microbiome_analysis_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9508260",
   "metadata": {},
   "source": [
    "This notebook is a template for a default 16s V4 marker gene analysis. I assume data arrive in terms for raw Illumina files (a complex directory structure of \\*.bcl files) and metadata describing the samples as excel file or another type of spreadsheet.\n",
    "\n",
    "Should the metadata not yet exist, I recommend 1) use https://qiita.ucsd.edu/iframe/?iframe=qiimp to produce an according excel template and 2) do **not** start any analysis until you know your samples. Any efforts without these data is a waste of time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4f1ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# toggle between \"notebook\" and \"inline\", if graphs are not directly displayes\n",
    "%matplotlib  inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71243014",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from ggmap.workflow import *\n",
    "\n",
    "# use the following two information to create according github repo and project directory on lummerland\n",
    "PROJECT_PI = 'YOURNAME'\n",
    "PROJECT_NAME = 'YOURPROJECTNAME'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fd655c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this object will hold many of the preprocessing data\n",
    "# be careful to re-set it as you will have to re-do multiple steps to recreate these infos\n",
    "prj_data = dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f910947f",
   "metadata": {},
   "source": [
    "## Load metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac9147b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "prj_data['metadata'] = pd.read_csv('PATH TO YOUR FILE', sep=\"\\t\", dtype=str, na_values=['not applicable'], index_col=0)\n",
    "prj_data['metadata']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f42587",
   "metadata": {},
   "source": [
    "# Process data\n",
    "## Initiate new project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b520e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prj_data = init_project(pi=PROJECT_PI, name=PROJECT_NAME, prj_data=prj_data, force=False)\n",
    "# Should you receive file permission errors, it could well be the case that Stefan already \n",
    "# executed this function (for you) and directory structure already exists. Try to set\n",
    "# force=True and re-execute!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee6ebdc",
   "metadata": {},
   "source": [
    "## Deposit raw data\n",
    "After calling the `init_project()` function above, you should start moving raw data into newly created directory structure, i.e.\n",
    "  - raw Illumina data\n",
    "  - Illumina demultiplexing sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0cea74",
   "metadata": {},
   "source": [
    "## BaseCalling + Demulitplex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04f1df09",
   "metadata": {},
   "outputs": [],
   "source": [
    "prj_data = project_demux(\n",
    "    'PATH TO YOUR ILLUMINA RAW DATA', \n",
    "    'PATH TO YOUR DEMUX-SHEET IN CSV FORMAT', \n",
    "    prj_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028bc0c2-65bd-4dfa-8df9-63f58caf6f67",
   "metadata": {},
   "source": [
    "### Sequencing Quality Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d73ba60c-4817-4c62-951b-76e8af701892",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_qc = QC(prj_data['paths']['demux'], dry=False, wait=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0bbce5a",
   "metadata": {},
   "source": [
    "## Trim primers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03172501",
   "metadata": {},
   "outputs": [],
   "source": [
    "prj_data = project_trimprimers(\n",
    "    \"GTGCCAGCMGCCGCGGTAA\", \n",
    "    \"GGACTACHVGGGTWTCTAAT\", prj_data)\n",
    "# we expect per sample fastq files to follow the default naming schema: _R1_001.fastq.gz\n",
    "# should this not apply to your data, supply the optional parameters: pattern_fwdfiles and/or r1r2_replace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cfbce33",
   "metadata": {},
   "source": [
    "## Deblur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ca99083",
   "metadata": {},
   "outputs": [],
   "source": [
    "prj_data = project_deblur(prj_data)\n",
    "# we expect per sample fastq files to follow the default naming schema: _R1_001.fastq.gz\n",
    "# should this not apply to your data, supply the optional parameters: pattern_fwdfiles (we never use reverse reads for Deblur, thus r1r2_replace is not available)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e0c2df",
   "metadata": {},
   "source": [
    "## SEPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "141aeb2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prj_data = project_sepp(prj_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e681d23",
   "metadata": {},
   "source": [
    "before starting big compute, double check sources of metadata and feature table. It is not unlikely that you partially use data from Qiita and mix with local metadata that lack qiita-study-id prefix like `14078.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0574bef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "qiita_metadata = any(map(lambda x: 'qiita' in x.lower(), prj_data['metadata'].columns)) and all(map(lambda x: re.match('^\\d+\\.', x) is not None, prj_data['metadata'].index))\n",
    "qiita_counts = all(map(lambda x: re.match('^\\d+\\.', x) is not None, biom2pandas(prj_data['paths']['deblur_table']).columns))\n",
    "if (qiita_metadata != qiita_counts) and (len(set(qiita_metadata.index) & set(biom2pandas(prj_data['paths']['deblur_table']).columns))) <= 0:\n",
    "    raise ValueError(\"Please double check that both, metadata and feature table stem from Qiita or other sources. It looks like sample indices do not match!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1650f3dc",
   "metadata": {},
   "source": [
    "## Rarefaction + Diversity + Emperor\n",
    "\n",
    "You have to run the below function at least twice: \n",
    " - First, to get rarefaction curves, which you will use to determine a rarefaction depth for samples to compute diversity values\n",
    " - Second, alpha- beta- and emperor computation need ONE specific `rarefaction_depth` value, which you have determined in step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a761e8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_control_samples = set(prj_data['metadata'][prj_data['metadata']['sample_type'] == 'control'].index)\n",
    "res_all = process_study(\n",
    "    metadata=prj_data['metadata'],\n",
    "    control_samples=idx_control_samples,\n",
    "    fp_deblur_biom=prj_data['paths']['deblur_table'],\n",
    "    fp_insertiontree=prj_data['paths']['insertion_tree'],\n",
    "    rarefaction_depth=7000,\n",
    "    fp_taxonomy_trained_classifier='/homes/sjanssen/MicrobiomeAnalyses/References/Q2-Naive_Bayes_classifiers/gg-13-8-99-515-806-nb-classifier_2021.4.qza',\n",
    "    tree_insert=prj_data['insertion_tree'],\n",
    "    is_v4_region=True,\n",
    "    emperor_fp=prj_data['paths']['Generated/Emperor'],\n",
    "    emperor_infix='_deblur_',\n",
    "    dry=False,\n",
    "    use_grid=True,\n",
    "    fix_zero_len_branches=False, # since this has already been corrected via the project_sepp call\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad8fe60",
   "metadata": {},
   "source": [
    "### stratify rarefaction curves by metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf5cb30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = rarefaction_curves(\n",
    "    res_all['counts_plantsremoved'], \n",
    "    reference_tree=prj_data['paths']['insertion_tree'], \n",
    "    control_sample_names=idx_control_samples, \n",
    "    fix_zero_len_branches=False, # since this has already been corrected via the project_sepp call\n",
    "    sample_grouping=prj_data['metadata'][\"diseased\"], \n",
    "    dry=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90302ffa",
   "metadata": {},
   "source": [
    "# Explore Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732b74dc",
   "metadata": {},
   "source": [
    "## Taxonomy Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a00fe091",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = plotTaxonomy(res_all['rarefaction']['results'], prj_data['metadata'], file_taxonomy=res_all['taxonomy']['RDP']['results']['Taxon'],\n",
    "                 group_l1='sample_type', rank='Family',\n",
    "                 print_sample_labels=True)\n",
    "p[0].set_size_inches((15, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729a2aa8",
   "metadata": {},
   "source": [
    "## Correlation: within metadata\n",
    "Without knowing too much about the metadata, let is check if the different columns correlate with each other. If so, we have redundant information and it might be worth ignoring one of the columns in downstream analysis.\n",
    "\n",
    "We need to classify metadata columns into four categories to optimally harvest statistical power:\n",
    "  - dates, e.g. `collection_timestamp`: `2021-12-06 00:00:00`\n",
    "  - interval data, e.g. `host_height` in cm: `186`\n",
    "  - ordinal data, e.g. `shower_frequency`: `once per week` < `every day` < `multiple times per day`\n",
    "  - categorial data, e.g. `genotype`: `balb`, `black6`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "870b4ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_ignore = set()\n",
    "for c in prj_data['metadata'].columns:\n",
    "    if prj_data['metadata'][c].dropna().unique().shape[0] == 1:\n",
    "        # ignore column, since it contains only ONE value for all samples\n",
    "        cols_ignore.add(c)\n",
    "    if prj_data['metadata'][c].dropna().unique().shape[0] == prj_data['metadata'][c].dropna().shape[0]:\n",
    "        # ignore column, since every sample comes with another value\n",
    "        cols_ignore.add(c)\n",
    "\n",
    "cols_date = { # a dictionary where key = column name and value = format string to correctly parse date information\n",
    "    # 'collection_timestamp': '%Y-%m-%d %h:%m:%s'\n",
    "}\n",
    "cols_interval = set([\n",
    "    # host_height,\n",
    "])\n",
    "cols_ordinal = { # note this is a dictionary with keys = column names and values = lists of potential strings in the metadata ORDERED in the list\n",
    "    # 'shower_frequency': ['once per week', 'every day', 'multiple times per day'],\n",
    "}\n",
    "cols_categorial = set([\n",
    "    # 'genotype',\n",
    "    'diseased', 'sample_type'\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d759634",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use this output to decide how to classify remaining metadata columns\n",
    "# when done, this function should not output anything\n",
    "for c in prj_data['metadata'].columns:\n",
    "    if c in cols_ordinal.keys() or c in cols_ignore or c in cols_categorial or c in cols_date.keys() or c in cols_interval:\n",
    "        continue\n",
    "    print(c, prj_data['metadata'][c].value_counts(dropna=False))\n",
    "    print(\"--------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f10ff6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_corrmeta = correlate_metadata(prj_data['metadata'][list(set(prj_data['metadata'].columns) - set(cols_ignore))], categorials=list(cols_categorial), ordinals=cols_ordinal, dates=cols_date, intervals=list(cols_interval))\n",
    "display(res_corrmeta[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b51bb7",
   "metadata": {},
   "source": [
    "## Correlation: metadata with microbiome\n",
    "We are throwing a whole arsenal of statistical tests on multiple metrics to the data to find something that looks significant. Go through the resulting table of p-values and look for significant ones that you can further investigate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3174a524",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_diversity = correlation_diversity_metacolumns(\n",
    "    prj_data['metadata'][sorted(list(set(cols_categorial) | set(cols_date.keys()) | set(cols_ordinal.keys()) | set(cols_interval)))].fillna('NaN'),\n",
    "    list(sorted(set(cols_categorial) | set(cols_ordinal.keys()))),\n",
    "    res_all['alpha_diversity']['results'], \n",
    "    res_all['beta_diversity']['results'],\n",
    "    dry=False, wait=False, use_grid=True, dirty=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76e0c009",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.pivot_table(data=corr_diversity['results'], columns='column', index=['div','type','metric','test'], values='p-value').fillna(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0311afd",
   "metadata": {},
   "source": [
    "collapse above table to smallest p-value per column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e428f7d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_cols = {k for k,v in corr_diversity['results'].groupby('column')['p-value'].min().iteritems() \n",
    "            if v < 0.05 \n",
    "            if k != '__fake_qiime2_numcol'}\n",
    "display(corr_diversity['results'][corr_diversity['results']['column'].isin(sig_cols)].groupby('column')['p-value'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cb179b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset columns to those that significantly correlate with microbiome data\n",
    "# you should manually reduce the number of columns you want to investigate, \n",
    "# since every addition of a column costs a lot of statistical power and a too complex model might find nothing at all!\n",
    "model_cols_date = {k: v for k,v in cols_date.items() if k in sig_cols}\n",
    "model_cols_interval = {c for c in cols_interval if c in sig_cols}\n",
    "model_cols_ordinal = {k: v for k,v in cols_ordinal.items() if k in sig_cols}\n",
    "model_cols_categorial = {c for c in cols_categorial if c in sig_cols}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8dfaa53",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "usegrid = True\n",
    "\n",
    "max_rows = max(len(res_all['alpha_diversity']['results'].keys()), len(res_all['beta_diversity']['results'].keys()))\n",
    "fig, axes = plt.subplots(max_rows, 2, figsize=(5*2, 5*max_rows))\n",
    "for i, metric in enumerate(res_all['alpha_diversity']['results'].keys()):\n",
    "    redundancy_analysis_alpha(prj_data['metadata'][sorted(list(set(model_cols_categorial) | set(model_cols_date.keys()) | set(model_cols_ordinal.keys()) | set(model_cols_interval)))],\n",
    "                              res_all['alpha_diversity']['results'][metric],\n",
    "                              categorials=list(model_cols_categorial), intervals=list(model_cols_interval), dates=model_cols_date, ordinals=model_cols_ordinal,\n",
    "                              ax=axes[i][0], title='%s' % metric,\n",
    "                              dry=False, wait=False, use_grid=usegrid)\n",
    "for i, metric in enumerate(res_all['beta_diversity']['results'].keys()):\n",
    "    redundancy_analysis_beta(prj_data['metadata'][sorted(list(set(model_cols_categorial) | set(model_cols_date.keys()) | set(model_cols_ordinal.keys()) | set(model_cols_interval)))],\n",
    "                             res_all['beta_diversity']['results'][metric], metric,\n",
    "                             categorials=list(model_cols_categorial), intervals=list(model_cols_interval), dates=model_cols_date, ordinals=model_cols_ordinal,\n",
    "                             ax=axes[i][1], title='%s' % metric,\n",
    "                             dry=False, wait=False, use_grid=usegrid)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8118fb00",
   "metadata": {},
   "source": [
    "# Specific Analysis types\n",
    "All above analysis can be applied on any kind of dataset. The following assume specific experimental designs and/or hypothesis you want to follow, i.e. it might **not** make sense for you to execute them! Decide yourself.\n",
    "\n",
    "## Temporal Analysis\n",
    "You might have time series data, i.e. multiple samples from the same individual across time. To visualize the experimental design, you might want to compute a \"Gantt\"-chart of the samples timepoints per individual. Here is an example:\n",
    "![image.png](attachment:image.png)\n",
    "(from https://github.com/jlab/microbiome_pax5/blob/master/4Cohorts/pax5_cohousing_abx_bmt.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f046d0f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "chart, col, info = ganttChart(\n",
    "    prj_data['metadata'],\n",
    "    col_birth='birth_timestamp',  # metadata column that flags the start of the timeline, must be some parsable dates like 2022-10-09\n",
    "    col_entities='host_subject_id',  # metadata column that contains name of the person/animal which has been sampled over time, i.e. to know which samples belong to the same line\n",
    "    col_events='collection_timestamp', # metadata column that indicate the exact date when samples have been collected\n",
    "    # col_events_title='tp_bin35_title', # you might want to provide a metadata column to give sampling events more speaking names\n",
    "    # align_to_event_title='0 weeks', # by default, lines are drawn by actual date. However, often it makes sense to align them to the first timepoint of an intervention\n",
    "    )"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c4f36bb0",
   "metadata": {},
   "source": [
    "## Differential Abundance\n",
    "One typical line of investigation is the question for differentially abundant taxa between two groups of microbial samples. I suggest two alternative computational approaches for the very same question. \n",
    "  1. Ancom is the more conservative method (=if it reports taxa, they are probably really diff. abundant), \n",
    "  2. dsFDR is more liberal regarding correction for multiple hypothesis testing and less well accepted in the field, but reports more\n",
    "  \n",
    "### ANCOM\n",
    "Example: ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "274f85c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_ancom = ancom(\n",
    "    res_all['rarefaction']['results'],  # you can in principle also start from a feature table NOT being rarefied, i.e. res_all['counts_plantsremoved']\n",
    "    rank='Genus',  # feature table is collapsed at this taxonomic rank, as ANCOM struggles from too many features like individual ASVs. But try rank=\"raw\" as well!\n",
    "    taxonomy=res_all['taxonomy']['RDP']['results']['Taxon'],\n",
    "    grouping=prj_data['metadata']['host_genotype'],  # the metadata column that groups all samples into two conditions\n",
    "    dry=False, wait=False,\n",
    "    # following are plotting parameters only, i.e. won't change/trigger a re-run\n",
    "    min_mean_abundance_per_feature=0.007,\n",
    "    post_cache_arguments={'palette': {'B6J': 'blue', 'C': 'green'}, \n",
    "                          'feature_order': list(name_map.keys()), \n",
    "                          'hue_order': ['B6J', 'C']})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64236b33",
   "metadata": {},
   "source": [
    "### dsFDR\n",
    "You need to additionally import (and probably install first) the \"calor\" package: https://github.com/biocore/calour\n",
    "My code regarding this type of analysis is a bit ugly, since you need to \"export\" metadata as well as feature counts to actual files before importing them into the calor module :-/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "599114d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import calour as ca\n",
    "ca.set_log_level(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efa97efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas2biom('tmp_dsfrd.biom', res_all['counts_plantsremoved'])\n",
    "prj_data['metadata'].to_csv('tmp_dsfdr.tsv', sep=\"\\t\", index=True)\n",
    "\n",
    "dsFDR_experiment = ca.read_amplicon('tmp_dsfrd.biom', \n",
    "                                    'tmp_dsfdr.tsv', min_reads=0, normalize=10000)\n",
    "\n",
    "field = 'host_genotype'  # the metadata column that groups all samples into two conditions\n",
    "res_dsFDR_genotype = find_diff_taxa(dsFDR_experiment, prj_data['metadata'], [field],\n",
    "                                    # random_seed=43  # hard code a random seed for reproducible results\n",
    "                                   )\n",
    "\n",
    "if len(res_dsFDR_genotype) > 0:\n",
    "    p = plot_diff_taxa(res_all['counts_plantsremoved'],\n",
    "                       prj_data['metadata'][field], res_dsFDR_genotype,\n",
    "                       min_mean_abundance=0.0,\n",
    "                       colors_boxplot={'B6J': 'blue', 'C': 'green'}, \n",
    "                       color_barplot='gray'\n",
    "                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adc3e87",
   "metadata": {},
   "source": [
    "## SourceTracking\n",
    "Assess if a microbial community might be the result of \"sourcing\" bacteria from different other microbial communities. Imagine you shake hands with four different people. You sample your hand and the four hands of the others and wonder to what degree your microbiome is composed alike the four other hands microbiome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d0ecfa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sourcetracker2(res_all['counts_plantsremoved'],\n",
    "               prj_data['metadata'],\n",
    "               col_envname='st2_env',  # names of the other four people in our example. You might also have samples them multiple times. Thus, multiple samples form the same \"source\"\n",
    "               col_type='st2_type',  # define which samples to use as 'Source' and which are the 'Sinks'\n",
    "               dry=False, wait=False)"
   ]
  }
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