Sequence Quality': ['Read 1: Before filtering',\n",
       "    'Read 1: After filtering',\n",
       "    'Read 2: Before filtering',\n",
       "    'Read 2: After filtering']},\n",
       "  {'GC Content': ['Read 1: Before filtering',\n",
       "    'Read 1: After filtering',\n",
       "    'Read 2: Before filtering',\n",
       "    'Read 2: After filtering']},\n",
       "  {'N content': ['Read 1: Before filtering',\n",
       "    'Read 1: After filtering',\n",
       "    'Read 2: Before filtering',\n",
       "    'Read 2: After filtering']}]}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiqc.list_plots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use this information to call `multiqc.get_plot(\"<module\">, \"<section>\")` to retrieve plot object, and show it with `plot.show()`. \n",
    "\n",
    "When plot has several datasets (like \"Sequence Quality\", \"GC Content\", and \"N Content\" plots), pass the dataset name as well into `plot.show(dataset_id=\"<dataset>\")`.\n",
    "\n",
    "Let's display the GC Content plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:44.522378Z",
     "start_time": "2024-05-06T16:09:43.887262Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"mqc_mplplot\" id=\"fastp-seq-content-gc-plot\"><img 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height=\"515px\" width=\"1100px\"/></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot = multiqc.get_plot(\"fastp\", \"GC Content\")\n",
    "plot.show(dataset_id=\"Read 2: Before filtering\", flat=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rendered plot is a static image, because the option `flat=True` was provided. To print an interactive plot that you can hover and zoom, omit the `flat` flag.\n",
    "\n",
    "You can also save plot to a file using the `plot.save()` method instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot.save(\"gc_content.html\")  # will save an interactive HTML\n",
    "plot.save(\"gc_content.png\")  # will save a flat image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding more modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we only parsed logs for one module. Now, let's parse more. The original viralrecon workflow uses a [custom script](https://github.com/nf-core/viralrecon/blob/3731dd3a32a67a2648ea22c2bd980c224abdaee2/bin/multiqc_to_custom_csv.py) to summarize data from multiple modules, and we are going to do that interactively, and after that, generate an HTML report.\n",
    "\n",
    "We also notice that the viralrecon workflow runs QUAST several times: for each assembler separately, and then for the consensus sequence. To keep each run in a separate section, we will use the [module_order](https://multiqc.info/docs/reports/customisation/#running-modules-multiple-times) configuration option to provide path filters for each QUAST run. \n",
    "\n",
    "Any custom settings supported by MultiQC can be passed to the function call like that, for example, `extra_fn_clean_exts` can be used to trim additional endings from sample names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:45.494Z",
     "start_time": "2024-05-06T16:09:44.648708Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;208m///\u001b[0m \u001b[1mhttps://multiqc.info\u001b[0m 🔍 \u001b[2mv1.22.dev0\u001b[0m\n",
      "\u001b[34m       file_search\u001b[0m | Search path: /Users/vlad/git/example-notebook/data/assembly\n",
      "\u001b[34m       file_search\u001b[0m | Search path: /Users/vlad/git/example-notebook/data/kraken2\n",
      "\u001b[34m       file_search\u001b[0m | Search path: /Users/vlad/git/example-notebook/data/variants\n",
      "\u001b[34m       file_search\u001b[0m | Search path: /Users/vlad/git/example-notebook/data/fastqc\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: bcftools\n",
      "\u001b[34m          bcftools\u001b[0m | Found 3 stats reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: bowtie2\n",
      "\u001b[34m           bowtie2\u001b[0m | Found 3 reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: custom_content\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: cutadapt\n",
      "\u001b[34m          cutadapt\u001b[0m | Found 3 reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: fastqc\n",
      "\u001b[34m            fastqc\u001b[0m | Found 7 reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: kraken\n",
      "\u001b[34m            kraken\u001b[0m | Found 3 reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: mosdepth\n",
      "\u001b[34m          mosdepth\u001b[0m | Found reports for 3 samples\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: nextclade\n",
      "\u001b[34m         nextclade\u001b[0m | Found 3 samples\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: pangolin\n",
      "\u001b[34m          pangolin\u001b[0m | Found 3 samples\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: picard\n",
      "\u001b[34m            picard\u001b[0m | Found 2 AlignmentSummaryMetrics reports\n",
      "\u001b[34m            picard\u001b[0m | Found 3 BaseDistributionByCycleMetrics reports\n",
      "\u001b[34m            picard\u001b[0m | Found 1 InsertSizeMetrics reports\n",
      "\u001b[34m            picard\u001b[0m | Found 2 QualityByCycleMetrics reports\n",
      "\u001b[34m            picard\u001b[0m | Found 2 QualityScoreDistributionMetrics reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: samtools\n",
      "\u001b[34m          samtools\u001b[0m | Found 3 stats reports\n",
      "\u001b[34m          samtools\u001b[0m | Found 3 flagstat reports\n",
      "\u001b[34m          samtools\u001b[0m | Found 3 idxstats reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: snpeff\n",
      "\u001b[34m            snpeff\u001b[0m | Found 3 reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: quast\n",
      "\u001b[34m             quast\u001b[0m | Found 3 reports\n",
      "\u001b[34m      exec_modules\u001b[0m | Running module: quast\n",
      "\u001b[34m             quast\u001b[0m | Found 3 reports\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['fastp',\n",
       " 'Bcftools',\n",
       " 'Bowtie 2 / HiSAT2',\n",
       " 'Cutadapt',\n",
       " 'FastQC',\n",
       " 'Kraken',\n",
       " 'Mosdepth',\n",
       " 'Nextclade',\n",
       " 'Pangolin',\n",
       " 'Picard',\n",
       " 'Samtools',\n",
       " 'SnpEff',\n",
       " 'VARIANTS: QUAST',\n",
       " 'ASSEMBLY: QUAST (SPAdes)']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import multiqc\n",
    "multiqc.parse_logs(\n",
    "    \"./data/assembly\",\n",
    "    \"./data/kraken2\",\n",
    "    \"./data/variants\",\n",
    "    \"./data/fastqc\",\n",
    "    module_order=[\n",
    "        dict(\n",
    "            quast=dict(\n",
    "                name=\"VARIANTS: QUAST\",\n",
    "                anchor=\"quast_variants\",\n",
    "                info=\"This section of the report shows QUAST QC results for the consensus sequence.\",\n",
    "                path_filters=[\"*/variants/*\"],\n",
    "            )\n",
    "        ),\n",
    "        dict(\n",
    "            quast=dict(\n",
    "                name=\"ASSEMBLY: QUAST (SPAdes)\",\n",
    "                anchor=\"quast_spades\",\n",
    "                info=\"This section of the report shows QUAST results from SPAdes de novo assembly.\",\n",
    "                path_filters=[\"*/spades/*\"],\n",
    "            )\n",
    "        ),\n",
    "    ],\n",
    "    extra_fn_clean_exts=[\".unclassified\", \".pangolin\", \" MN908947.3\", \" MN908947.3\"]\n",
    ")\n",
    "multiqc.list_modules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:45.494182Z",
     "start_time": "2024-05-06T16:09:45.476039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SAMPLE1_PE',\n",
       " 'SAMPLE1_PE_1',\n",
       " 'SAMPLE1_PE_2',\n",
       " 'SAMPLE2_PE',\n",
       " 'SAMPLE2_PE_1',\n",
       " 'SAMPLE2_PE_2',\n",
       " 'SAMPLE2_PE_R1',\n",
       " 'SAMPLE2_PE_R2',\n",
       " 'SAMPLE3_SE']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiqc.list_samples()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now there are many more plots available in the session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:46.873187Z",
     "start_time": "2024-05-06T16:09:46.871238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ASSEMBLY: QUAST (SPAdes)': ['Assembly Statistics', 'Number of Contigs'],\n",
      " 'Bcftools': ['Variant Substitution Types',\n",
      "              {'Variant Quality': ['Count SNP',\n",
      "                                   'Count Transitions',\n",
      "                                   'Count Transversions',\n",
      "                                   'Count Indels']},\n",
      "              'Indel Distribution',\n",
      "              'Variant depths'],\n",
      " 'Bowtie 2 / HiSAT2': ['Single-end alignments', 'Paired-end alignments'],\n",
      " 'Cutadapt': ['Filtered Reads',\n",
      "              {'Trimmed Sequence Lengths': ['Counts', 'Obs/Exp']}],\n",
      " 'FastQC': ['Sequence Counts',\n",
      "            'Sequence Quality Histograms',\n",
      "            'Per Sequence Quality Scores',\n",
      "            {'Per Sequence GC Content': ['Percentages', 'Counts']},\n",
      "            'Per Base N Content',\n",
      "            'Sequence Length Distribution',\n",
      "            'Sequence Duplication Levels',\n",
      "            'Overrepresented sequences by sample',\n",
      "            'Top overrepresented sequences',\n",
      "            'Status Checks'],\n",
      " 'Kraken': [{'Top taxa': ['Genus',\n",
      "                          'Family',\n",
      "                          'Order',\n",
      "                          'Class',\n",
      "                          'Phylum',\n",
      "                          'Domain',\n",
      "                          'Root',\n",
      "                          'Unclassified']}],\n",
      " 'Mosdepth': [],\n",
      " 'Nextclade': ['Run table'],\n",
      " 'Pangolin': ['Run table'],\n",
      " 'Picard': [{'Alignment Summary': ['Aligned Reads', 'Aligned Bases']},\n",
      "            'Mean read length',\n",
      "            {'Base Distribution': ['% Adenine',\n",
      "                                   '% Cytosine',\n",
      "                                   '% Guanine',\n",
      "                                   '% Thymine',\n",
      "                                   '% Undetermined']},\n",
      "            {'Insert Size': ['Counts', 'Percentages']},\n",
      "            'Mean Base Quality by Cycle',\n",
      "            'Base Quality Distribution'],\n",
      " 'Samtools': ['Percent mapped',\n",
      "              'Alignment stats',\n",
      "              {'Flagstat': ['Read counts', 'Percentage of total']},\n",
      "              {'Mapped reads per contig': ['Normalised Counts',\n",
      "                                           'Observed over Expected Counts',\n",
      "                                           'Raw Counts']}],\n",
      " 'SnpEff': ['Variants by Genomic Region',\n",
      "            'Variant Effects by Impact',\n",
      "            'Variants by Effect Types',\n",
      "            'Variants by Functional Class'],\n",
      " 'VARIANTS: QUAST': ['Assembly Statistics', 'Number of Contigs'],\n",
      " 'fastp': ['Filtered Reads',\n",
      "           'Insert Sizes',\n",
      "           {'Sequence Quality': ['Read 1: Before filtering',\n",
      "                                 'Read 1: After filtering',\n",
      "                                 'Read 2: Before filtering',\n",
      "                                 'Read 2: After filtering']},\n",
      "           {'GC Content': ['Read 1: Before filtering',\n",
      "                           'Read 1: After filtering',\n",
      "                           'Read 2: Before filtering',\n",
      "                           'Read 2: After filtering']},\n",
      "           {'N content': ['Read 1: Before filtering',\n",
      "                          'Read 1: After filtering',\n",
      "                          'Read 2: Before filtering',\n",
      "                          'Read 2: After filtering']}]}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "pprint(multiqc.list_plots())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:47.342904Z",
     "start_time": "2024-05-06T16:09:47.184051Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"mqc_mplplot\" id=\"quast_num_contigs\"><img 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height=\"300px\" width=\"1100px\"/></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot = multiqc.get_plot(module=\"ASSEMBLY: QUAST (SPAdes)\", section=\"Number of Contigs\")\n",
    "plot.show(flat=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's re-summarize the data and add a custom section into the report. \n",
    "\n",
    "It will be a table, so we will need to define titles for the table columns (`headers`) along with a value for each sample for each column.\n",
    "\n",
    "We will use the `multiqc.get_module_data()` and `multiqc.get_general_stats_data()` methods to pull data parsed and summarized by modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:48.903680Z",
     "start_time": "2024-05-06T16:09:48.893185Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SAMPLE1_PE': {'clade': '20A',\n",
      "                'genome_fraction': 98.094,\n",
      "                'input_reads': 55442,\n",
      "                'largest_contig': 29903.0,\n",
      "                'lineage': 'B.1',\n",
      "                'mean_coverage': 433.22,\n",
      "                'missense_variants': 3.0,\n",
      "                'n50': 29903.0,\n",
      "                'n_contigs': 1.0,\n",
      "                'non_host_reads': 99.95856639734825,\n",
      "                'num_mapped_reads': 48013.0,\n",
      "                'number_of_SNPs': 7,\n",
      "                'number_of_indels': 1,\n",
      "                'pct_mapped_reads': 99.53,\n",
      "                'trimmed_read_pairs': 24125},\n",
      " 'SAMPLE2_PE': {'clade': '19B',\n",
      "                'genome_fraction': 89.804,\n",
      "                'input_reads': 42962,\n",
      "                'largest_contig': 29903.0,\n",
      "                'lineage': 'A.2',\n",
      "                'mean_coverage': 343.94,\n",
      "                'missense_variants': 6.0,\n",
      "                'n50': 29903.0,\n",
      "                'n_contigs': 1.0,\n",
      "                'non_host_reads': 99.78127278408499,\n",
      "                'num_mapped_reads': 37942.0,\n",
      "                'number_of_SNPs': 7,\n",
      "                'number_of_indels': 1,\n",
      "                'pct_mapped_reads': 98.9,\n",
      "                'trimmed_read_pairs': 19160},\n",
      " 'SAMPLE3_SE': {'clade': '19A',\n",
      "                'genome_fraction': 97.432,\n",
      "                'input_reads': 49202,\n",
      "                'largest_contig': 29903.0,\n",
      "                'lineage': 'B',\n",
      "                'mean_coverage': 411.97,\n",
      "                'missense_variants': 37.0,\n",
      "                'n50': 29903.0,\n",
      "                'n_contigs': 1.0,\n",
      "                'non_host_reads': 99.90141237489016,\n",
      "                'num_mapped_reads': 46278.0,\n",
      "                'number_of_SNPs': 54,\n",
      "                'number_of_indels': 0,\n",
      "                'pct_mapped_reads': 99.18}}\n"
     ]
    }
   ],
   "source": [
    "headers = {\n",
    "    \"input_reads\": {\n",
    "        \"title\": \"# Input reads\",\n",
    "        \"description\": \"Total number of reads in raw fastq file\",\n",
    "    },\n",
    "    \"trimmed_read_pairs\": {\n",
    "        \"title\": \"# Trimmed reads (Cutadapt)\",\n",
    "        \"description\": \"Total number of reads remaining after adapter/quality trimming with fastp\",\n",
    "    },\n",
    "    \"num_mapped_reads\": {\n",
    "        \"title\": \"# Mapped reads\",\n",
    "        \"description\": \"Total number of Bowtie2 mapped reads relative to the viral genome\",\n",
    "    },\n",
    "    \"pct_mapped_reads\": {\n",
    "        \"title\": \"% Mapped reads\",\n",
    "        \"description\": \"Percentage of Bowtie2 mapped reads relative to the viral genome\",\n",
    "        \"suffix\": \"%\",\n",
    "    },\n",
    "    \"non_host_reads\": {\n",
    "        \"title\": \"% Non-host reads (Kraken 2)\",\n",
    "        \"description\": \"Total number of non-host reads identified by Kraken2\",\n",
    "    },\n",
    "    \"cov_mean\": {\n",
    "        \"title\": \"Coverage\",\n",
    "        \"description\": \"Mean coverage calculated by mosdepth\"\n",
    "    },\n",
    "    \"number_of_SNPs\": {\n",
    "        \"title\": \"# SNPs\",\n",
    "        \"description\": \"Total number of SNPs\",\n",
    "    },\n",
    "    \"number_of_indels\": {\n",
    "        \"title\": \"# SNPs\",\n",
    "        \"description\": \"Total number of INDELs\",\n",
    "    },\n",
    "    \"missense\": {\n",
    "        \"title\": \"# Missense variants\",\n",
    "        \"description\": \"Total number of variants identified as missense mutations with SnpEff\",\n",
    "    },\n",
    "    \"n_contigs\": {\n",
    "        \"title\": \"# Contigs\",\n",
    "        \"description\": \"Total number of contigs in SPAdes assembly as calculated by QUAST\",\n",
    "    },\n",
    "    \"largest_contig\": {\n",
    "        \"title\": \"Largest contig\",\n",
    "        \"description\": \"Size of largest contig in SPAdes assembly as calculated by QUAST\"\n",
    "    },\n",
    "    \"genome_fraction\": {\n",
    "        \"title\": \"Genome fraction\",\n",
    "        \"suffix\": \"%\",\n",
    "        \"description\": \"% genome fraction for SPAdes assembly as calculated by QUAST\",\n",
    "    },\n",
    "    \"n50\": {\n",
    "        \"title\": \"N50\",\n",
    "        \"description\": \"N50 metric for SPAdes assembly as calculated by QUAST\",\n",
    "    },\n",
    "    \"lineage\": {\n",
    "        \"title\": \"Pangolin lineage\",\n",
    "        \"description\": \"Pangolin lineage inferred from the consensus sequence\",\n",
    "    },\n",
    "    \"clade\": {\n",
    "        \"title\": \"Nextclade clade\",\n",
    "        \"description\": \"Nextclade clade inferred from the consensus sequence\",\n",
    "    },\n",
    "}\n",
    "\n",
    "from collections import defaultdict\n",
    "summarized_data = defaultdict(dict)\n",
    "for s in multiqc.list_samples():\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"fastp\"):\n",
    "        summarized_data[s][\"input_reads\"] = data[\"summary\"][\"before_filtering\"][\"total_reads\"]\n",
    "\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"Cutadapt\"):\n",
    "        summarized_data[s][\"trimmed_read_pairs\"] = data.get(\"pairs_written\")\n",
    "\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"Bowtie 2 / HiSAT2\"):\n",
    "        summarized_data[s][\"pct_mapped_reads\"] = data.get(\"overall_alignment_rate\")\n",
    "\n",
    "    if data := multiqc.get_general_stats_data(sample=s):\n",
    "        summarized_data[s][\"non_host_reads\"] = data.get(\"Kraken.pct_unclassified\")\n",
    "        summarized_data[s][\"mean_coverage\"] = data.get(\"Mosdepth.mean_coverage\")\n",
    "\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"Mosdepth\"):\n",
    "        summarized_data[s][\"cov_mean\"] = data[\"mean_coverage\"]\n",
    "        \n",
    "    if data := multiqc.get_module_data(sample=s, module=\"SnpEff\"):\n",
    "        summarized_data[s][\"missense_variants\"] = data[\"MISSENSE\"]\n",
    "\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"Bcftools\"):\n",
    "        summarized_data[s][\"number_of_SNPs\"] = data[\"number_of_SNPs\"]\n",
    "        summarized_data[s][\"number_of_indels\"] = data[\"number_of_indels\"]\n",
    "\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"VARIANTS: QUAST\"):\n",
    "        summarized_data[s][\"n_contigs\"] = data[\"# contigs (>= 0 bp)\"]\n",
    "        summarized_data[s][\"largest_contig\"] = data[\"Largest contig\"]\n",
    "        summarized_data[s][\"genome_fraction\"] = data[\"Genome fraction (%)\"]\n",
    "        summarized_data[s][\"n50\"] = data[\"N50\"]\n",
    "\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"Pangolin\"):\n",
    "        summarized_data[s][\"lineage\"] = data[\"lineage\"]\n",
    "\n",
    "    if data := multiqc.get_module_data(sample=s, module=\"Nextclade\"):\n",
    "        summarized_data[s][\"clade\"] = data[\"clade\"]\n",
    "\n",
    "summarized_data = {\n",
    "    s: {\n",
    "        k: v for k, v in d.items() if v is not None\n",
    "    } for s, d in summarized_data.items()\n",
    "}\n",
    "summarized_data = {s: d for s, d in summarized_data.items() if d}\n",
    "pprint(dict(summarized_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's show this table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:56.181077Z",
     "start_time": "2024-05-06T16:09:55.449985Z"
    }
   },
   "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>input_reads</th>\n",
       "      <th>trimmed_read_pairs</th>\n",
       "      <th>num_mapped_reads</th>\n",
       "      <th>pct_mapped_reads</th>\n",
       "      <th>non_host_reads</th>\n",
       "      <th>number_of_SNPs</th>\n",
       "      <th>number_of_indels</th>\n",
       "      <th>n_contigs</th>\n",
       "      <th>largest_contig</th>\n",
       "      <th>genome_fraction</th>\n",
       "      <th>n50</th>\n",
       "      <th>lineage-1</th>\n",
       "      <th>clade-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SAMPLE1_PE</th>\n",
       "      <td>55442</td>\n",
       "      <td>24125</td>\n",
       "      <td>48013.0</td>\n",
       "      <td>99.53</td>\n",
       "      <td>99.958566</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29903.0</td>\n",
       "      <td>98.094</td>\n",
       "      <td>29903.0</td>\n",
       "      <td>B.1</td>\n",
       "      <td>20A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMPLE2_PE</th>\n",
       "      <td>42962</td>\n",
       "      <td>19160</td>\n",
       "      <td>37942.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>99.781273</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29903.0</td>\n",
       "      <td>89.804</td>\n",
       "      <td>29903.0</td>\n",
       "      <td>A.2</td>\n",
       "      <td>19B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMPLE3_SE</th>\n",
       "      <td>49202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46278.0</td>\n",
       "      <td>99.18</td>\n",
       "      <td>99.901412</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29903.0</td>\n",
       "      <td>97.432</td>\n",
       "      <td>29903.0</td>\n",
       "      <td>B</td>\n",
       "      <td>19A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           input_reads trimmed_read_pairs num_mapped_reads pct_mapped_reads  \\\n",
       "SAMPLE1_PE       55442              24125          48013.0            99.53   \n",
       "SAMPLE2_PE       42962              19160          37942.0             98.9   \n",
       "SAMPLE3_SE       49202                NaN          46278.0            99.18   \n",
       "\n",
       "           non_host_reads number_of_SNPs number_of_indels n_contigs  \\\n",
       "SAMPLE1_PE      99.958566              7                1       1.0   \n",
       "SAMPLE2_PE      99.781273              7                1       1.0   \n",
       "SAMPLE3_SE      99.901412             54                0       1.0   \n",
       "\n",
       "           largest_contig genome_fraction      n50 lineage-1 clade-1  \n",
       "SAMPLE1_PE        29903.0          98.094  29903.0       B.1     20A  \n",
       "SAMPLE2_PE        29903.0          89.804  29903.0       A.2     19B  \n",
       "SAMPLE3_SE        29903.0          97.432  29903.0         B     19A  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from multiqc.plots import table\n",
    "plot = table.plot(\n",
    "    data=summarized_data,\n",
    "    headers=headers,\n",
    "    pconfig={\n",
    "        \"id\": \"summary_assembly_metrics\",\n",
    "        \"title\": \"Summary Assembly Metrics\",\n",
    "    },\n",
    ")\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display this data as a violin plot, which is a more compact representation for very wide or very tall tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:09:59.254005Z",
     "start_time": "2024-05-06T16:09:58.695339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"mqc_mplplot\" id=\"summary_assembly_metrics\"><img 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b9+eBx54IM3NzfnYxz6W1tbW4ravv/56fvazn2X16tW58cYb8+CDD+bFF19Mkjz66KNZt25d5syZk0984hPFbdra2vLkk09mx44dOXToUBYuXJhzzjknv/mbv1l1jIcOHcorr7ySX/3qV9m6dWu6urpy5pln5r3vfW+WLl3ap++OHTvy6KOP5s0338ysWbNy0UUXpbm5ueJ+e3p68sQTT+Tll1/O7t27s2zZstx888254IILBvy7JcmJEyfy5JNP5rXXXsv27dvT2tqa+fPn54477sjZZ59dcZuurq68/PLL2bZtW7Zu3Zq9e/dm9erVufrqq/s9b09PT37+859n/fr12b17d1auXJl3v/vdVcfzyiuv5Omnn05bW1sWLlyYK6+8MjfeeKMPjAnkzTffTFdX11gPg3FkoOoqw/X/d6W7BMqX3XUGMDFsP9yQv3q2JT9rm50kmTO9Nx9e1ZXPX3Eic6er2gVMfPe92pyvv9KUzfvmJEkuWNCdT1/cld88v3OMRwZw6g6daMh/e2F67nu1NYdPnPzufcvyrvz5dR1ZNsexHMB4VOm8ab1tw2EwAZdKNxYyOBO5KnpTU1NWrlw51sNgFKi5Q91mz56dj3zkI0mSRx55JLt27arZf9u2bfm7v/u7bN68Oeeff35mzJiR559/Pl//+teLfZYtW5YFCxZky5Yt+f73v19MEx47diz/8i//kp07d+ayyy5LkkyfPr1YNWX69OmZOXNmZsyYUdzX2rVrc/fdd2fTpk1ZuHBhrrvuusydOze7d++uOc62trbcf//96erqymWXXZYFCxbkjTfeyDe/+c1ipYfC6/nSl76UzZs3Z82aNTnnnHOybt26/OxnP6u433vuuSePPPJIurq6cvHFF2f79u35xje+kddff73meJLk+PHj+bu/+7s89thjOXDgQK666qqce+65OXbsWI4ePVp1u6NHj+bee+/N7t27s2rVqlxwwQXZtGlTvvGNb/T7O9xzzz3Fsd9www3p7u7O3XffXXG/zzzzTL7zne9k586dufzyy3PkyJE89NBDeeyxxwZ8LTBVDPbAr9K0OqOt1lyWvgAAUO7QiYb8u/tm5Wdtv75P4fCJhnx9fXP+0+MtYzgygOHxyJuN+U9PtGTzvl9XbN28rzH/z8db8sibo1/FFWC4/dnDM/L19c3FcEmS/KytKf/uvlk5dMJ5AAD6q1T1olpb6TbDqbwKenlbpfbydRM1tAHjkQomDMqqVaty+eWX58UXX8y9996bP/iDP6g4N1hvb29+8pOfJEl+//d/PytWrEhvb2/++Z//OVu2bMmbb76ZFStWJEk++MEPZvv27XnttdfyxBNP5F3veld+8pOfpL29PXfeeWfmzDl519Att9xSrLpx4403FoMnyckqJD/60Y+SJO9///tz/fXX1/2aVqxYkT//8z/P7Nkn78Ls6enJ//F//B85dOhQDhw4kIULFyZJHnrooSTJJz/5yVx00UVJkttuuy3f/e5388Ybb/TZ5+bNm7Nhw4acd955ueuuuzJt2rRcd911+ad/+qf8/Oc/z7nnnltzTI899lj279+fhQsX5vd///eLYxvIrFmz8u///b/P6aefXmy75557sn79+mzbti2LFy9OcnKqoQ0bNmTp0qX53Oc+V6zCsn79+txzzz199nn48OH85Cc/SUtLS/7sz/4sLS0tOXHiRP7Lf/kv+dnPfpZ3vOMdxYo0cCpqpZ5LD/7KU9DVEtKlfUfDYJ9LgAOAiea+V5v6XIwo9bO2pmza15jVC7orrgeYCL7xyvSa696z4tgojgYYLN+za9u0rzHPvV05LHf4REPue7Upn75YtSYAxp96K7dU61Op32ioNmV8rWsahf7VrpGM5+MdIZ6pQ8CEQbvjjjuycePGbN++Pc8++2xuuOGGfn32799fnNZl2bJlxUog55xzTrZs2ZK33nqrGDBpbm7Opz71qfxf/9f/lUceeSRHjhzJSy+9lIsvvjiXXHJJXWMqVAVZvHhxrrvuukG9nkKApWDatGlZvnx5NmzYkOPHjydJDhw4kK1bt6a1tTVr1qwp9m1tbc3555/fL2DyyiuvJEkuuuii9Pb2pru7uzjdztatWwcsD/bSSy8lSW699da6wyXJyfJTpeGS5GSAZv369Tl27Ncnwgr7v/766/tM8XPxxRf3C5hs2bIlSbJ69eo0NTWlu7s7jY2NOe+887Jly5bs2bMny5Ytq3uMpd56660hbcfQHDp0qE9VnlK1DmwAAJLkzX1zk1QPFr+9/2jObD4xegOa4Bxvwfjz3NvVv38/93ZjDh8+PIqjARheb++fnmRm1fV7D5/I4cNHRm9AAMC4N5hrR42Nja77jbIzzzxzTJ5XwIRBmzVrVj784Q/nnnvuyf33358LLrigX599+/YlSdrb2/O//W//W7/1hw4d6rO8aNGi3Hnnnfnud7+bZ599Nq2trfngBz9Y95j27t2b5GSYYignaXfv3p0XX3wxu3btysGDB7Nz584+6w8cOJAkWbp0aV37L0xH84Mf/CA/+MEP+q0/evRoWltbK27b0dGR9vb2JMlZZ501mJeR5OTf/KWXXkpbW1sOHTqUPXv29OtT+PcpVGeppfC3femll4rBlFJOsE1cpQcCtcrZDZSqFUwBgKlhdnPPWA8BYES1NvWkvavybNKtTd4DAQCAiWmo13nquXbE1CNgwpBcfPHF+eUvf5kNGzbkvvvu61fBoqfn5ImXhQsX5uqrr+63/ZIlS/q1dXR0FB+fdtppg5p2pVB2qbFx8HMiv/TSS/ne976XlpaWXHDBBbnmmmvywgsvZPv27cU+R48eTXKyQkg9Cq//+uuvz7x58/qtr7Wf0hJSlaYfqmX//v35+7//+3R0dGTVqlVZs2ZN9uzZkxdeeKFPv0IopJ7XUxjPBRdckHPOOaff+npCKtWMVbJuquro6EhXV9dYD2PYDBRuGUwQptL0PABAXwvnNNdcf/7pMzJnjqkT6+WYA8afCxf15rm3q68rr4AKMJGc0Vn7vOnCOdO9zwFABdWmqal0XaHSdYlK+5mM1yKamppc95siBEwYsg984AN5/fXXs3Xr1mzdurXPukWLFiVJOjs78453vGPAN8hdu3blBz/4QebPn58ZM2Zk+/btefTRR3PbbbfVNZZCyKE0FFKP7u7uYrjkT//0T4tfot54440++yq079+/v98+Ks0ptmTJkuzYsSPLli3LZZddNqgxzZgxI62trWlvb8+uXbsGNUXOfffdl46Ojtx1111ZtWpVkuTZZ5/tFzA57bTTsnfv3hw+fLg4dU+111L4t5w9e3Z+4zd+Y1CvBUZSreRs+fJQ+g638oPNeh+P5JgAYDA+vKor33hlenYc6f+59KHzO7Nsjrl2gYntC1ecyOfvrzx9xBeuMAUYjHeT7SLNcFu9oDsfOr8zP3ytf2h46ezefPrizjEYFQBUVu38eK3z5iN1LDCUaxC1+lfbH0wUgyuPACXmzJmT973vfRXXnXbaaVmyZEkOHTrUL9xQrrOzM9/+9reTJJ/85CfzqU99Ki0tLXniiSeyadOmPn1nzjx5oqd8Cptzzz03SbJt27Zs2LCh7tdQmKqnqampOGVNd3d3jhw5Od/oiRMnTyAVAiw7d+7Mjh07itu3tbXl8ccf77ff1atXJ0keffTR4j4G45JLLkmSPPLII+nsrP/L3a5du5Ik8+fPL7YVXmPpfs4444wkyS9+8Yvih3FXV1fuvffefvtcuXJlkuT5558v7h8YvIaGhj4HjvU+Hq6DzNID78Lj8rbS9kp9AJja5k7vzbd+62g+dH5nrjy9M61NPblqSVe+cMWJ/Od3dgy8A4Bx7tql3flv7zuWW5Z3Z8mskz+3LD/Zdu3S7rEeHsAp+8/v7MgXrjiRq5Z0pbWpJ1ee3pkPnd+Zb/3W0bEeGgDjUOE8celPrfXDqdr58VrnzQU2YHSoYMIpueKKK/LLX/4yW7Zs6dM+bdq0/OZv/mb+8R//Mffee2+2b9+e5cuXp729Pdu2bcuZZ56ZG2+8MUly//33Z+/evXn/+99frKbx8Y9/PN/4xjfyve99L3/8x39cDEysXr06DzzwQNatW5fW1tYsWbIk5513XubNm5fbbrstDz30UL797W/nmmuuyaJFi7Jt27Zs3749/+E//IeK458/f36WLl2aHTt25Nvf/naWLVuWl19+Obt3706SvPDCC1m0aFHmzJmTa6+9NmvXrs1//+//PZdffnn279+fV199Na2trX2m90mSNWvWZPXq1dm0aVP+7u/+Lu94xzsyY8aM7NixI7/61a/yu7/7uzWnALrlllvyy1/+Mtu2bct/+2//LZdffnk6Ojry+uuv58orr8w111xTcbsrr7wyTzzxRO65555ccsklaWtry6uvvpokeeWVV3LhhRdmyZIlue666/Lkk09mw4YN+ed//ucsWLAgmzZtSnNz/zsY5s2blzvuuCMPPPBA/vZv/zbvfve7s3DhwuzZsye/+tWvctNNNxVDKIxvK1eu7HewV/q4MLVTpeWB+pevr7Rcz3PW6i/ocGoGk6Ye6YoulVLmgy0nCMDYmDu9N//5nR05fvx4Ojs7M2PGjIrHkAAT1bVLu3Pt0mPFqWVNFwFMNn985Yn8wSWdOX78eJqbmzNjxoyxHhIAqT6Ne/njgtE4VzrQczhfO/zKAzSly+Xn9adNm9anT63+Q9lH+fp6npOpQcCEujQ1Vf5PpaGhIR/60IfyN3/zN8XlgrPOOit//Md/nB/84AdZt25d1q1blySZO3duseLIxo0b89xzz2X16tW57rrrittecMEFuemmm/LEE0/kO9/5Tj7/+c+noaEhCxYsyC233JKnnnoqDz74YBYvXpwvfvGLSZKbbrops2bNKgZQCiqFH0rHWQhPbNq0KZs2bcoVV1yRT37yk/nHf/zHvPjii5kzZ05uu+22vPe9782JEyfy4osv5tlnn01LS0ve/e53p7m5OQ8+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height=\"973px\" width=\"1100px\"/></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot.show(violin=True, flat=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add this plot into the report. We will prepend th new section to make it appear in the beginning of the report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:10:02.076567Z",
     "start_time": "2024-05-06T16:10:02.069151Z"
    }
   },
   "outputs": [],
   "source": [
    "module = multiqc.BaseMultiqcModule(\n",
    "    name=\"nf-core/viralrecon summary\",\n",
    "    anchor=\"custom_data\",\n",
    ")\n",
    "module.add_section(\n",
    "    name=\"De novo assembly metrics\",\n",
    "    anchor=\"de_novo_assembly_metrics\",\n",
    "    description=\"Summary of input reads, trimmed reads, and non-host reads. Generated by the nf-core/viralrecon pipeline\",\n",
    "    plot=plot,\n",
    ")\n",
    "multiqc.report.modules = [module] + multiqc.report.modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can write the updated report to a file. Since our new section is designed to replace general stats, we will tell MultiQC not to render the general stats table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:10:05.467507Z",
     "start_time": "2024-05-06T16:10:04.457222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;208m///\u001b[0m \u001b[1mhttps://multiqc.info\u001b[0m 🔍 \u001b[2mv1.22.dev0\u001b[0m\n",
      "\u001b[34m     update_config\u001b[0m | Report title: nf-core/viralrecon report\n",
      "\u001b[34m       file_search\u001b[0m | Excluding modules 'general_stats'\n",
      "\u001b[34m     write_results\u001b[0m | Data        : multiqc_report_data   (overwritten)\n",
      "\u001b[34m     write_results\u001b[0m | Report      : multiqc_report.html   (overwritten)\n",
      "LICENCE\n",
      "README.md\n",
      "\u001b[1m\u001b[36mdata\u001b[m\u001b[m/\n",
      "multiqc_config_illumina.yml\n",
      "multiqc_example.ipynb\n",
      "multiqc_report.html\n",
      "\u001b[1m\u001b[36mmultiqc_report_data\u001b[m\u001b[m/\n",
      "nf-coreviralrecon-report_multiqc_report.html\n",
      "\u001b[1m\u001b[36mplayground\u001b[m\u001b[m/\n"
     ]
    }
   ],
   "source": [
    "multiqc.write_report(\n",
    "    force=True,\n",
    "    title=\"nf-core/viralrecon report\",\n",
    "    filename=\"multiqc_report\",\n",
    "    exclude_modules=[\"general_stats\"],   \n",
    ")\n",
    "%ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a report, we can show it inside the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:10:10.346927Z",
     "start_time": "2024-05-06T16:10:10.337798Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"600\"\n",
       "            src=\"./multiqc_report.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x11c1edf70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython\n",
    "\n",
    "# Best if using Google Colab\n",
    "# IPython.display.HTML(filename='./multiqc_report.html')\n",
    "\n",
    "# Best if running locally\n",
    "IPython.display.IFrame('./multiqc_report.html', '100%', 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to restart the session from scratch, you can either restart the kernel, or call `multiqc.reset()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:10:14.842515Z",
     "start_time": "2024-05-06T16:10:14.825901Z"
    }
   },
   "outputs": [],
   "source": [
    "multiqc.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like the command line tool, all interactive commands will load the config in [the same order](https://multiqc.info/docs/getting_started/config/#configuring-multiqc). Any config passed directly to a function (like `module_order` or `exclude_modules` that we specified above) will apply to this function call on top of any config loaded from file. You can also manually load a custom user config with `multiqc.load_config()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-06T16:10:15.488636Z",
     "start_time": "2024-05-06T16:10:15.453043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;208m///\u001b[0m \u001b[1mhttps://multiqc.info\u001b[0m 🔍 \u001b[2mv1.22.dev0\u001b[0m\n",
      "\u001b[34m            config\u001b[0m | Loading config settings from: multiqc_config_illumina.yml\n"
     ]
    }
   ],
   "source": [
    "multiqc.load_config(\"multiqc_config_illumina.yml\")"
   ]
  }
 ],
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