microbiome_analysis_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e25264b",
   "metadata": {},
   "source": [
    "# Microbiome Analysis in R\n",
    "\n",
    "In this tutorial, we introduce basic microbiome analysis starting from the \n",
    "processed output from 16S rRNA sequencing, i.e. feature table. Specifically, \n",
    "we will look at the microbial compositions from different body sites of human, \n",
    "including gut, tongue, left palm, and right palm. \n",
    "\n",
    "This tutorial is adapted from https://www.yanh.org/2021/01/01/microbiome-r/. \n",
    "\n",
    "Input data is sourced from Caporaso et. al, 2011 (https://pubmed.ncbi.nlm.nih.gov/21624126/)\n",
    "and processed using QIIME2.\n",
    "\n",
    "Data used in this tutorial were sequenced on an \n",
    "Illumina HiSeq using the Earth Microbiome Project hypervariable region 4(V4) \n",
    "16S rRNA sequencing protocol. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cb6fc5e",
   "metadata": {},
   "source": [
    "# 1. Load Packages\n",
    "\n",
    "Uncomment and run this chunk of code to install all required packages at once.\n",
    "\n",
    "Also **key note**: for `tidyverse` installation: let's check that your environment has the tidyverse requirements. To ensure doing so, we are working in the `med263_R_jupyter` environment made in the beginning of the quarter, and running the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb6317c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run below in terminal\n",
    "# conda install -c r r-tidyverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb6b32b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# p1 <- c(\"tidyverse\", \"vegan\", \"BiocManager\")\n",
    "# p2 <- c(\"phyloseq\", \"DESeq2\", \"ComplexHeatmap\")\n",
    "# load_package <- function(p) {\n",
    "# if (!requireNamespace(p, quietly = TRUE)) {\n",
    "# ifelse(p %in% p1,\n",
    "# install.packages(p, repos = \"http://cran.us.r-project.aorg/\"),\n",
    "# BiocManager::install(p))\n",
    "# }\n",
    "# library(p, character.only = TRUE, quietly = TRUE)\n",
    "# }\n",
    "# invisible(lapply(c(p1,p2), load_package))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a529ba",
   "metadata": {},
   "source": [
    "If you prefer to install packages one at a time (for trouble shooting.etc), you \n",
    "can do it here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a862b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# install.packages('tidyverse', repos = \"http://cran.us.r-project.aorg/\")\n",
    "# install.packages('vegan', repos = \"http://cran.us.r-project.aorg/\")\n",
    "# install.packages('BiocManager', repos = \"http://cran.us.r-project.aorg/\")\n",
    "# library(BiocManager)\n",
    "# BiocManager::install(\"phyloseq\")\n",
    "# BiocManager::install(\"DESeq2\")\n",
    "# BiocManager::install(\"ComplexHeatmap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa15d08f",
   "metadata": {},
   "source": [
    "Now load all necessary packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e19c503e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Updating HTML index of packages in '.Library'\n",
      "\n",
      "Making 'packages.html' ...\n",
      " done\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages(\"R.utils\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0abbdee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n",
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      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
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      "========================================\n",
      "ComplexHeatmap version 2.18.0\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite either one:\n",
      "- Gu, Z. Complex Heatmap Visualization. iMeta 2022.\n",
      "- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "    genomic data. Bioinformatics 2016.\n",
      "\n",
      "\n",
      "The new InteractiveComplexHeatmap package can directly export static \n",
      "complex heatmaps into an interactive Shiny app with zero effort. Have a try!\n",
      "\n",
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      "  suppressPackageStartupMessages(library(ComplexHeatmap))\n",
      "========================================\n",
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   ],
   "source": [
    "library(tidyverse)      \n",
    "library(vegan)          \n",
    "library(BiocManager)\n",
    "library(DESeq2)\n",
    "library(ComplexHeatmap)\n",
    "library(phyloseq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e138331f",
   "metadata": {},
   "source": [
    "# 2. Load Data\n",
    "\n",
    "After sequencing, you will get some (fastq/fast5/h5) files from the sequencing. \n",
    "These files contain the nucleotide information. If the sequenced sample is a mixed \n",
    "community, i.e. metagenomics, you need to identify where the reads come from \n",
    "(specific taxa and samples). \n",
    "\n",
    "Established microbial pipelines such as QIIME2 can convert these nucleotide \n",
    "information to the following outputs: \n",
    "\n",
    "* Feature table (Necessary), a matrix containing the abundances of detected \n",
    "microbial features (OTUs, ASVs, microbial markers)\n",
    "* Taxonomy table (Optional), an array indicating the taxonomic assignment of \n",
    "features, which can be integrated in biom format.\n",
    "* Phylogenetic tree (Optional), a phylogenetic tree indicating the evolutional \n",
    "similarity of microbial features, potentially used when calculating phylogenetic \n",
    "diversity metrics (optionally integrated in biom format).\n",
    "* Metadata, a matrix containing the infomation for analysis (optionally \n",
    "integrated in biom format).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7ce9091",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load otu table (feature table)\n",
    "otu <- read.table(file = 'feature-table.tsv', sep = '\\t', header = T, row.names = 1, skip = 1, comment.char = \"\") # 770 otus, 34 samples\n",
    "\n",
    "# load taxonomy (feature metadata)\n",
    "taxonomy <- read.table(file = 'taxonomy.tsv', sep = '\\t', header = T, row.names = 1)\n",
    "\n",
    "# load metadata\n",
    "metadata <- read.table(file = \"sample-metadata.tsv\", sep = \"\\t\", header = T, row.names = 1)\n",
    "\n",
    "# phylogenetic tree will be imported later"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "603c022a",
   "metadata": {},
   "source": [
    "# 3. Clean Data\n",
    "\n",
    "Let's do some data cleaning for our taxonomy table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c7b95ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mExpected 7 pieces. Missing pieces filled with `NA` in 201 rows [2, 3, 5, 8, 18, 19, 24, 37, 41, 42, 44, 45, 49, 61, 64,\n",
      "66, 82, 84, 86, 90, ...].”\n"
     ]
    }
   ],
   "source": [
    "# clean the taxonomy\n",
    "tax <- taxonomy %>% \n",
    "  select(Taxon) %>% \n",
    "  separate(Taxon, c(\"Kingdom\", \"Phylum\", \"Class\", \"Order\", \"Family\", \"Genus\", \"Species\"), \"; \")\n",
    "\n",
    "# fill up NA values\n",
    "tax.clean <- data.frame(row.names = row.names(tax), \n",
    "                        Kingdom = str_replace(tax[, 1], \"k__\", \"\"), \n",
    "                        Phylum = str_replace(tax[, 2], \"p__\", \"\"), \n",
    "                        Class = str_replace(tax[, 3], \"c__\", \"\"), \n",
    "                        Order = str_replace(tax[, 4], \"o__\", \"\"), \n",
    "                        Family = str_replace(tax[, 5], \"f__\", \"\"), \n",
    "                        Genus = str_replace(tax[, 6], \"g__\", \"\"), \n",
    "                        Species = str_replace(tax[, 7], \"s__\", \"\"),\n",
    "                        stringsAsFactors = FALSE\n",
    "                        )\n",
    "\n",
    "tax.clean[is.na(tax.clean)] <- \"\"\n",
    "tax.clean[tax.clean  == \"__\"] <- \"\"\n",
    "\n",
    "for (i in 1:nrow(tax.clean)){\n",
    "  if (tax.clean[i,7] != \"\"){\n",
    "    tax.clean$Species[i] <- paste(tax.clean$Genus[i], tax.clean$Species[i], sep = \" \")\n",
    "  } else if (tax.clean[i,2] == \"\"){\n",
    "    kingdom <- paste(\"Unclassified\", tax.clean[i,1], sep = \" \")\n",
    "    tax.clean[i, 2:7] <- kingdom\n",
    "  } else if (tax.clean[i,3] == \"\"){\n",
    "    phylum <- paste(\"Unclassified\", tax.clean[i,2], sep = \" \")\n",
    "    tax.clean[i, 3:7] <- phylum\n",
    "  } else if (tax.clean[i,4] == \"\"){\n",
    "    class <- paste(\"Unclassified\", tax.clean[i,3], sep = \" \")\n",
    "    tax.clean[i, 4:7] <- class\n",
    "  } else if (tax.clean[i,5] == \"\"){\n",
    "    order <- paste(\"Unclassified\", tax.clean[i,4], sep = \" \")\n",
    "    tax.clean[i, 5:7] <- order\n",
    "  } else if (tax.clean[i,6] == \"\"){\n",
    "    family <- paste(\"Unclassified\", tax.clean[i,5], sep = \" \")\n",
    "    tax.clean[i, 6:7] <- family\n",
    "  } else if (tax.clean[i,7] == \"\"){\n",
    "    tax.clean$Species[i] <- paste(\"Unclassified \",tax.clean$Genus[i], sep = \" \")\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706d8699",
   "metadata": {},
   "source": [
    "# 4. Build a phyloseq object\n",
    "\n",
    "Many microbiome analysis (alpha diversity, beta diversity) are standardized by \n",
    "the package phyloseq. In order to run these analyses using phyloseq. Let's build \n",
    "a phyloseq object first. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3dba308a",
   "metadata": {},
   "outputs": [],
   "source": [
    "OTU = otu_table(as.matrix(otu), taxa_are_rows = TRUE)\n",
    "TAX = tax_table(as.matrix(tax.clean))\n",
    "SAMPLE <- sample_data(metadata)\n",
    "TREE = read_tree(\"tree.nwk\")\n",
    "\n",
    "# merge all data to build a phyloseq object\n",
    "ps <- phyloseq(OTU, TAX, SAMPLE,TREE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736decfe",
   "metadata": {},
   "source": [
    "# 5. Rarefy samples \n",
    "\n",
    "Let's take a look at the rarefaction curve first. The curve is created by \n",
    "randomly re-sampling the pool of N samples several times and then plotting the \n",
    "average number of species found in each sample. Generally, it initially grows \n",
    "rapidly (as more common species are found) and then slightly flattens (as the \n",
    "rarest species remain to be sampled).\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "92e3ca9b",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d7d616",
   "metadata": {},
   "source": [
    "As we can see, a sequencing depth of 1000 has captured most taxa. To \n",
    "minimize the bias from varying sequencing depth. Rarefaction is recommended \n",
    "before calculating the diversity metrics. To be consistent with the original \n",
    "publication, all samples are rarefied (re-sampled) to the sequencing depth of \n",
    "1103. Note that since it's a random sampling process, the result is unlikely to \n",
    "be identical as the publication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf463f39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`set.seed(1)` was used to initialize repeatable random subsampling.\n",
      "\n",
      "Please record this for your records so others can reproduce.\n",
      "\n",
      "Try `set.seed(1); .Random.seed` for the full vector\n",
      "\n",
      "...\n",
      "\n",
      "3 samples removedbecause they contained fewer reads than `sample.size`.\n",
      "\n",
      "Up to first five removed samples are: \n",
      "\n",
      "\n",
      "L3S242L3S341L3S360\n",
      "\n",
      "...\n",
      "\n",
      "104OTUs were removed because they are no longer \n",
      "present in any sample after random subsampling\n",
      "\n",
      "\n",
      "...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "set.seed(111) # keep result reproductive\n",
    "ps.rarefied = rarefy_even_depth(ps, rngseed=1, sample.size=1103, replace=F)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c45992",
   "metadata": {},
   "source": [
    "# 6. Alpha diversity\n",
    "\n",
    "Alpha diversity metrics assess the species diversity with the ecosystems, \n",
    "telling us how diverse a sequenced community is.\n",
    "\n",
    "Some common alpha diversity metrics:\n",
    "- observed: number of unique species identified in each sample\n",
    "- Shannon: shannon diversity index, which combines species richness and evenness\n",
    "\n",
    "\n",
    "## 6.1 Plot Observed and Shannon index across communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96de702d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+nX13ZlHQ5IHoCUGCPV6nnR+EMBUoEKH0T/tiO0zmh5ScqwbsEISO6HdOgm5XOk\nh68YfihAKlAhSK/2oCqkZLKDcTgguR9SA23LhlGGHwqQClQI0osDNUjnCeuSAJL7Id2hQXrV\n8EMBUoEKQer+SKYKaWLJC4zDAcn9kCprkBoYfihAKlAhSLtKPTH3/nYLY0syr8QJJqTwAeaq\nA0hF1kaD9D/DDwVIBSq8+nvZbcouXmHNWPfrCyoksz0OSEV2/h4F0ouGP4wHpIL96QPZqyvj\nJnxyiH04ILkfEk0d8fhTM7ONPxQgFagQpL4fXjI4PJiQnnrBXIAUMHwga7rCa+1ImcZfMW/V\noISVDYDEn2chZX3bujy5teNG9uGABEj8eRaSXOby2JtI9cFY/c0aIPHnZUhyGSuexWldmAMk\n/jwNKefHrlVJhZ2MwwEJkPjzLqSsVR3uINc3/yaLdTggARJ/noU07FZSKvwz5k9jKSABkpk8\nC+m5uv9jXcvgD5DcAOmrfjpJz+r9xM9FPBQgFagQJOP/BwDJDZBGmt4qRPqqiIcCpALhrOYh\nAWn9CVPNBiS9cFbzkIDE+osxQIsBSS+c1RyQ9AMk3f70OVLGGUOb2gESIJnIq5DSxj4cRsL+\n1t2hu1EAEkuAZDukM4+SW+vFhlcl1y9gHg5IgMSfRyG9eM2EDOV6dfVSW1iHAxIg8edNSJvI\nOP+to7e9xDockACJP29CGnDT1dybI0qw7ikLSIDEnzchNfy/vJvrcYBI5gCJP29Ceq5Z3s2D\nBOdHYi2kIf33ZVPV9SSkuvknkD2ILRuYC2FIo3WcvCQ9qUdpQxHTApKlARJbQYSkV6rUhefB\nXA+pROncSgESc4AUsBCF1L1ugbYzDgckQApYiELiCpAAKWCAxB4gAVLAAIk9QAKkgAESe4AE\nSAEDJPYACZACBkjsARIgBQyQ6O2nGId7AFK/A6baD0iBCmFIV+YPHTBgQP8GpKjDmBWVByCZ\nDpAClCp15nkwD0BKvk87hlCJl1mP3ABIgBSopQ9KNRsYOGxBbh6ANKrc7L3PjPhtcP2LrMMB\nCZAC9D0pKdUg1ZMND/QApKj2lL74AaWTIliHewBSh2WmWgpIRfeoComMNjzQA5BeHkBpvUmU\nppU4zjjcA5Dcudbuy3aNXp/tP4Hy+k4N2izL/YZTIPmu0SBFG34wD0BqX9dHW7ShNOvazYzD\nAckeSF9HJyRtfe0z9faByCVnfmq0zv8dp0Cit2iQOhoe2Flq295Ur9oOaS15zDet1JSNHQnr\nPsSAZA+kBGW16rsj1duTh8sXs3r5v+MYSB00SPGGB3Y2/7bVbkh02oM07QlCSHvW4YBk2wey\nOXtitB21uy6SLzY29Gl3OwZSam0F0jvGHyz/wNlFd1jqrfMTzEfcFthfP5DNXBa3hnk4INkF\naX5EkxXardjv5Ivd4cobiz4xMY2rOAQSzflEqrfN3OMW2UmpnwWzmg2bCLkUUnLit9HL1Vux\n38sXu8Ivy5etn332mepOgcS7ZYNeB6SuNvzF0aswpG9aPI89ZI1l47Z2S7U1Yt2Vl3YbGjnt\npZ1FkHyDbpCq/cOKv3TmKgRpJQm7q7oa1tqxZg+k4QupsupOvT11qHwxo7//O16HNIGUkqqR\nSkniZzZXIUgx1Vg/P8obAUi2QFrUZH3SjtZx9OLUU/Rwg8Vn1jTMPVy7xyH5blMhkXeFz2yy\nQpBefMvocECyB5Lvi/aNXv8wnR4L301pQufItqtyv+NxSMlEg9RB+MwmKwTpzeaBfixQgIT9\nkQJmBaSc6zVIQ4XPbLJCkI7eE29wOCABUsAseY/0lgrp+kTxM5srH9Loli1fb1ziqZiWSnsZ\nhwMSIAXMEkhXG8mQbuVZHGsrcBD9kgXCQfRZA6SAWfQ50moplnk3n+CFD2QBST9HQXLHlg1X\nEvedNTAckAApYCEMaUXtMEJIxd5prMMBCZACFrqQ4kvc/lqvvu0eIa+yDgckQApY6EKKqX1Z\nvR5BDjMOByRACljoQnphmnadGraJcTggAVLAQhdSmwna9YkSIXSASEDSD5B0KwRpW80DylVm\ni7asw3kgndxywfAYQGINkOypEKSZz5Z8qkXbBhVva9+hfXum3c2NQzr2EiEl2l42OAqQWAMk\neyoEqS4pGMtww5CynlTnbmNsFAUk1gDJngpxSc/MKhDLcMOQvtOQhp0wNgyQWAMkewr2JkIf\nEFKuQilCfjL6SIDEFiDZUz6kCQWPscf6H94wpMWEVJRuJsbXUQASW4BkT/mQCr1BimccbhhS\n6j0qpOeNjaKAxBog2VM+pD4DD1IAACAASURBVOyC7498jMONr7Xbeo8M6Z+G3yIBEmOAZE9F\nvUfyrYm2cMuG9D7SVI7jkgESW4BkT3+FdGJUdVLCysNxzZLWGh4DSKwBkj39CVLW0vCSpNqw\nY6zDAQmQAha6kA72rUiuK9uB9Q0SBSRAKiZXQYp/QXqqv4lDIedDuvJJXULqzEh5cICB4YAE\nSAFzE6T5jymng3mJf4J8SC+QKn12y9eAZChACpibINXSTqz0EfcEBT5HKtlsqXIyc0AyFCAF\nzEWQ9vnPUPYa9wz5kPa+eSu57a1dgGQsQAqYiyDtEQiJ0qufyu+S/nkrIBkJkALmIkj0MXEv\n7dT2v30bua41+xalgARIAXMTpHnqm6QX+Cf4yweyGZ8/H0Zq7mMcDkiAFDA3QaI/PC892Ydp\n16GiK2oTocS+FeMZhwMSIAXMVZDMZsMhiwHJygDJngAJkPQDJN2CD2l7Q6kt87Z8+QESW4Bk\nT0GHNEvZse86w3uaAxJjgGRPwYZ0vJy6h2xVw+tHAIktQLKnYEOa6z9mwy9GHwmQ2AIkewo2\npBl+SBuNPhIgsQVI9hRsSL9okMqlGn0kQGILkOwp6Csb2quQphp+JEBiC5DsKeiQMif+Q6q9\n0PgjARJbgGRP+EAWkPQDJN0ACZD0AyTdAAmQ9AMk3QAJkPQDJN3EQ/Kl6vSB9J3OT6QV8UiA\nxBYg2ZN4SBcl0xW1oyIgsQVI9mQFpP/rbK4nAYk/QLInKyB1NzcnfRWQ+AMkewIkQNIPkHQD\npJCAVOcJU/0TkPQCpJCAZDpA0gmQAAmQBARIIQGp7xhTtQMkvQApJCBhZYPVARIg6QdIugES\nIOkHSLoBEiDpB0i6ARIg6QdIugESIOkHSLoBEiDpB0i6ARIg6QdIugESIOkHSLoBEiDpB0i6\nARIg6QdIugESIOkHSLoBEiDpB0i6ARIg6QdIugESIOkHSLpZAemNg+Z6CZD4AyR7wnHtAEk/\nQNINkABJP0DSDZAAST9A0s0KSM2Wmus5QOIPkOwJa+0AST9A0g2QAEk/QNINkABJP0DSDZAA\nST9A0g2QAEk/QNINkABJP0DSDZAAST9A0g2QAEk/QNINkABJP0DSDZAAST9A0g2QAEk/QNIN\nkABJP0DSDZAAST9A0g2QAEk/QNINkABJP0DSDZAAST9A0g2QAEk/QNINkABJP0DSDZAAST9A\n0g2QAEk/QNINkEIC0o7LploASHoBUkhAMh0g6QRIIQDps046Sc/o/cRGjkUBJPYAyQ2QdKsd\nK35BAMlIgARIAUuVOlsxLSCxBkgmcg6kZQ9JNRscFj8vILEWANJbx831NCAFygpIq0hJqQap\nnix8YkBiLQAk0wFSoKyA9KgKiYwWPjEgsVYkpPP9dWosddT5ifeKmBaQlCyA5LuGlKhxFyHR\nwmcGJNaKhKTbZOkXjlGApGTFX6TyRK2j8IkBiTVAMpFjIHXQIMULnzh0ID3dxFx1AIk/x0BK\nqa04GiJ+4lCBdOlfxVX7scdqSbUee6xOcT8UzrMogKTmGEg0e8Hbw3+2YN5QgVR8XxFSUbqZ\nhJ0397hFBEhqzoFkVRZByj6UYmZ4sCHR2iqk9uYetqgASQ2QuPKNvoGQV47wTxB0SDlv3C1V\nMfmPt8gASQ2QuBqvrhl5OJ17gqBDonSWtNbcgxYdIKkBEk+ZN2rrGOdwzwBIgARI9CghJcuX\nI4R/ZkACJECiKSVJWakKIRO4ZwAkQAIkSqNUSDfwr20AJEACJErP1ZYh3fgF/wSABEiAJJcz\nS4o9bWI8IAESICntl0aaGQ5IgARISoDkD5DUAIkzQPIHSGqAxBkg+QMkNUDiDJD8AZIaIHEW\nLEgr58stlG8cX/Zl/iHZAQmQgp3LIbXvGxcXN53SdQ2Gjmu8MPdeB0EaKa3nGAVISoAUPEjR\nG9Sr7OZLKE2ITPLf6xhIic9Wlm5+M9PwOEBSAiR6oLn0zDwf/3hWSL76WzeuSqR0T0Sa/FXs\nSv/dToGU/jCpLF1Pehke6FpIl/bnfg6fslMp91cbIHG1uYyyrV0H/glYIV0Obzr4nchZdHVz\n5aves+WL8wkJCQ1KOgPSAqJCKmV4b2GXQsqJa/BGo17a/9ufItvKfe//DiBx9aC29fc67glY\nIaWvOEzptvBdK1orXw2S3yzRNcrRS29wBqRRGiSyy+hAl0JaHn2UXmo7Q729otDvUUAKUM/w\nYqr38MOPSI89/PDTxf1Q+O/FTG9s9XfbhdpfpF7KnoR/zJkz5+VSzoA0U4MUlqT/o4VzKaSd\nCfLFlEHq7YW9sk9dzvsOIAUoVnrBXE9Je4qZnhVStvpP9PUl+8JT5euYVf67nfIeKekOFVJR\nR/cuPpdCUsru8ql6PatVi+iI4crTQn9NSPiiHCAVWaxk8nHHCIG0N3w/pTsiDuS0XCS/Km90\n0X+3UyDR+EoypKfPGR7nXkjZE7trh+pYOfkkPfnGZOVmlPxiuwYgFZlDINFJTSaOa/QJpZsb\nDhvTaHnuvY6BRFO7SnM41l66FlJK//6pBb78pqly+Wlc3JAKgFRkToFEd3/1TaJyfWLp4sS8\nO50DKcS2bEhq90F2wa/XNvD/FvH+e6RfpPZXOYY5BlKRAZI9kNLbzc+7PWGufDG5h/8rr0PK\n7lxKqlaN418QIDEWUpBmtYyXW09Pd/idrqs/e+20Bjv83+GA5Fv8+PMbBC+gZQ0lMiRS4YTh\ngYDEWEhBmt5PaTg92+8Ipb9OeueDw7nfMQ7p6n+IdB8xey6eIOUrr0LiOBEgIDEWUpACZxxS\nX6JAIostWRzRJRNS8q5bCMfGPIDEGCCpGYdUTYPU1JLFEZ3vJu3Ywsa31AYkxgBJzTikChqk\nly1ZHOENUh3dcszwQEBiDJDUjEN6VoPUx5LFEV7WG7KjKquNDwQkxgBJzTikhDIKpEpnLVkc\nCzqy9KcrHMMAiTFAUuNY/b26lnR/vX1WLIyTAiTGAEmN7wPZFuIXxGkBEmOApOb1LRu4AyTG\nAEkNkAIESIwBkhogBQiQGAMkNUAKECAxBkhqgBQgQGIMkNQAKUCAxBggqQFSgACJMUBSA6QA\nARJjgKQGSAECJMYASQ2QAgRIjAGSGiAFCJAYAyQ1QAoQIDEGSGqAFCBAYgyQ1AApQIDEGCCp\nAVKAAIkxQFIDpAABEmOApAZIAQIkxgBJDZACBEiMAZIaIAUIkBhzFaST86dYdLRtQAoQIDHG\nCymyt7l4IM27gRDySjrH8uoGSAECJMZ4IZnO+D/cPWXVw4Vactx6QAoQIDHGBynztE4DpM06\nP3He8IMO0I5ffT3HGQZ1A6QAARJjfJB0e0c6IHzOdoRUqFqSkMv6P2o4QAoQIDFmCaQjr1aV\n7pgo+i/HOEKqSaXIXYKnVQOkAAESY1ZAuvIQqSqVJe8LnvZcZRXS/wRPqwZIAQIkxqyA9BFR\nIZXPEjzvjtrVpFveFTypFiAFCJAYswJSbw0SOS585m6S8TP8MAVIAfIcpC/rSq9uMveoReSb\n97QUuUP/54w1VoN0jfi1An2k08LnVAOkAHkN0hBSUbqZfGHuYf9aN1JZur70KsGz/n6dCsmC\ns0ICUrDzGKT9RIV0i+BP9bcQBRL5W47Yaemn18mQHjf+SZFugBTsPAZplgaJJJh73D83XoNE\nhH/mczxGmilapxIgBTvvQVLbYu5x/9x4/7TGV33oNUISPycFpOAXK31vrs6OgnRA+wdf4aq5\nx/1zP2vT3i3+jwcgeaRY81tVOgkSHa7+i//K3MP+tbeUWUuvET0tIHkmr0GiXzd8vPnP5h61\nqD6P+GfLXeKnBSSv5DlILguQPFKstNNcvQDJTIDkkTy21s51AZJHAiR7AySPBEj2BkgeCZDs\nzRJISXVrSJUXip+XAlLAAMnerICUVV7dsW+O8IkpIAUMkOzNCkgdtF3NywmfmAJSwADJ3vgg\nJTxRXFKtWpL8v1rF/lAPvuUFpAABkr3xQdosvfCaqZpKXfmWF5ACFCt1MNfLgGQmXkjTzD1s\nMiAJDpsI2RsgeaSMK8V0+a4S10lVS5RYWNwPXSlu3wJA0gmQQqLFpKxUhbzMfwRDQNIJkEKj\nFU9ID/U3cZAbQNIJkEKk/dJIM8MBSSdACpEAydoAKUQCJGsDpBAJkKwNkEIkQLI2QAqRAMna\nAClEAiRrA6QQCZCsDZBCJECyNkAKkQDJ2gApRAIkawOkEAmQrA2QQiRAsjZACpEAydoAKUQC\nJGsDpBAJkKwNkEKjtJFSIzOnBQKkYjvb+l6p5ifGxwGSy/qjalmpSmkTTxogFVdmHVJVKkuM\nSwIkl/V/6jEbyhR3nKDiA6TimkdUSHcYPjUtILmri2EqJDKaewZAKq4+hNxRozQhJ4wOBCR3\ndYxokPpzzwBIxTVSO1l6iRSjAwHJXWXfpkHiP0MIIBXX7jIqpFcMDwQklzVXhfR0NvcEgFRs\nU0vJju49aXgcILmteQ9INTqc5x8PSMW3e1T3j64aHwZIrgsfyDoxQHJdgOTEXAUpecjjT36Q\nxfdw3gmQnJibICVVIdJ95P/432d7I0ByYm6C1IookIjJBXZ9gOTE3ATpLg1SJN/jeSZAcmJu\nglSRkJIlCQnnezzPBEhOzE2QGmmbb4zlezzPBEhOzE2QjtysOPpHOt/jeSZAcmJugkSPvH5/\nrX7JfA/nnQDJibkKElICJCcGSK4LkJzYZumllqaKAaQgB0hObLNkOkAKboDkxADJdQGSE9ss\n1WttqlhACnKA5MSwssF1AZIT2yzFjDbVUEAKcoDkxPAeyXUBkhNL21tcg8qVqyHdVO7eYn/o\nGN8jAxJngOS+Uu4l1aRS5DMr5gYkzgDJhR18qbr0t48smRqQOAMkV/a2dNqaiQGJM0ByYVfe\nkaT6v1oyNSBxBkjuK+c55T1SmS1WzA1InAGS+/qEqCsbJCvmBiTOAMl9ddIghVmxUyogcQZI\n7qu7BqlkpgVzAxJngOS+VmiQ/mvF3IDEGSC5sNcVSBUOWTE1IHEGSG5s4X+kt5MsmRmQOAMk\nN5bRTtptzcyAxNl66S0zhz8HJDtaW72aVO5tw6d4ZgmQuMpoW1aq8vAv/BMAkg2duFVd2WDJ\nsU0Biate6qkvq1zkngCQbGiEttbuNivmBiSe0stqJ2Oezj0DINlQO0JuuaskIZctmBuQePqD\nkGvvupmQvtwzAJINvaMdtr68z4K5AYmnK6W1p2QK9wyAZEOHb1SftcFWzA1IXHVSn5E7znJP\nAEh2tOJO+VlrY8lpWwGJqytR8jNyz0/8EwCSLV1e/UWiNTMDEmd7F/1oZiNiQPJYgGRPgOSx\nAMmeAMljAZI9AZLHAiR7AiR3dnz6O5Nytww7+cHgSftzvwFI9gRIruxI1PjVsyI2qLfPRY9c\nPa3hAf93AMmeAMmVzRsoX4zSdqCZ/ZaP0neH+78DSPYESO7t3dHq1dvz5Iv4pv47AcmeAMm1\n7W+8S71uvVK+2BmeJl9O7NOn052AZEeA5NZ+if5eu9FilXyxJzxFvoySJKkGINkRILm0Zc23\n+W91/Eq+2Fpf2U/6zPHja68FJDsCJHe26I3juTfHTpIvFuSelwzvkewJkFzZzmbaySyurEum\nWxofoKdjl/q/A0j2BEiubGDjtnJv0WPhuymdU79N5KTcI6kAkj0BkitL3Km0h17dqaysu7g3\n/xh5gGRPgOSxAMmeAMljAZI9AZLHAiR7AiSPBUj2BEgeC5DsCZA8FiDZEyB5LECyJ0DyWIBk\nT4DksQDJngDJYwGSPQGSxwIkezINqdcYFKx+ZXhG9hLJ7sUMofLPp2QS0pq/E8PdVLWs8UH6\nla9a2oppb616rRXTVqha0vigWQzPyMU3OZamSkWOQbqVqHq7FdNeU7WCFdOWqnqL8UHVREGi\n8QsN10MaZXyQfp2kCVZM+7o01Yppm0uzjA86xPKMnOZYmsdf5Rik2ydSYyum/VCKsWLaKVIb\n44O+EQaJo1nSWiumnSyZOJVu4EZIlrwL7COdtmJazmrHWjFrqtTFimlPSv2smHa/NNLMcEDS\nCZC4AyRrAyQKSCYCJIQ8GyAhJCBAQkhAgISQgACp6I7sVvs11e4FQf7StGfkN4dukwZIRdcv\nXK3hbrsXBPnbqT0jEUPsXpCiA6SiS798+XLy4Tlz7F4OlFu2/IxcTto86rDdC1J0gFRsbRPt\nXgJUuDUj7F6CogOkYlufpP8zKJid2mz3EhSd6yFN752XyGkP7tBKpTtEThsKJeY/I9P1f5q5\nVP8zcpCecuL6BtdDWjknL5HT+lc2hO+k4SKnDYVO5T8jKwVO61/ZEN6PzrdkEyGTuR6SXOYl\n8XPmZGv5aLb4yb1fcobwKX3+ZySH+nL0fzrouR/Shk4R4Y3jxFq6fIWe+mLKe7O36f8o+kuX\n4qLCIzptFDvpWZqzftakqUsviJ1WWK6HtK3BjE3dPx/RS+ScJ5v/vL9RTN+BncOniZw2v9Wn\nrJnXGb3ddeXibsujhf4WmtGTjoroNqj3a82O6/8wR+fMvgp1PaR3F8lvaHZmtz4hcM7pE7In\nTVFePxxsLPgXYNJH702Sax23T+y8Tup4TBrd2Y9+O17kpI0TT0cdla+zJkwROa3c6veVZ2RY\n6+/TTE3jekiDdyqQaN8DAuccso0O1VbW9RI5rdyYtmPHybXs/5nYeZ3U3r5UgfTrUJGT9qQH\n+6g3tgudltKjEUOVZ2RwzLijpuZxPaQpnymQzjW9LHDOScvoSnUd4PkOgt80j9LeOQzdKXZa\nR3U+Wv2LNHOmyEljM7O7q68OZn8lclpKj2hnsT5odk2g6yElRm2i/fpHfyhyzq1Rc354a9ii\nVXNbz08ROS+lp5Np+gUfvZwldlpnNe5NujO2V9OzIufs2Wf5/DazVn49rOsekdNSmp1IfRfS\nabbZJ9r1kGjiaTpr0nqf0Dl/7B0brdZsl9B5KY1vFx7eYOw5wbM6q8ytNHH43PNC57wwuUNz\n7SkR/NKOnp/QIDy8zWqz07gfktYqd6wH2xw1b+vONX2EboTh0JJW2L0ETPm6912zc+u8qO0m\n53E9pHWL1TpNWyGUkn9/JCWR045ZrlxmxHp5E74k7RmZ0XbJRpGfnKblPyNHBE57tFWmcrUs\nzuQ8roc0sYNaVOuYJiKnzd1ESE7ktEO1Ix31cuLWYqLarz0jbRq1i/xY4LQ7858RkZsIHdA+\ngTS9MtD1kPwN3elrJ3I+ZX+ky8enL0u7LHJ1IP1osnJ5ponQSZ3ZwX509RCB86n7I11MGHrq\n8uV0gdOmNVE/4Z1udm2VVyDF7aMWbM/jay94M77zr3WcNG1E1AdiZ3VkR8bRJMFr2JR+mCF4\nwjkNh02b1CXK7BsD10OydluBbvsFT3h6Vv+3R6x04laX4hK0rUDRbRoieELfmlFv95tu7tNY\nGmRIl84I/liGWratwK/r5OKnRVqwYbmTyjybJHzrdlHbCvypS8ozsu7btqL/IgkqWJA2/ki/\nbiG/T2zzo+CJLdpWYEiUXNMu8WJnpbMGqvV3wB7saVN8SQPrh4c3GC34t5uobQX+1C7lGYlq\nOUbwr7ZD2jMy0KzPIEE63mDN7shPtu3dOrOB4H9Dp5PVK4u2FcgW+0EvXT5DbkL0VAes/p7Q\nLWNgz/hdv63qMlrsxNnaM2x6W4Gi8wl+ok8qz8i0Ph3M/oIPEqSvJ9HF2i4Jk5cG5xHNlnZG\naYAlO5pf6u+AN0nRZ2iUuvVaUjO7F4Ut3znlGUnoa8nkC+JNThAkSEtm0bXaCsYPVwXnEU22\nPEL7yKLBVCtmf9cBL+2irtAO6lr41DZ2LwpTae38nyK1PmzB7MeHm5wgSJC2tz6VOXxDFs1Z\n31fkhwDW9c7HR4/J9V8tdNNLf6mdRH42z1mveTkJ4+SXmOdGxdu9KEztjz2oPCMb3jp21YLZ\nvx1lcoJgrWx4N6LVG+H1W0eFdxB5QAzrWqJtrfqN4P0x1e0w2jfu5oADQeyNjmrbNPy1luFN\nu9u9KExd1PboO7tE7LTadhitIn4yOU/QVn8fWL1cbdlegZPmH4ur1+8Cp7UudcvApRvEHxqE\no5TN32hPidA9fPKPxWXRbvqC07YMXGn6JYK7P5BVjsX1Ro8ps8a9NtEB68GY8p0/444Xt5zJ\nz8iEpmNnftCzdbzdi8Ja2pkL5lfOBhHSqqFv9ZAT/NJulfoBQPLbLthVzrfgcPb7UeHhET0d\nsK5B6eyUPsoz0kPsrNnd1PeVi9ywG8WRz+jOTuHh4a/NMUspeJB2Npzy6Xw5wZ+cDtNWUI9x\nwfbU38UcWhD79a971g+PdQb7vj3mKs/IfLGzHtT2tjoqeg88C8ppMyW96bhNe3d8Gb3c5FTB\ng/Sl4HeJ/ibMVS7T2ojcGyn3eMU7dmwX+aniqB/o0HXKDV87Z/xJamnJOo9T0erHU8smiJw0\n7xnZIXLrxyNtfAc7qrdMH5s/eJBWWfNJ7G+Rb773wdjm/UVugpC/M1KEyF3NhyXQKdoh4Ls7\n45TmXQRvt+FvSNNR0yb3ihT6WXb+zkgDBc568E167k311uZJJqcKHqQrww5ZMu/h6UN6Dl10\nReSUWRkZxwduuXDl+Ly5Iqf9eEjaqYEHfTR9nkNWaK362JLVhxnfjundd7LYo11kZGTM+/Bo\n2qWfh4o8gOGVqK107vw0WVSvP0xOFTxIK98Ib9ZKTvgrvGQrfq9O0T5XGCRyZWBKpwZtYsMb\ntY6M7OqMlfXdG0e2VJ4S0fNmWLH/xLnO6tUes/uEF2p5ZPO2DcJjo8Nbmj3uZPAgHVj5nZrg\nf0NH24a/Jv9aOSZ2Vjo0Qb16R+gBIrN3rPR/mOaMlfUrtGfkO8HTzo6IGJ6xR/QvzAPa58b7\nxa7DOLVOe0a+Xm9yoqB/jrRL8Gagk0btmt/K925bwX+W5nVMOHPp6MIooS8ZHdqnYqc73Xjt\n9o7f7mlu+ghXhUtvPPvwpaTtb30idlpRBQ9SxsLJyo6TPfpuEDrtO/Jr8e7naDuhO5HJSztW\n2Wq1qdnfU3+Z9tOB6ic3zlhZv2ea8oxMilwidN3H/gHye/f3afxYkZPKbWmtrGsYJHpLuz1j\n3laeEfe8tPum6Whlx8nunccJnXbBMkqHH6K9RO8TTi/t23YgU/Skn7aaqX5yc0b0xFzF9FSe\nkXGR44T+vkjvkEF/H0m3vyNyUqXsP3b8Kvw1cVrTEfOUZ+QHk/MED9LX2j6IiwV//Jc5fmXK\nsN07opLFTutbPX1pJhX+tL3jjD9F/qJT1asowdNuG/3H3mHnx4neJ/zU3Fn7aaro7av2DxMz\nT/AgpWgfmV4V/F9i1Wvq5z2CedIljQbEjqUdZguedqIzPon1d0jbwELwzttXetVXnpI2gndA\nuRrbsVeDI+tiBR9S95Sgs6S7e6NVuaGztu/YsVP4XkNDf6IXotP3NRG8v/ShcRfFTujADrZJ\n2LFjxwHRH1Ed7Oyjcz5KnyD0JBdyU8QcN971kHZZ8y9zwi+UDj5Gews+P9KUmIgY5ZMbkfuS\nOK2rWy2Z9mQvSveOozsEb8L3e5eIRsozMsbkPK6HlL9LktAj0+8bfJWOSPS1FbwycLP/kxun\nnglVRIn5z8h0kfOO2EITR9DVglcGnvN/vGlWv+shrcw/Gb3IaS9Naj+93YS+HcQfpcSKc7A7\nqlP5z4jQXWa2tBozsd2UZmtEzqkl4hzsrodkUWNjevXu3Xey8I1LrTgHe2h0NKK7/Ddu6ArR\nG4QJOge76yFZdMjFada8i7HiHOxOS9QhF//UKbOHJwmQoHOwux7Sillyk1u9L/gjn/xdkoRu\n0mTFOdid1inlGZkxqH282GlT858RkR/GiToHu+shaaX3F7wRgkXnR7LiHOwObYXgXc0tOj+S\nqHOwewQSHSd4E6GsjLxETmvFOdgd2jHBmwj58p8RkTvqizoHu+shJe6U2/FtE8EHoPOf+nJX\nqthprTgHu9O6rDwjOxNGvit22txTX4reNkTQOdhdD0l7DRbxkSXThtcXfVZzK87B7rD8r8Ha\nCj6arH/aiEFipxV1DnbXQ1Jfg11YLvSjP7mMdLmzX4k96VJopL4Gu3JglGBIOcozkrp3vENP\nX+96SP5GWvPfd4Qz9ndwY4cmWjJt4nuWTGs6j0DKsWZXOV9PS06oGRJtFrR/wp/aOsSSaU3n\nekjqtnY9Y9oI3th4Rj+53i2be/rwwtakbWvXJULw6u9E5Rnp92bkIrHTisr1kH5Qdm9csErw\n2jVtE74pbRIETxsKnVH3AV4iejWNugnfrKG9HHAij6JyPSRLu9rHEeeNQPl9Z80Be00HSMUm\n+nNeZLZjQ+xegqIDpKJTt7Xb9nUU1to5JW1bu41DJ9u9IEUHSEWnfiBbv7VDX0eEYtoHsk36\nO+Ow6X8JkBASECAhJCBAQkhAgISQgAAJIQEBEkICAiQnRwLtxrGXBNpt7ltlzJJ4ixYIBQqQ\nnBwHpKyLmZQ+OsSiBUKBAiQnRxacmTFqkXpcl5MfjZyyU73zx7Fx+2VIO4eoX54dskq9N3vF\ne+MWKWdDHfLbscEl6g7ZT2nS3FHTQuAoK84IkJwcGXRrjSqkzhVK55S55Zm7SXv5vk7k7n+X\nH0rePXNNjPIjU0i8cnX+gWtrP1m24m7lpd2+50mNV7bQpTdU+M89JQbbufwhFCA5OXLzGkqH\nkUn04LXPpVLaW36pt4k0z6EnH5Rf2tUvp5wq45mq6gEgxhD579OZaq+r75GOkSGUHr/uhVTq\n600En4ESFR0gOTnyunyRUe55OpRslm+llw2nfYmyo89MGdJSMkt+xVdioPqTbxLlRVwOzYc0\nliiHtrxapqVdCx9aAZKTI9OUy0fupo2JeurUh6vThkQ5qtsWGVJWxacpjSPau6AdN9/caZmy\nd2MepGbk7T5yFR6za+FDK0BycmSecvnkHfTFa9Svn7iD/vda5cYuZa1dr7Df6b+f8v/owQ6V\nSOk2F/MhvRL2olprKNWi3AAAAYxJREFUW5Y85AIkJ0filMtHqtMoop7A4sEatD5RVuJtUCDt\nJcOOhxU4Dtmuvtc0yocUTQSfVhcVFyA5OfKGfJF53Ut0pLrO4HLpxrSnslaBvqd+jvTkAxNK\n+09YeFE9LH/M9fmQxpPvlLsEH10OBQiQnBwp/4uy1mA6PVz66VSa04msoGtIVDY9cK8K6UNS\nqYl89b8JlP7z4SRKUx/5uwrpnLKe/OT1j52ldGGY6GPQoiIDJCdHht1e6wHyH/nF3Lwy5f91\nZwnlcL0tSIWHyn1AlDNAplxHvpGvHryV0q23lpb+eUP5VdomQveFPfQ/uvSG6+r8nUSLPOI8\nChggObkhh0/PGLVYPQDVyVkjpmkHwVw1Ou5AxpAflZv1KipMpiqo0r4cP37RZXXLBkpPTFR+\n9tzHI6cKPbsTChwgubikcgPtXgTkD5DcW3rEjWZPRoJEBUiu7cXbr1lo9zKg3ADJtU0fJ/qo\nwIg/QEJIQICEkIAACSEBARJCAgIkhAQESAgJCJAQEhAgISSg/weORIy2Kr1ajQAAAABJRU5E\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_richness(ps.rarefied, x=\"body.site\", measure=c(\"Observed\", \"Shannon\")) + \n",
    "  geom_boxplot() +\n",
    "  theme_classic() +\n",
    "  theme(strip.background = element_blank(), axis.text.x.bottom = element_text(angle = -90))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689f4d2b",
   "metadata": {},
   "source": [
    "## 6.2 Wilcox tests\n",
    "\n",
    "We could see tongue samples had the lowest alpha diversity. Next, some \n",
    "statistics: pairwise test with Wilcoxon rank-sum test, corrected by FDR method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2140d1c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(xi, xj, paired = paired, ...):\n",
      "“cannot compute exact p-value with ties”\n",
      "Warning message in wilcox.test.default(xi, xj, paired = paired, ...):\n",
      "“cannot compute exact p-value with ties”\n",
      "Warning message in wilcox.test.default(xi, xj, paired = paired, ...):\n",
      "“cannot compute exact p-value with ties”\n",
      "Warning message in wilcox.test.default(xi, xj, paired = paired, ...):\n",
      "“cannot compute exact p-value with ties”\n",
      "Warning message in wilcox.test.default(xi, xj, paired = paired, ...):\n",
      "“cannot compute exact p-value with ties”\n",
      "Warning message in wilcox.test.default(xi, xj, paired = paired, ...):\n",
      "“cannot compute exact p-value with ties”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>group1</th><th scope=col>group2</th><th scope=col>p.adj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>left palm </td><td>gut       </td><td>0.309861520</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>right palm</td><td>gut       </td><td>1.000000000</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>tongue    </td><td>gut       </td><td>0.002644163</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>right palm</td><td>left palm </td><td>1.000000000</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>tongue    </td><td>left palm </td><td>0.002644163</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>tongue    </td><td>right palm</td><td>0.015796057</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & group1 & group2 & p.adj\\\\\n",
       "  & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & left palm  & gut        & 0.309861520\\\\\n",
       "\t2 & right palm & gut        & 1.000000000\\\\\n",
       "\t3 & tongue     & gut        & 0.002644163\\\\\n",
       "\t5 & right palm & left palm  & 1.000000000\\\\\n",
       "\t6 & tongue     & left palm  & 0.002644163\\\\\n",
       "\t9 & tongue     & right palm & 0.015796057\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | group1 &lt;chr&gt; | group2 &lt;chr&gt; | p.adj &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | left palm  | gut        | 0.309861520 |\n",
       "| 2 | right palm | gut        | 1.000000000 |\n",
       "| 3 | tongue     | gut        | 0.002644163 |\n",
       "| 5 | right palm | left palm  | 1.000000000 |\n",
       "| 6 | tongue     | left palm  | 0.002644163 |\n",
       "| 9 | tongue     | right palm | 0.015796057 |\n",
       "\n"
      ],
      "text/plain": [
       "  group1     group2     p.adj      \n",
       "1 left palm  gut        0.309861520\n",
       "2 right palm gut        1.000000000\n",
       "3 tongue     gut        0.002644163\n",
       "5 right palm left palm  1.000000000\n",
       "6 tongue     left palm  0.002644163\n",
       "9 tongue     right palm 0.015796057"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rich = estimate_richness(ps.rarefied, measures = c(\"Observed\", \"Shannon\"))\n",
    "wilcox.observed <- pairwise.wilcox.test(rich$Observed, \n",
    "                                        sample_data(ps.rarefied)$body.site, \n",
    "                                        p.adjust.method = \"BH\")\n",
    "tab.observed <- wilcox.observed$p.value %>%\n",
    "  as.data.frame() %>%\n",
    "  tibble::rownames_to_column(var = \"group1\") %>%\n",
    "  gather(key=\"group2\", value=\"p.adj\", -group1) %>%\n",
    "  na.omit()\n",
    "tab.observed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d3e4f8",
   "metadata": {},
   "source": [
    "The species richness of tongue is significantly different from all the other \n",
    "body sites (gut, left palm, and right palm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8258ccef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>group1</th><th scope=col>group2</th><th scope=col>p.adj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>left palm </td><td>gut       </td><td>0.074825175</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>right palm</td><td>gut       </td><td>0.170229770</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>tongue    </td><td>gut       </td><td>0.071904566</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>right palm</td><td>left palm </td><td>0.851814852</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>tongue    </td><td>left palm </td><td>0.000987248</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>tongue    </td><td>right palm</td><td>0.008391608</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & group1 & group2 & p.adj\\\\\n",
       "  & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & left palm  & gut        & 0.074825175\\\\\n",
       "\t2 & right palm & gut        & 0.170229770\\\\\n",
       "\t3 & tongue     & gut        & 0.071904566\\\\\n",
       "\t5 & right palm & left palm  & 0.851814852\\\\\n",
       "\t6 & tongue     & left palm  & 0.000987248\\\\\n",
       "\t9 & tongue     & right palm & 0.008391608\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | group1 &lt;chr&gt; | group2 &lt;chr&gt; | p.adj &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | left palm  | gut        | 0.074825175 |\n",
       "| 2 | right palm | gut        | 0.170229770 |\n",
       "| 3 | tongue     | gut        | 0.071904566 |\n",
       "| 5 | right palm | left palm  | 0.851814852 |\n",
       "| 6 | tongue     | left palm  | 0.000987248 |\n",
       "| 9 | tongue     | right palm | 0.008391608 |\n",
       "\n"
      ],
      "text/plain": [
       "  group1     group2     p.adj      \n",
       "1 left palm  gut        0.074825175\n",
       "2 right palm gut        0.170229770\n",
       "3 tongue     gut        0.071904566\n",
       "5 right palm left palm  0.851814852\n",
       "6 tongue     left palm  0.000987248\n",
       "9 tongue     right palm 0.008391608"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.shannon <- pairwise.wilcox.test(rich$Shannon, \n",
    "                                       sample_data(ps.rarefied)$body.site, \n",
    "                                       p.adjust.method = \"BH\")\n",
    "tab.shannon <- wilcox.shannon$p.value %>%\n",
    "  as.data.frame() %>%\n",
    "  tibble::rownames_to_column(var = \"group1\") %>%\n",
    "  gather(key=\"group2\", value=\"p.adj\", -group1) %>%\n",
    "  na.omit()\n",
    "tab.shannon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb170120",
   "metadata": {},
   "source": [
    "When looking at Shannon diversity index, which takes into account of not only\n",
    "species richness but also species evenness, tongue is significantly different \n",
    "from left palm and right palm. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cbf9180",
   "metadata": {},
   "source": [
    "# 7. Beta Diversity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45071e77",
   "metadata": {},
   "source": [
    "## 7.1 Ordination plots\n",
    "\n",
    "Beta diversity metrics assess the dissimilarity between ecosystem, telling you \n",
    "to what extend one community is different from another. Here are some demo code \n",
    "using Bray Curtis and binary Jaccard distance metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e5a10cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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wAAIAmCHQAAgCQIdgAAAJIg2AEAAEiC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\ngmIHAAAgCYodAACAJCh2AAAAkvh/2xKle/gBRHQAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = phyloseq::distance(ps.rarefied, method=\"bray\")\n",
    "ordination = ordinate(ps.rarefied, method=\"PCoA\", distance=dist)\n",
    "plot_ordination(ps.rarefied, ordination, color=\"body.site\") + \n",
    "  geom_point(size = 1.5) +\n",
    "  theme_classic() +\n",
    "  theme(strip.background = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3ecee7",
   "metadata": {},
   "source": [
    "We can see that mostly samples cluster by body sites. Samples from the gut and \n",
    "tongue area are separated from samples from the left and right palm area."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b108633b",
   "metadata": {},
   "source": [
    "## 7.2 Permanova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee4cdc74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'adonis' will be deprecated: use 'adonis2' instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 3 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>SumsOfSqs</th><th scope=col>MeanSqs</th><th scope=col>F.Model</th><th scope=col>R2</th><th scope=col>Pr(&gt;F)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>body.site</th><td> 3</td><td> 5.213908</td><td>1.7379693</td><td>7.975992</td><td>0.4698395</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>Residuals</th><td>27</td><td> 5.883302</td><td>0.2179001</td><td>      NA</td><td>0.5301605</td><td>   NA</td></tr>\n",
       "\t<tr><th scope=row>Total</th><td>30</td><td>11.097210</td><td>       NA</td><td>      NA</td><td>1.0000000</td><td>   NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & Df & SumsOfSqs & MeanSqs & F.Model & R2 & Pr(>F)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tbody.site &  3 &  5.213908 & 1.7379693 & 7.975992 & 0.4698395 & 0.001\\\\\n",
       "\tResiduals & 27 &  5.883302 & 0.2179001 &       NA & 0.5301605 &    NA\\\\\n",
       "\tTotal & 30 & 11.097210 &        NA &       NA & 1.0000000 &    NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 6\n",
       "\n",
       "| <!--/--> | Df &lt;dbl&gt; | SumsOfSqs &lt;dbl&gt; | MeanSqs &lt;dbl&gt; | F.Model &lt;dbl&gt; | R2 &lt;dbl&gt; | Pr(&gt;F) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| body.site |  3 |  5.213908 | 1.7379693 | 7.975992 | 0.4698395 | 0.001 |\n",
       "| Residuals | 27 |  5.883302 | 0.2179001 |       NA | 0.5301605 |    NA |\n",
       "| Total | 30 | 11.097210 |        NA |       NA | 1.0000000 |    NA |\n",
       "\n"
      ],
      "text/plain": [
       "          Df SumsOfSqs MeanSqs   F.Model  R2        Pr(>F)\n",
       "body.site  3  5.213908 1.7379693 7.975992 0.4698395 0.001 \n",
       "Residuals 27  5.883302 0.2179001       NA 0.5301605    NA \n",
       "Total     30 11.097210        NA       NA 1.0000000    NA "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata <- data.frame(sample_data(ps.rarefied))\n",
    "test.adonis <- adonis(unname(dist) ~ body.site, data = metadata)\n",
    "test.adonis <- as.data.frame(test.adonis$aov.tab)\n",
    "test.adonis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6dc2ca",
   "metadata": {},
   "source": [
    "If you encounter an error using adonis, you can use adonis2, which gives similar \n",
    "results. The use of function slightly varies from adonis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b6a40e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# metadata <- data.frame(sample_data(ps.rarefied))\n",
    "# test.adonis <- adonis2(dist ~ body.site, data = metadata)\n",
    "# test.adonis <- as.data.frame(test.adonis)\n",
    "# test.adonis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84f9115",
   "metadata": {},
   "source": [
    "## 7.2 Pairwise PERMANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "75f2058d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'adonis' will be deprecated: use 'adonis2' instead\n",
      "\n",
      "'adonis' will be deprecated: use 'adonis2' instead\n",
      "\n",
      "'adonis' will be deprecated: use 'adonis2' instead\n",
      "\n",
      "'adonis' will be deprecated: use 'adonis2' instead\n",
      "\n",
      "'adonis' will be deprecated: use 'adonis2' instead\n",
      "\n",
      "'adonis' will be deprecated: use 'adonis2' instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>1</th><th scope=col>2</th><th scope=col>p</th><th scope=col>p.adj</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>gut       </td><td>left palm </td><td>0.001</td><td>0.0020</td></tr>\n",
       "\t<tr><td>gut       </td><td>right palm</td><td>0.003</td><td>0.0036</td></tr>\n",
       "\t<tr><td>gut       </td><td>tongue    </td><td>0.001</td><td>0.0020</td></tr>\n",
       "\t<tr><td>left palm </td><td>right palm</td><td>0.942</td><td>0.9420</td></tr>\n",
       "\t<tr><td>left palm </td><td>tongue    </td><td>0.001</td><td>0.0020</td></tr>\n",
       "\t<tr><td>right palm</td><td>tongue    </td><td>0.002</td><td>0.0030</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " 1 & 2 & p & p.adj\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t gut        & left palm  & 0.001 & 0.0020\\\\\n",
       "\t gut        & right palm & 0.003 & 0.0036\\\\\n",
       "\t gut        & tongue     & 0.001 & 0.0020\\\\\n",
       "\t left palm  & right palm & 0.942 & 0.9420\\\\\n",
       "\t left palm  & tongue     & 0.001 & 0.0020\\\\\n",
       "\t right palm & tongue     & 0.002 & 0.0030\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| 1 &lt;chr&gt; | 2 &lt;chr&gt; | p &lt;dbl&gt; | p.adj &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| gut        | left palm  | 0.001 | 0.0020 |\n",
       "| gut        | right palm | 0.003 | 0.0036 |\n",
       "| gut        | tongue     | 0.001 | 0.0020 |\n",
       "| left palm  | right palm | 0.942 | 0.9420 |\n",
       "| left palm  | tongue     | 0.001 | 0.0020 |\n",
       "| right palm | tongue     | 0.002 | 0.0030 |\n",
       "\n"
      ],
      "text/plain": [
       "  1          2          p     p.adj \n",
       "1 gut        left palm  0.001 0.0020\n",
       "2 gut        right palm 0.003 0.0036\n",
       "3 gut        tongue     0.001 0.0020\n",
       "4 left palm  right palm 0.942 0.9420\n",
       "5 left palm  tongue     0.001 0.0020\n",
       "6 right palm tongue     0.002 0.0030"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbn <- combn(x=unique(metadata$body.site), m = 2)\n",
    "p <- c()\n",
    "\n",
    "for(i in 1:ncol(cbn)){\n",
    "ps.subs <- subset_samples(ps.rarefied, body.site %in% cbn[,i])\n",
    "metadata_sub <- data.frame(sample_data(ps.subs))\n",
    "permanova_pairwise <- adonis(unname(phyloseq::distance(ps.subs, method = \"bray\")) ~ body.site, \n",
    "                             data = metadata_sub)\n",
    "p <- c(p, permanova_pairwise$aov.tab$`Pr(>F)`[1])\n",
    "}\n",
    "\n",
    "p.adj <- p.adjust(p, method = \"BH\")\n",
    "p.table <- cbind.data.frame(t(cbn), p=p, p.adj=p.adj)\n",
    "p.table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75af0ba4",
   "metadata": {},
   "source": [
    "As we could see, there’s no difference in composition between the left and right \n",
    "palm samples. Can you run the same analysis with a binary Jaccard metrics and \n",
    "a different ordination technique NMDS, to see if the results are robust across \n",
    "different difference metrics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f7723ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "anosim(x = dist, grouping = metadata$body.site) \n",
       "Dissimilarity: jaccard \n",
       "\n",
       "ANOSIM statistic R: 0.7593 \n",
       "      Significance: 0.001 \n",
       "\n",
       "Permutation: free\n",
       "Number of permutations: 999\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>1</th><th scope=col>2</th><th scope=col>p</th><th scope=col>p.adj</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>gut       </td><td>left palm </td><td>0.001</td><td>0.0012</td></tr>\n",
       "\t<tr><td>gut       </td><td>right palm</td><td>0.001</td><td>0.0012</td></tr>\n",
       "\t<tr><td>gut       </td><td>tongue    </td><td>0.001</td><td>0.0012</td></tr>\n",
       "\t<tr><td>left palm </td><td>right palm</td><td>0.764</td><td>0.7640</td></tr>\n",
       "\t<tr><td>left palm </td><td>tongue    </td><td>0.001</td><td>0.0012</td></tr>\n",
       "\t<tr><td>right palm</td><td>tongue    </td><td>0.001</td><td>0.0012</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " 1 & 2 & p & p.adj\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t gut        & left palm  & 0.001 & 0.0012\\\\\n",
       "\t gut        & right palm & 0.001 & 0.0012\\\\\n",
       "\t gut        & tongue     & 0.001 & 0.0012\\\\\n",
       "\t left palm  & right palm & 0.764 & 0.7640\\\\\n",
       "\t left palm  & tongue     & 0.001 & 0.0012\\\\\n",
       "\t right palm & tongue     & 0.001 & 0.0012\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| 1 &lt;chr&gt; | 2 &lt;chr&gt; | p &lt;dbl&gt; | p.adj &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| gut        | left palm  | 0.001 | 0.0012 |\n",
       "| gut        | right palm | 0.001 | 0.0012 |\n",
       "| gut        | tongue     | 0.001 | 0.0012 |\n",
       "| left palm  | right palm | 0.764 | 0.7640 |\n",
       "| left palm  | tongue     | 0.001 | 0.0012 |\n",
       "| right palm | tongue     | 0.001 | 0.0012 |\n",
       "\n"
      ],
      "text/plain": [
       "  1          2          p     p.adj \n",
       "1 gut        left palm  0.001 0.0012\n",
       "2 gut        right palm 0.001 0.0012\n",
       "3 gut        tongue     0.001 0.0012\n",
       "4 left palm  right palm 0.764 0.7640\n",
       "5 left palm  tongue     0.001 0.0012\n",
       "6 right palm tongue     0.001 0.0012"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tCPhrR0RbWqqj4Dld71rF0RAACQEMHO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\nHb/10lGSO6YAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = phyloseq::distance(ps.rarefied, method = \"???\") # TRY JACCARD DISTANCES (BINARY)\n",
    "# dist = phyloseq::distance(ps.rarefied, method = \"jaccard\", binary = TRUE)\n",
    "ordination = ordinate(ps.rarefied, method=\"PCoA\", distance=dist)\n",
    "plot_ordination(ps.rarefied, ordination, color=\"body.site\") + \n",
    "  geom_point(size = 1.5) +\n",
    "  theme_classic() +\n",
    "  theme(strip.background = element_blank())\n",
    "\n",
    "# ANOSIM\n",
    "metadata <- data.frame(sample_data(ps.rarefied))\n",
    "anosim(dist, metadata$body.site)\n",
    "\n",
    "# Pairwise ANOSIM\n",
    "cbn <- combn(x=unique(metadata$body.site), m = 2)\n",
    "p <- c()\n",
    "\n",
    "for(i in 1:ncol(cbn)){\n",
    "ps.subs <- subset_samples(ps.rarefied, body.site %in% cbn[,i])\n",
    "metadata_sub <- data.frame(sample_data(ps.subs))\n",
    "permanova_pairwise <- anosim(phyloseq::distance(ps.subs, method=\"jaccard\", binary = TRUE), \n",
    "                             metadata_sub$body.site)\n",
    "p <- c(p, permanova_pairwise$signif[1])\n",
    "}\n",
    "\n",
    "p.adj <- p.adjust(p, method = \"BH\")\n",
    "p.table <- cbind.data.frame(t(cbn), p=p, p.adj=p.adj)\n",
    "p.table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e866dd55",
   "metadata": {},
   "source": [
    "# 8. Abundance plot\n",
    "\n",
    "Phylum level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1e3adda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'Sample', 'body.site'. You can override using the `.groups` argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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SUlygFxcXEffvihWq0u3cmaNWtiYmK0uxs2bDh79qwQYv369UuWLAkMDGzWrNn6\n9evffPPNt99+Oz09PS8vb8SIEcHBwbX0JOsaI+4AAACoqt9++00I0aVLF2X3xo0bp0+f1tXV\nzc3Nbdu27bFjxzQazZYtW37++WdlgDwwMLBHjx79+vWrSOc6OjrGxsbbt28XQqhUqpkzZx48\neNDZ2VkIcfbs2ZCQkEmTJtXQ86pXCO4AAAB4YZcvX96wYYMQori4+OrVq4GBgS4uLq+88ory\n6JtvvqmrqyuEUKlUbdu2vX37tp2dnaOjo5+fnxLc9+/f7+bmVvHLDRo0SNlo166dEGLgwIHa\n3Vu3blXbs6rfCO4AAAB4YX/++aeyhqOOjo6FhcXBgwdLz183NTXVbuvq6hYXFwshNBrNzJkz\nfX19L126dOvWrenTp1f8ctpJNTo6OkIIlUql3VU6bwwI7gAAAHhhffv23bdv3wudMnHixEWL\nFh08ePDChQvOzs5mZmZPHKCjo6OdBC+EePjwYTUU2oAQ3AEAAFAb9PX133///T179iQmJgYF\nBT19QKtWrdLT05XtiIiInJyc2i2wviO4AwAAoJbMnTt33bp1tra2gwcPfvrRKVOmrF271sDA\nIDc399SpUzY2NrVeYL1GcAcAAMCLmTlzpnaW+dOWL19e+mtTFy5cqF2a3cbGpn379vPmzSt9\n/IoVK5SvTf3iiy+GDBmSkJDQrl27gwcP7t27Vzlx+fLlvXr1Ug7u2LGjp6enkZGRsjt+/Pis\nrKxqfXL1F8EdqEcmX4qvch921VAHAADlmjlzZjmPLl++vPTuwoULtduBgYFZWVmzZ88ufcCK\nFSuUDV1d3QkTJmjbtfm+dIcdO3ZcvXq1dnf8+PEvWLvECO4AAACocSdPnvznP//57bff+vn5\nNWvWrK7LkRLBHQAAADWuWbNmnTt3/umnnwYMGFDXtciK4A4AAIAa179///79+9d1FXLTresC\nAAAAADwfwR0AAACQAMEdAAAAkADBHQAAAJAAwR0AAACQAKvKANUj2GRrXZcAAAAaMoI7UE1y\np9R1BQAA1JKCjz6oYg+G676slkoaFabKAAAAABIguAMAAAASILgDAAAAEiC4AwAAABIguAMA\nAAASILgDAABAMteuXYuLiyvdEhsbm56eXrolIiKiqKioduuqWSwHCQCofoExLSt55qhqrQNA\nQ+Tn5+fm5jZ69OgDBw4oLcuXLxdCXLx4cfXq1X379hVCnD59euPGjSEhIXVZaI3sjXIAACAA\nSURBVHVjxB0AAAD1VGJi4vz58wsLC7Utx44d+/77793d3UsfdvjwYW9v7yVLluzevVsIUVRU\n9OGHH375ZUNbKp7gDgAAgHrn9OnTf/vb3yZOnNinTx99fX1t+2uvvfbPf/7T1NRU25Kbm2tg\nYCCEaN269c2bN4UQmzZtmjhxYrt27fLz82u/8ppDcAcAAEA9EhQU1Ldv31WrVs2ZM+fChQvv\nv/++jo6O9tGmTZs+cbyxsXFBQYEQIjs7u1mzZjdu3AgODn7llVcGDhw4ZMiQ//3f/63V6msS\nc9wBAABQj/zjH/9YsGCBMm29InR0dDp27Hj8+PGjR4+OGDFi0aJFPj4+GzZsCAgIaNas2Tvv\nvDN27NgaLbjWMOIOAACAemTnzp0nT57s1avXt99+++jRo4qc4u/vf/r06ZdfftnMzMzU1PT1\n119PTk62sbFp166dMnmmYWDEHQAAAPXIqFGjRo0aFRkZuWnTplWrVi1YsGDx4sWlZ8s8rW3b\ntmvXrs3Ly3v11VePHz8uhDA0NCwqKjI0NNTTazhxt+E8EwAAUBEs1gkpvPLKK3v37r1586av\nr29RUZFy+6kQIiUl5datW9euXbt3797Zs2fNzc1feukl5SEvL6+5c+e2bt1aCPHXv/71hx9+\nUKvV/fr1q6unUO0I7gAAAKinLC0tvb29S7f8+9//DgoKUraXL18+YMCAtWvXCiEePHhw69at\nTz/9VHno008/Xbp0aXFxsY+PTy3XXHMI7gAAAJDGwoULFy5c+HS7mZnZN998o91t0aJF6d2G\ngZtTAQAAAAkQ3AEAAAAJENwBAAAACRDcAQAAAAkQ3AEAAAAJsKoMAAAAXozhui/ruoTGiBF3\nAAAAQAKMuANAfZU7pa4rAOojvvm1PgjbpVPFHgbNKamWShoVRtwBAAAACTDiDgAAXkCw7t26\nLgFopBhxBwAAACRAcAcAAAAkQHAHAAAAJEBwBwAAACRAcAcAAEC9c/DgwS5duri7u5dzzJkz\nZ/z9/YUQCxcuDAoKeqH+nZ2d/fz8Kleb9rrPVYnCysGqMgAAoBGZfCm+sqfaVWcdeJ5vvvlm\n27ZtGo3ms88+09X971hzYWHhpUuXzMzMOnTokJGRERAQkJeX17t3b41G06JFCyFEbGxs69at\nS0pKkpOT7ezsmjVrppxYVFQUHx9vaGhoa2ur7U0IcefOnevXr3fr1s3Y2Fhpyc/Pj42NNTY2\n7tq1q46OTvnXdXBwePToUXx8vEql6tixo3KwUsO9e/eaNm2qLexZPb8QgjsAAADql5s3b6ak\npAwfPrxnz57ff//9G2+8IYRITEwcM2ZMp06dkpKSxo0b9+6774aFhRkYGPzyyy/ffffd2LFj\nXVxc1qxZY2hoWFRU1Lx586CgoD/++MPa2jo+Pn7cuHEODg5ZWVnZ2dknTpwwNzcXQnz//fdf\nf/21Wq2Oj4//9ddfbWxs4uLiRo4c2bt37xs3bpiYmISGhurp6ZVzXZVKNWbMGDs7u/v37xsb\nGx85csTIyMjT09PAwCAlJcXLy2vLli1KYWX2/KIvC1NlAAAAUL8EBARMnTpVCDFjxgztpBQP\nDw+NRnP8+PFz5879+OOPbdu2HTp06NixY+fPn689UVdXNy8v78CBA9u3b3/99dePHj2qnDhr\n1qxDhw6FhoZaW1v7+voqB2dkZISHh4eGhjo6Om7btk0IcezYsQULFhw+fDgiIuLKlSunT58u\n/7ru7u7vvPPOkSNHfv755yZNmuzdu1cIoaenl5mZGRYWNmTIEG1hZfb8ohhxBwCgcWGuCOo/\nf3//Tz755OzZs82bNz958uSDBw/MzMzCw8OVKe+mpqaRkZHPOnfAgAHKRps2be7duyeEOHv2\nrIeHh9Lo5OR0/PhxZfuNN95Qpqy89tprISEhQoilS5c+fvz45s2beXl5FhYWd+/eFUKUc92w\nsLDx48dHRUUJIbp163b69Ok5c+YIIRwdHZ+oqsyeXxTBHQAAAPXIr7/+mpOTs3v3bmW3devW\n//znP+fNm5ednW1oaPjc00sfU1JSIoTIyclRqVRKi7GxcXZ2trJtYmKibczLyxNCBAQErFix\nonfv3mq1+vr168rp5Vw3JyfH399f20/Pnj2f6FmrzJ5fFMEdAAAA9cju3bs9PDwWLFig7J46\ndcrd3X3evHnW1tZJSUndunUTQuzdu3fEiBEV7LBDhw7Jycn29vZCiOTkZBsbG6X9+vXrysbN\nmzfbtWsnhFi2bFlgYKCTk5MQQrmQEKKc69rY2Li7uyvHl5SUlHPLaZk9vyiCOwCg+jEZA0Cl\nBQUFffLJJ9rd1157LSkpKSEh4f3331+zZk3Lli3j4uI8PT0nTZqkVqsjIyNjYmLK79DFxcXL\ny8va2jozM3PXrl0HDx5U2kNCQkaOHNmsWbOvv/7a29tbCGFiYpKYmGhtbe3n51dcXBwTE1NQ\nUFDOdV1dXT08PLZv366rq7t48eJFixZNnDixzBrK7Lkif0AojeAOAACAesTZ2dnS0lK726RJ\nk7///e+///67m5ubSqVav369mZlZaGioSqWaNWvWkiVLAgMDu3fvbmFhIYSwt7fXntu5c2dl\nOchFixap1WpPT09jY+M9e/YMHDhQCPHSSy9NnTr10KFDV69edXNzc3Z2FkLs27fPx8fn5MmT\nrq6uw4YN27JlS0JCQjnXXbt2rVqt9vb2LiwsfO+995TUXroGbWFl9tyjR48XemUI7gAAAKhH\nvv766ydaVq5cqWxoNBqNRqNtt7W1VW4q1Vq1apV2e9GiRdptFxcXFxeX0kd+9tlnQognBsj7\n9+8fHBys3R0+fPhzrztjxowZM2Y8qwYvL6/ye34hLAcJAAAASIDgDgAAAEiA4A4AAABIgOAO\nAAAASICbU4EGKDCmZZXOH1VNdQAAgOpDcAcAAMCLGTSnMl/8iSpiqgwAAAAgAUbcAQAA8GKc\nj+lUsYegMYzZvzBG3AEAAAAJENwBAAAACRDcAQAAAAkQ3AEAAAAJENwBAAAACbCqDACgDJX/\nGi++wAsAagYj7gAAAKhfLCwshg0bNnToUCsrq/79+//5558VP/fMmTP+/v6VvvTChQuDgoKe\naHRxcfH29q6hK1YcwR0AAAD1i5OTU2ho6I8//njt2rWCgoLAwEClPT8//9y5c3FxcSUl/7cM\nfGFhYXR0dEpKihAiIyMjICDg1KlTFy9eFEI8evQoJibm6tWr2oNjY2Pv3r0bHx+flpYmhCgq\nKrp48eLly5eLi4uVAzQazaBBg5TtP//887fffsvJySld2xN9PveKQoj09PSzZ8/euHGjii8L\nU2UAAABQT+np6dnY2BQUFAgh4uLiRo4c2bt37xs3bpiYmISGhurp6SUmJo4ZM6ZTp05JSUnj\nxo179913w8LCDAwMfvnlF5VKNWbMGDs7u/v37xsbGx85csTIyMjT09PAwCAlJcXLyysrK2vc\nuHEODg5ZWVnZ2dknTpwwNzdfsWLF2LFjXVxcfv3114kTJzo5Od2+fVsIYWtrK4RISkp6os+b\nN2+Wf8UNGzbs3LmzT58+0dHRo0eP9vHxqfyrUV0vKwAAQEVwBwWeKyMj4+zZs8XFxZGRkbGx\nsV999ZUQ4tixYwsWLPjoo48eP37coUOH06dPDx061MPDQ6PRuLm5ZWdnDx48eNmyZUOHDjUz\nM5s/f/7kyZPfeecdT09PIcSYMWP27t07Z84cPT29zMzMsLAwIcTEiRNnzZrl4eEhhHjrrbd8\nfX1XrVqlreGTTz5ZtWrVwoULCwoKOnbsOGLECCGEu7v7032Wf8Vdu3YFBgb27du3oKDgiy++\nqMrLwlQZAAAA1C8XLlxYvXr1J598smvXrp49e+bn5wshli5d+uGHH968eTM5OdnCwuLu3btC\niPDwcEdHRyGEqalpZGRkmzZttJ2EhYV16tQpKioqKiqqW7dup0+fVtqV44UQZ8+eVeK4EMLJ\nyencuXOla4iOjlbmzBgaGg4cOLD8Pst5dNiwYRqNZseOHXfu3Pnoo4+q8rIw4g4AAID6ZejQ\nofv27VO2d+7c+dZbb/32228BAQErVqzo3bu3Wq2+fv26Ms09Ozvb0NCwzE5ycnL8/f1NTEyU\n3Z49eyob2pacnByVSqVsGxsbZ2dnP3G6kZGRsq3deFaf5Tzq6+sbEhISEhKycuXKadOmbd68\nuVIviRAEdwAAANRnvXv3Xrp0qRBi2bJlgYGBTk5OQohu3bopj1pbWyclJSm7e/fu1Y6gCyFs\nbGzc3d2V40tKSnR0dJ7ouUOHDsnJyfb29kKI5ORkGxub0o+2bds2NTW1a9euQgjtJcrvs8xH\nHzx4MGHChAkTJty/f79Dhw5LliyxsrKq3EtBcAfwTJMvxVetA7vqqQMA0Mgoc9yVjXXr1r35\n5ptCCBMTk8TERGtraz8/v+Li4piYmIKCgvfff3/NmjUtW7aMi4vz9PScNGmSWq2OjIyMiYlx\ndXX18PDYvn27rq7u4sWLFy1aNHHixNJXcXFx8fLysra2zszM3LVr18GDB0s/On78+PXr17du\n3frMmTPaVWLK7LOcK44ePdrBwcHHx6dv374xMTGGhoYtWrSo9MtCcAcAAED9kp+fv3z5ch0d\nHXNz80mTJmk0GiHEvn37fHx8Tp486erqOmzYsC1btiQkJLi5ualUqvXr15uZmYWGhqpUqlmz\nZi1ZsiQwMHDt2rVqtdrb27uwsPC9995TUru9vb2lpaVylUWLFqnVak9PT2Nj4z179igT2bt3\n725hYSGEWLt27erVq5cvX+7o6Lhy5UpTU1MhhEajebrP8q944sSJLVu27Nmzx9LS8vvvvzc2\nNq70y0JwBwAAqCVV+Etm4/ob5qlTp55u7N+/f3BwsHZ3+PDhyoZGo1GSvcLW1jYkJETZnjFj\nxowZM0p3UnrdGCGEi4uLi4tL6RYvLy9lw8jIqMwvXXq6z/Kv6ODg4Ofn93Q/lcCqMgAAAIAE\nnj/inpeXFxISkpiY+PjxY1tb2wkTJqjV6lqoDAAAAIDWc0bcHzx40KtXr6+//jo1NTU9PT0g\nIMDBweHWrVu1UxwAAAAARdkj7vfu3VPueP3pp5+cnZ0//fRT7UMbN248dOjQggULKtL748eP\nJ0yYoKv7318PbG1tN2zYIISIjo7+6quvMjMzO3To4Obm1rZt26o+DwAAAKBBKzu4f/XVV/Hx\n8Vu3bjU3Nz9//nxGRkbLli2FEFlZWZGRkaNHj65g7w8fPjQwMAgKCirdmJeX5+Pj4+bm9sor\nrwQHB2/atGn9+vVVfBoAAABAw1Z2cF+6dOnKlSt79uy5a9euzp07W1tbGxgY6Orq5uXlTZs2\nbfLkyRXsPTc39+kJ8VFRUdbW1r169RJCTJgw4V//+pd2gB8AUE+w9gWAcgSNKanrEhqjsoO7\ngYHBunXrxo8fP3PmzFGjRqWmpt6/f7+4uNjS0lL7xbAVkZOTU1xcvHr16uTk5Hbt2rm4uHTq\n1CktLU27fGaTJk3atGmTlpamDe4///xzUVGREEJfX79qTw0AAABoOMpbVWbQoEFRUVFubm6D\nBg0KCAjo27fvi/ZubGw8cODAUaNGtW/f/j//+Y+Xl9eOHTsKCgpKh3J9ff28vDzt7po1a7Ky\nsoQQL/QbAgAAAGqNzs/bqthDyesVumESpT1nVRm1Wr1jx45NmzY5OzuvXr360aNHL9S7paWl\nq6urjY1NkyZNxo0bV1JScu3aNSMjo9zcXO0xubm5pb9Bat++fSEhISEhIdnZ2S90LQAAAKAB\ne+aI+9GjR/ft23fnzh0rKytXV9fIyMj58+cPGDAgICCga9euFez9/v37WVlZHTp0UHYfP36s\nr69vZWUVFhamtOTn52dkZFhZWWlPadeunbJRXFxcmScEoEqzkxXMUQYAoN4pe8T9119//fTT\nT4cMGTJv3rwePXpMmzYtMzPzwIEDS5YsGTZs2NGjRyvY+40bN1auXHnz5s2SkpKQkBBjY+P2\n7dv36NHjzp07ERERjx8/Dg4Otre3Nzc3r75nBAAAADRAZY+4nz17dt26dUOHDlV2S0pKIiIi\nbG1t33777cGDB8fExFSw9x49erz11lurVq3Ky8uzsbFZsWKFnp6enp7e8uXLt23btmHDhi5d\nuvzjH/+onqcCAAAANFxlB/cuXbr4+voaGxubm5tfv359//7933zzjfJQu3bttLNZKmLcuHHj\nxo17orFbt27bt2+vXMUAAABAI1R2cB8/fvyFCxdmzZqVkZFhaWm5ePFiZdl1AAAAAHXimavK\nrFixIj4+PiMjIzo6etasWbVZEwAAABqzw4cPW1hY/OUvfxk4cGDTpk1nz55dUFBQExdydnb2\n8/Or3Llnzpzx9/evyJELFy4MCgqq3FVKe85ykAAAAEBtOn/+/NSpU7/88svk5OTw8PCkpKTo\n6OjVq1ffvHnz4sWL2sMSExOvXr0qhMjPzz937lxcXFxJyX+/zzU2Nvbu3bvKaih//vmn9pSi\noqKLFy9evnz5idUL79y588cff5T+ZqGn+xRCFBYWRkdHp6SkCCEyMjICAgJOnTqllPTo0aOY\nmBilntI1xMfHp6WlaTSaQYMGldNzBZX3BUwAAABALfvyyy+nTJny1ltvKbutWrX697//bWJi\nkpCQMGrUqFu3bunp6Qkhpk6d6uHhkZ+fP3LkyN69e9+4ccPExCQ0NFRPT2/NmjWGhoZFRUXN\nmzcPCgr6448/rK2t4+Pjx40b5+DgkJWVlZ2dfeLECWVhw++///7rr79Wq9Xx8fG//vqrjY1N\nXFzc030mJiaOGTOmU6dOSUlJ48aNe/fdd8PCwgwMDH755ReVSjVmzBg7O7v79+8bGxsfOXLE\nyMjI09PTwMAgJSXFy8try5YtY8eOdXFxKbPnir8yjLgDAACgHjl//ryjo2PpFgsLC1NT0z59\n+rRo0eLEiRNCiOvXrycmJo4ePfrYsWMLFiw4fPhwRETElStXTp8+LYTQ1dXNy8s7cODA9u3b\nX3/9dWUpcw8Pj1mzZh06dCg0NNTa2trX11fpPCMjIzw8PDQ01NHRcdu2bUKIMvv08PDQaDTH\njx8/d+7cjz/+2LZt26FDh44dO3b+/Pnu7u7vvPPOkSNHfv755yZNmuzdu1cIoaenl5mZGRYW\nNmTIEO0TKbPnimPEHQAAAPVIYWGhqalpmQ9NmzYtMDBw7NixwcHBb775pqGh4dKlSx8/fnzz\n5s28vDwLC4u7d+8qRw4YMEDZaNOmzb1794QQZ8+e9fDwUBqdnJyOHz+ubL/xxhs6OjpCiNde\ney0kJEQIUWaf4eHh7u7uQghTU9PIyMjSVYWFhY0fPz4qKkoI0a1bt9OnT8+ZM0cI8cSvH8/q\nueII7gAAAKhH2rdvHx///30FeElJiZKtp02b5uDgkJ2dHRwc/PHHHwshAgICVqxY0bt3b7Va\nff36de3EcUNDw9KnCyFycnJUKpXSYmxsnJ2drWybmJhoG5Vp7mX2mZ2dXbrP0nJycvz9/bX9\n9OzZ84metZ5VbQUR3AEAAFCPTJgw4fPPP//ggw+04+6bNm1KTk7eunVrx44dX375ZT8/v8uX\nLw8fPlwIsWzZssDAQCcnJyFEt27dyum2Q4cOycnJ9vb2Qojk5GQbGxul/fr168rGzZs3lW8r\nKrNPa2vrpKQkZXfv3r0jRozQ9mxjY+Pu7q4cr/0do0wVr7ZMBHcAAADUI3Pnzg0KCurbt6+b\nm5uVldV33323f//+0NBQ5dHp06cvW7Zs2rRpym2dJiYmiYmJ1tbWfn5+xcXFMTExz1o40sXF\nxcvLy9raOjMzc9euXQcPHlTaQ0JCRo4c2axZs6+//trb2/tZfb7//vtr1qxp2bJlXFycp6fn\npEmT1Gp1ZGRkTEyMq6urh4fH9u3bdXV1Fy9evGjRookTJ5ZZQ5k9P2sg/2kEdwAAANQjenp6\nJ0+e3L17d2ho6MOHD3v06HHu3DkrKyvl0cmTJy9atOjtt99Wdvft2+fj43Py5ElXV9dhw4Zt\n2bIlISHB3t7e0tJSOaBz587NmjUTQixatEitVnt6ehobG+/Zs2fgwIFCiJdeemnq1KmHDh26\nevWqm5ubs7Pzs/p0c3NTqVTr1683MzMLDQ1VqVSzZs1asmRJYGDg2rVr1Wq1t7d3YWHhe++9\np6T20jV0797dwsLiWT336NGjoq9Mdb3EAAAAQLXQ19efO3fu3Llzn34oMzOzffv2r7/+urLb\nv3//4OBg7aPK/JnSUXjRokXabRcXFxcXl9K9ffbZZ0KIJwbIy+xTCKHRaDQajbbd1tZWuZlV\nCDFjxowZM2aU7mTVqlXabS8vr/J7riCWgwQAAIAEHj16FBUV5eLisnTpUl3dxhhiG+NzBgAA\ngHRycnI++uijESNGlB72blSYKgMAAAAJmJmZnTx5sq6rqEuMuAMAAAASILgDAAAAEmCqDAAA\nAF5MyesL6rqExogRdwAAAEACjLgDAADgxegcz6hiDyWjWlZLJY0KI+4AAACABAjuAAAAgASY\nKiO9YN27dV0CAAAAahwj7gAAAIAECO4AAACABAjuAAAAgASY4w4AAID6pU2bNl27dtXuzp07\nNzw83NHR0dnZueKdLFy48IVOOXPmTEJCwsyZM1+o1NpEcAcAAED9Mnz48H379pVu6dGjR4sW\nLYQQsbGxrVu3vnfvXtOmTe/fv9+6devCwsLU1NSePXsaGhrGxMTo6enZ2dkJITQajXKKEKKw\nsPDSpUtmZmYdOnQQQly8eLFVq1Zt27YVQsTHx6vVamNj44CAgLy8vN69ezs4ODx69Cg+Pl6l\nUnXs2FFbQ3p6+rVr1ywtLdu3b19rL0VpBHcAAADUdytWrBg7dqyLi4unp6eBgUFKSoqXl9f/\n/M//6OrqGhsbp6enZ2RkvPbaa48ePQoNDZ09e/aSJUu0pyQmJo4ZM6ZTp05JSUnjxo3buHGj\nu7u7s7OzMrju7e3dvXv34cOHh4WFGRgY/PLLLyqVasyYMXZ2dvfv3zc2Nj5y5IiRkdGGDRt2\n7tzZp0+f6Ojo0aNH+/j41P6LQHAHAABA/XL79u1Tp05pd7t166bd1tPTy8zMDAsLE0Ls2LFD\nT09v9+7d+fn5pqamixYtmj59+smTJz08PJYsWaI9xcPDQ6PRuLm5ZWdnDx48+Pbt209f8eWX\nXx46dKiZmdn8+fMnT578zjvveHp6CiHGjBmzd+/eOXPm7Nq1KzAwsG/fvgUFBV988UXNPfdy\nENwBAABQv1y4cGH16tXa3TVr1pR+1NHRUbvdq1cvIYSRkVGzZs2U7TZt2mRmZpY+Pjw83N3d\nXQhhamoaGRn53KuHhYWNHz8+KipKCNGtW7fTp0/PmTNn2LBhGo1m7ty5o0eP/uijj6rw5CqP\n4A4AAID65ek57ps3b9Zum5iYaLcNDAye3i4pKSl9bnZ2tqGhYcWvnpOT4+/vr71Kz549hRC+\nvr4hISEhISErV66cNm1a6XpqDcEdAAAADZm1tXVSUpIy32bv3r0jRowwNDR89OiR8uitW7e6\nd+9e+ngbGxt3d3cnJychRElJiY6OjhDiwYMHEyZMmDBhwv379zt06LBkyRIrK6tafiKs4w4A\nAICG7P3331+zZk14eLifn9/y5ctNTU27du164sSJrKyskydPRkdHK4ep1erIyMiYmBhXV1cP\nD49z585FRkY6OjoeOnQoPz/fwcHh22+/TUxMPHXqlKGhoXa9mtrEiDsAAADql9KLuCu6d+9u\nYWEhhLC3t7e0tFQaS28PGjTI2NhYCGFiYtK/f//Sp7i5ualUqvXr15uZmYWGhqpUqg8//HDl\nypWTJ092cnJasWKFksJnzZq1ZMmSwMDAtWvXqtVqb2/vwsLC9957b+LEiUKIEydObNmyZc+e\nPZaWlt9//71yrVpGcAcAAED98vHHHz/R4uXlpWysWrVK21h6OyQkRNmwtbU9cOBA6VOEEBqN\nRqPRaHfNzMx8fX2fuIStra22kxkzZsyYMaP0ow4ODn5+fpV6NtWGqTIAAACABAjuAAAAgAQI\n7gAAAIAECO4AAACABAjuAAAAgARYVQYAAAAvpmRUy7ouoTFixB0AAACQACPuAAAAeDHf+lS1\nh6lLq6OORoYRdwAAAEACjLjjv4J179Z1CQAAAHgmRtwBAAAACRDcAQAAAAkQ3AEAAAAJENwB\nAAAACRDcAQAAUL+0adPGsZRvv/22Kr05Ozv7+flV7twzZ874+/tX5MiFCxcGBQVV7ioVxKoy\nAAAAqF+GDx++b9++0i0XL15s1apV27ZthRDx8fFqtbp9+/ZCiPT09GvXrllaWiq7QoiioqL4\n+HhDQ0NbW1td3f8bpL5z587169e7detmbGystOTn58fGxhobG3ft2lVHR0dpLCwsvHTpkpmZ\nWYcOHTIyMgICAvLy8nr37u3g4PDo0aP4+HiVStWxY0fl4NjY2NatW9+7d69p06YajaZFixbl\n9Fx1BHcAAADUd+7u7s7OzjNnzhRCeHt7d+/e/cMPP9ywYcPOnTv79OkTHR09evRoHx+f+Pj4\ncePGOTg4/D/27jygqmpv/P9ilFlETHHEWcGpUOqqdZ2HBosyr6XmY6mhNpgziuKYmVaaN8u0\nWw44oSjdEFM0FUUlEQdQHEDECRAZBGTm/P7Yv+d8eRRhc/YRWPV+/XXOhs/nrL3P3vt8zjpr\nr/3gwYOsrKx9+/bVqVNHCHHgwIGffvrJ1tY2Njb22LFjrq6uFy9eHDRokIeHx82bN+3s7EJD\nQ83Nza9evfrKK6+0bNkyLi7utddee++9944fP25paRkWFmZjY/PKK6+0a9cuPT3d2tp6z549\nVlZWfn5+lpaWN27cWLx48apVq1599dWxY8eWmdkoG4GhMgAAAKhZkpOTD5dy717Zd5tZt26d\nv7//li1bzpw54+zsLISYPXv2mDFjAgMDQ0NDmzZtunr1auU/U1NTw8PDJ2flEAAAIABJREFU\nQ0NDe/Xq9d133wkhgoODJ02atHv37lOnTl27du3o0aNKuLe3d0hISGRk5KFDhxo0aNCnT59X\nX3114sSJPj4+77zzzp49e44cOWJmZrZp0yYhhLm5eVpa2vHjx3v37q1vVZmZjYIedwAAANQs\n58+fnz9/vv7pggULyvy3fv36eXt7jx8//uWXX545c6YQ4uTJk7Nnz1b+2rdv35CQEOXxwIED\nlSErL774YlBQkBBi+vTpxcXFt2/fzs3NdXFxUb4bhIeH+/j4CCHs7e2joqJKv9bx48eHDBly\n9uxZIYS7u/vRo0fHjRsnhOjVq9cjrSozs1FQuAMAAKBmeXyM+/Llyx//t9WrVwcFBQUFBc2d\nO3fEiBHffPNNdna2jY2N8ldra+usrCzlsZ2dnX5hbm6uEGLjxo2+vr4eHh62traJiYk6nU4I\nkZWVVatWrTKblJ2d/csvv+jzdOnS5ZHMemVmNgoKdwAAANR0tWrVKioqUh7fvXu3Q4cOQoiM\njAwvLy8vL6/09PRmzZpNnTq1WbNm169fd3NzE0Jcv37d1dVVCUlMTFQe3L59u2HDhkKIGTNm\n+Pv79+3bVwjh7u6u/LVp06ZxcXHK002bNg0YMEDfAFdXVx8fH+X/dTpdOZeclpnZKBjjDgAA\ngJquffv2+/bte/Dgwe+//37u3DkhRF5eXseOHbds2XL16tXDhw/XqlWrbt26Y8eOXbx48YUL\nF44cObJu3bqxY8cq4UFBQYcPH46Kivrpp5+8vLyEEHZ2dlevXr169erMmTNLSkqio6Pz8/Pf\nf//9BQsWhIeHr1+/ftasWfb29ra2tlFRUdHR0RMmTJg9e3ZkZGRUVFSvXr0CAwOf1NQyMxtl\nI9DjDgAAgJqlffv2jyyZNm3a3Llzhw0b1rdvX19f37p161pZWe3bt2/VqlUbNmxo1KjRgQMH\nrK2tP/30U1tbWz8/P2tr6w0bNnTv3l0I0bZt23fffTcwMDA+Pn7KlClDhw4VQmzevHn58uW/\n//77hAkT+vXrt2rVqsuXL0+ZMsXGxubLL790dHQMDQ21sbEZM2bM1KlT/f39ly5damtr+8UX\nXxQUFIwePfrNN98UQri5uTVq1EhpYYcOHVxcXJ6UuVOnTto3C4U7AAAAapY5c+Y8ssTR0VE/\nRYxex44dH7+z0tixY/Ud7YolS5YIIZRSW++FF17YtWuX/mn//v2VB97e3t7e3vrlrVq1Ui5m\nFUKMGjVq1KhRpZPMmzdP/3jx4sXlZ9aOoTIAAACABCjcAQAAAAlQuAMAAAASYIw7AACAHPyj\nnQ2MHGzUdqCa0OMOAAAASIAedwAAUKWGXYo1NLSdMdsBDd6dXt0t+Fuixx0AAACQAD3uAAAA\nqJyi9w3+2eT/Z/4ffj+pNHrcAQAAAAlQuAMAAAASoHAHAAAAJEDhDgAAAEiAwh0AAACQAIU7\nAAAAIAEKdwAAANQsLi4u/fr169evn7u7u6ur67lz59REnThx4pdffqnUCw0dOnT9+vWGNLEy\nL/fRRx/t3LnTsFcpjXncAQAAULP07dt38+bNymM/P7+PP/746NGjMTExzzzzzP379x0cHBo2\nbFhYWBgbG1urVq1WrVqZmpqmpqZu3LgxNzfXw8OjY8eORUVFsbGxNjY2LVq00Kd9JES/PCUl\nJTEx0d3d3draWlmSl5cXExNjbW3dvn17ExMTZWFBQcGlS5ccHR2bNWtW4cuVbq23t3fdunXL\nyawShTsAAABqrjfffPPrr78WQvj5+VlaWt64cWPx4sUPHjx47bXXOnbs+ODBg6ysrH379t2+\nffv48eOWlpZhYWE2NjavvPJKu3bt0tPTra2t9+zZY2VlFRsb+0hInTp1hBAHDhz46aefbG1t\nY2Njjx075urqevHixUGDBnl4eNy8edPOzi40NNTc3Pzq1auvvPJKy5Yt4+LiXnvttffee6/8\nlyvd2lWrVr366qtjx44tM7P6TcFQGQAAANRcycnJzs7OQghzc/O0tLTjx4/37t179uzZY8aM\nCQwMDA0Nbdq06erVqzt37tynT59XX3114sSJPj4+77zzzp49e44cOWJmZrZp0yYhxOMhSv7U\n1NTw8PDQ0NBevXp99913Qojg4OBJkybt3r371KlT165dO3r0qBLu7e0dEhISGRl56NChBg0a\nlP9ypVurX5cyM6tHjzv+7vyjnTXFDzZSOwAAwP9KTU09efKkECIpKcnX13fixInK8l69eikP\nTp48OXv2bOVx3759Q0JCSocfP358yJAhZ8+eFUK4u7sfPXp03LhxTwoZOHCgMmTlxRdfDAoK\nEkJMnz69uLj49u3bubm5Li4u9+7dE0KEh4f7+PgIIezt7aOioip8udKt1Sszs3oU7gAAAKhZ\nzp8/P3/+fCFEnTp1Zs6cOWrUKGW5nZ2d8iA7O9vGxkZ5bG1tnZWVVTo8Ozv7l19+0f9zly5d\nygnR/5u1tXVubq4QYuPGjb6+vh4eHra2tomJiTqdTgiRlZVVq1atMltb5suVzqxXZmb1KNwB\nAABQs/Tp00d/cWqZmjVrdv36dTc3NyHE9evXXV1dS//V1dXVx8enb9++QgidTqd0qD8pJDEx\nUXlw+/bthg0bCiFmzJjh7++vhLu7uyt/bdq0aVxcnPJ006ZNAwYMKP/lylRmZvUY4w4AAADJ\njB07dvHixRcuXDhy5Mi6devGjh0rhLC1tY2KioqOjp4wYcLs2bMjIyOjoqJ69eoVGBj4pBAh\nRFBQ0OHDh6Oion766ScvLy8hhJ2d3dWrV69evTpz5sySkpLo6Oj8/Pz3339/wYIF4eHh69ev\nnzVrlr29ffkvV6YyM6tfa3rcAQAAULO0b9/+8YVubm6NGjVSHn/66ae2trZ+fn7W1tYbNmzo\n3r27EGLMmDFTp0719/dfunSpra3tF198UVBQMHr06DfffPNJIW3btn333XcDAwPj4+OnTJky\ndOhQIcTmzZuXL1/++++/T5gwoV+/fqtWrbp8+fKUKVNsbGy+/PJLR0fH0NBQGxub8l+udGs7\ndOjg4uLypMydOnVSuVko3AEAAFCzzJkz5/GF8+bNK/107Nix+l5zRatWrZSrS4UQo0aN0o+M\nLydkyZIlQgil1NZ74YUXdu3apX/av39/5YG3t7e3t7fKlyvd2sWLF5efWSUK92qzy7Ry1xED\nAADg74wx7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkwMWpAAAAqBzz/7Sr7ib8HdHjDgAA\nAEiAwh0AAACQAIU7AAAAIAEKdwAAAEACFO4AAACABCjcAQAAAAnIMR3kLtN71d0EqMI7BQAA\n8JTQ4w4AAABIQI4edwD4G/KPdjYwcrBR2wEAqBnocQcAAAAkQOEOAAAASIDCHQAAAJAAhTsA\nAAAgAQp3AAAAQALMKmMIZisHAABAFaPHHQAAAJAAhTsAAAAgAYbKAADwf3DrKwA1Ez3uAAAA\ngAQo3AEAAAAJMFQGAGqoYZdiDQ1tZ8x2AABqBnrcAQAAAAnQ4w4AgGS4fBb4e6LHHQAAAJAA\nhTsAAAAgAQp3AAAAQAKMcQcAADJhwiX8bdHjDgAAAEiAHnf83WnouVHQfwMAAKoCPe4AAACA\nBCjcAQAAAAlQuAMAAAASoHAHAAAAJMDFqQCAGmeX6b3qbgIA1Dj0uAMAAAASoHAHAAAAJEDh\nDgAAAEiAwh0AAACQAIU7AAAAIAFmlQHw9+If7awpfrCR2gEAQCXR4w4AAABIgMIdAAAAkACF\nOwAAACABCncAAABAAhTuAAAAgAQo3AEAAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAA\nkACFOwAAACABCncAAABAAhTuAAAAgAQo3AEAAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIgMId\nAAAAkIB5dTegSu0yvVfdTQAA1HTDLsUaGtrOmO0AHsPO+TdHjzsAAAAgAQp3AAAAQAIU7gAA\nAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAAQAIU\n7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAA\nQAIU7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAfPq\nbgAAAH8p/tHOBkYONmo7APzl0OMOAAAASIAedwByMLwXU0FfJgBAcvS4AwAAABKgcAcAAAAk\nwFAZAAAkM+xSrKGh7YzZDgBVix53AAAAQAIU7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAk\nQOEOAAAASIDpIAHIQcP8dwpmwQMAyI0edwAAAEACFO4AAACABCjcAQAAAAlQuAMAAAAS4OJU\nAHgqdpneq+4mAAD+UuhxBwAAACRA4Q4AAABI4KkPlUlOTg4MDExJSWnatOlbb73l4OCg0+l8\nfX11Op3yD40aNZo0adLTbgYAAAAgtadbuOfl5fn4+AwcOLB3794hISHLli1bsmTJw4cPL168\nuGTJEuV/rK2tn2obAAAAgL+Ap1u4p6amdu/e/V//+pcQon79+mPGjCkpKcnJybGzs3Nzc3uq\nLw0AAAD8lTzdwr1x48Zjx45VHt+4ccPFxcXU1DQnJ8fCwmLnzp3x8fGNGjXy8vKysbF5qs0A\nAAAAZFdF00FmZmauXbt2zJgxQoji4mIrK6tatWoNHjx437598+bNW758uYmJifKfr7/+elZW\nlhDCwcGhatqGGoh59AAAAB5RFYV7UlLSwoUL33rrLU9PTyFEq1at1qxZo/ypffv2I0eOTExM\nbNasmbLk1VdfzcvLE0L88MMPVdA2AAAAQApPvXBPSkqaN2/e+PHju3btql9YXFxsZmYmhDA3\nN7e1tVUqdcW4ceOUBytXrnzabQMAAABk8XTncS8pKfn8888/+OCD0lV7WFjY5MmTi4qKhBCX\nLl3Kyclp0qTJU20GAAAAILun2+N+4sSJO3fuhISEhISEKEu8vb27d+9+7Ngxb29vFxeXhISE\njz76iItTAQAAgPI93cLdzc3Nz8+v9BJHR0czMzMfH5+kpKTs7OzGjRtbWVk91TYAAAAAfwFP\nt3CvU6dOnTp1yvxTgwYNnupLAwCAp8E/2tnAyMFGbQfw91NF00ECAFBlqCwB/CU93YtTAQAA\nABgFhTsAAAAgAQp3AAAAQAIU7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDC\nHQAAAJAAhTsAAAAgAfPqbgAAAEY27FKsoaHtjNkOADAqetwBAAAACVC4AwAAABKgcAcAAAAk\nQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAAQAIU7gAAAIAEKNwBAAAACVC4AwAAABKgcAcA\nAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAAQAIU7gAAAIAEKNwBAAAACVC4AwAAABKg\ncAcAAAAkQOEOAAAASIDCHQAAAJCAeXU3AACAv5Rhl2INDW1nzHYA+Muhxx0AAACQAIU7AAAA\nIAEKdwAAAEACjHEH8NT5Rztrih9spHYAACAzetwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAA\nSIDCHQAAAJAAhTsAAAAgAQp3AAAAQAIU7gAAAIAEuAETgL+XYZditSVoZ5x2AABQSRTuAACg\nEjR8++V7L6AJQ2UAAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3\nAAAAQAIU7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkYF7dDQD+IvyjnTXFDzZSOwAAwF8U\nPe4AAACABCjcAQAAAAlQuAMAAAASoHAHAAAAJMDFqQCeumGXYrUlaGecdgAAIDN63AEAAAAJ\nULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkACFOwAAACABCncAAABAAhTuAAAAgAQo3AEA\nAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkACFOwAAACABCncAAABAAhTuAAAAgAQo\n3AEAAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkACFOwAAACABCncAAABAAhTuAAAA\ngAQo3AEAAAAJmFd3A4C/iGGXYrUlaGecdgAAgL8oetwBAAAACVC4AwAAABKgcAcAAAAkQOEO\nAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAAQAIU7gAAAIAEKNwBAAAACXDnVAD4a/KPdjYwcrBR\n2wEAMBJ63AEAAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkACFOwAAACABCncAAABA\nAhTuAAAAgAQo3AEAAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIwLy6GwAAeCqGXYo1NLSdMdsB\nADASetwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAAQAIU\n7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAA\nQAIU7gAAAIAEKNwBAAAACVC4AwAAABKgcAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3\nAAAAQAIU7gAAAIAEKNwBAAAACZhXdwMA4K+pMCDVwMjBzkZtCADgL4IedwAAAEACFO4AAACA\nBCjcAQAAAAlQuAMAAAASoHAHAAAAJEDhDgAAAEiAwh0AAACQAIU7AAAAIAEKdwAAAEACFO4A\nAACABCjcAQAAAAlQuAMAAAASoHAHAAAAJEDhDgAAAEiAwh0AAACQAIU7AAAAIAEKdwAAAEAC\nFO4AAACABCjcAQAAAAlQuAMAAAASoHAHAAAAJGBe3Q0AylAYkKopfrCzkRoCAABQU9DjDgAA\nAEiAwh0AAACQAENlYExah7gIRrkAAACUjR53AAAAQAIU7gAAAIAEKNwBAAAACVC4AwAAABKg\ncAcAAAAkQOEOAAAASIDCHQAAAJAAhTsAAAAgAQp3AAAAQAIU7gAAAIAEKNwBAAAACVC4AwAA\nABKgcAcAAAAkYF7dDVDFP9pZa4rBxmiHURUGpGpNMVjzZgGAGsnwMyQnRgB/XXIU7saitVbm\n8wAAAADV5O9VuKMcfKsBAACoyeQo3IdditWco50R2gEAAABUEy5OBQAAACRA4Q4AAABIgMId\nAAAAkACFOwAAACABOS5ORTmYDQYAAODvgB53AAAAQAIU7gAAAIAEKNwBAAAACVC4AwAAABKg\ncAcAAAAkUG2Fe1paWmxsbE5OTnU1AAAAAJBI9UwHGRgYuHv3bldX14SEhMmTJ3t4eFRLMwzG\nDIwAAACoYtVQuN+/f3/nzp3ffvuts7PzmTNn1qxZs27dOhMTk6pvCQAAACCLahgqc/78eTc3\nN2dnZyHEc889l5ube/v27apvBgAAACCRauhxv3fvXt26dfVP69Wrd+/evcaNGytP161bl5eX\nJ4SwsrKq+rYBAAAANZOJTqer4pfcsmXLgwcPvL29laefffbZu+++261bN+Xp66+/npWVJYRI\nT09/7rnnfvrppypuHgAAAFADVUOPu729/Z07d/RPs7Ky7O3t9U+DgoKUBy1btqzqlgEAAAA1\nVTWMcW/RosWVK1eUx+np6enp6c2aNav6ZgAAAAASqYYedzc3Nysrq/Xr1z///POBgYH9+/e3\ntrau+mYAAAAAEqmGHncTE5MFCxYUFhbu3Lmzbdu2H3zwQdW3AQAAAJBL9dyAqU6dOhMmTKiW\nlwYAAABkVA097gAAAAAqi8IdAAAAkACFOwAAACABCncAAABAAhTuAAAAgAQo3AEAAAAJULgD\nAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkACFOwAAACABCncAAABAAhTuAAAAgAQo3AEAAAAJ\nULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkACFOwAAACABCncAAABAAhTuAAAAgAQo3AEA\nAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkACFOwAAACABCncAAABAAhTuAAAAgAQo\n3AEAAAAJULgDAAAAEqBwBwAAACRA4Q4AAABIgMIdAAAAkICJTqer7jaUrWPHjomJic7OzhX+\np4mJiRBC+4rUqDw1qjHGylP1jTE1Nb169erjy7dt2zZnzhzjvlaFeWrIu2DcPDWqMcbKozLJ\nSy+99PPPPz++/KOPPgoJCTHWqzwpXGhY2eoNF8ZYfakbr6YBc+bMef/99x9f3rlz5+zsbKO8\nRM0MF9X6/sqye2h89ZCQkDZt2hj2Eqh25tXdgCc6dOjQpEmT1PxnSkpKQUFB48aNNb5ifHx8\nnTp16tSpoyVJTk5OcnJykyZNLCwstOQx1kpdv37d0dHRKCvVqFGjWrVqaclz7969vLy8Jk2a\naEkihEhISHBwcHBycqrwP5WT4OOcnZ09PDyM+1rlyM3NvXv3bsOGDa2srLTkSU1NffjwYdOm\nTbUkEULcuHHDzs6ubt26WpLk5eXduXPHKCuVk5PTrFkzLUmEEImJiTY2Nmq+6pcjPz//9u3b\nLi4u1tbWFf5z69aty1zeokWLCveuO3fumJiYuLi4GNJKIVJSUvLz8w0+lO7fv5+VleXq6mpY\neEZGRlpaWvPmzZ90fJUvOzs7JSWladOm5uaGfABpPJoKCwtv3rxZv359W1tbA8JLSkoSEhKc\nnZ0dHBwMCBeqTynPPPNMmcu7dOmSm5tbTqBOp7t+/bqTk5Ojo6PBLbS3tzf4/HDr1i0LC4v6\n9esbFn737l2dTtewYUPDwjV+yqSlpT148MDgQyMzM/P+/fuurq6mpoYMZ9BYPyjnZDWnLzXn\nN9RcOvnNmjVr+PDh2vN069Zt7dq1GpMcOnTIw8MjLi5OYx5fX9+hQ4dqTKLT6f7xj398//33\nGpMcOXLEw8Pj8uXLGvPMnz//jTfe0JhEp9O9+OKLq1ev1p5HjV69eq1cuVJjklOnTnl4eJw7\nd05jnqVLlw4ePFhjEp1O169fvxUrVmhMEhkZ6eHhcebMGY15vvzyy/79+2tMotPpBg0a9MUX\nX2hMcu7cOQ8Pj4iICO3tKd+4ceMmTpxocPjs2bPffvttg8O//vrr3r17Gxy+ceNGDw+P/Px8\nw8L37t3r4eFx+/Ztw8L//PNPDw+Ps2fPGhZ+/fp1Dw+P0NBQw8KzsrI8PDy2bdtmWLhOp+vZ\ns+e///1vg8MrlJ+f7+HhsWHDBoMz9OnT56uvvjI4fNiwYT4+PgaHT5w4cdy4cQaH+/n5eXl5\nGRy+evXqF1980eDwrVu3enh4ZGdnGxZ+4MABDw+PhIQEw8LPnj3r4eHx559/GhYOWTDGHQAA\nAJBAzR0qo16rVq3s7e215/H09NQ+NMXJycnT01P771AtWrTQONhG0bVr10aNGmlMUqdOHU9P\nT8N+WS6tefPmJSUlGpMIIbp27ap9vI1KHh4e2l+rdu3anp6ednZ2GvO4uro+ePBAYxIhxLPP\nPqt9vI29vb2np6f2Q69Zs2bPPfecxiRCiGeffVb7eBs7OztPT8/atWtrb0/52rdvb2ZmZnB4\ny5YttYxQatq0qcqhYmVycXHx9PQ0bJyMEMLZ2dnT09PgcXcODg5adjxra2tPT0+DB7+ZmZl5\nenoaPA5ECNG1a1ftHzTlMDU19fT0NHioiRDCw8NDy6HUsWNHLa/etm3b4uJig8NbtGhhcKwQ\nokmTJl27djU4vEGDBp6engYf2hrrB+WcbPAgLsii5l6cCgAAAECPoTIAAACABCjcAQAAAAnI\nNMb9woULT/pTx44dVSbJycmJj48v80+2trbqh8elpKQkJyeX+af69es/aSavx9Wolfrbqml7\nRXx8fE5OTpl/atGihcqLDVipchhrpSqk8QDXGK5xm2sM17iRq+w9KlPNP6lqbKH2Fazefbt6\nDw2gGslUuM+ZM+dJM8uWeRuUMsXHx8+fP7/MqzfatWs3c+ZMlXkOHjwYFBRU5kUkr7/++htv\nvKEyT41aqb+tmrZXrF+//ubNm2XOcj1z5sx27dqpScJKlcNYK1UhjQe4xnCN21xjuMaNrP09\nmj17dpnL+/bt27dv3/Jja/5JVWMLta9g9e7b1XtoANVIpsLd1NRUfS1bDjc3t0WLFmnP4+Xl\n9a9//Utjkpq2Un9bNWqvEEL4+Pi4ublpTMJKlcNYK1U+jQe49vODxm2uMVzjRtYYbmdnV+YM\nISpvr1PzT6oaW6gxvHr37Wo/NIDqIlPhDgCASi1bthwwYEB1twIAjInCHf/PwYMHDx48WOaf\n1Py4rPekX6iFEJ9//rkhLQMAecTHx69fv77MPzVv3nzcuHFV3B4Y17p1665fv17mn8aOHVvh\nFQIaP2qN9UkNSVG44/9JSUnR+OOyIiYmZtKkSUZrFgBIJScnJzMz8/XXX3/8Twbf+wk1x/Xr\n19u2bevi4vL4n+rUqVNhuMaPWmN9UkNSMt2AKSkpqUGDBhqTFBQUZGdnaz91ZmVlmZiYaL8X\nZo1aqa1bt5qammof+PvGG2/s2bNHY5KqVNP2ivv379vb21taWmpJwkqVw1grVSGNB7jGcI3b\nXGO4xo2s/T06derU888/b1isxj3twoULO3bseKpD5DW2UPuhVL37dvUeGrNnzx45cqTBQ+Q1\nftQa65MakpJpHvfExMRHluTn5+fl5VUqSV5eXkpKyiMLHz58WFRUVKk8mZmZj998Pisrq7Jf\nhGrUStUoOTk5F57gSbOYGSw5OTkgIOCRbXjq1Klr165VKk90dPR///vfR96+kJCQ+/fvVyrP\nwYMHw8LCSu9LOTk5wcHBBQUF6pOwUuUw1kpVqG7duv7+/mfOnNEvuXr16qFDh6osPCwsLCgo\nSL8kJydn586dKs8wGsPt7e0TExN/+OGH0gv37NmTlJRUBeFCiOeQuPwzAAAgAElEQVSff/7a\ntWuHDx82oH/K0tLSyckpISHhnXfeCQoKKikpqWyGp83S0rKkpOT7779XngYHB//3v/89dOjQ\nqVOn1JwhNYaL/905b968KYSIiIjYs2dPaGhoeHj4uXPnpAg/duzY8ePHhRAJCQmBgYG///57\nWFjYmTNnHj58qCYDUF1kKtyXLVv2yJJ79+5NnTo1KytLfZIbN25s27btkYURERFLly6t1Mk9\nLCwsPDz8kYUbNmzYvXu3+iSihq1UjaLMVvZ1WQICAoz7WhkZGSdOnJg+fXpERIR+YXp6up+f\nX3R0tPo8CQkJR48e/fTTT2/fvq1fGBcXN2XKlEq9oWfPng0MDFywYIG+QioqKgoLC1u8eLH6\nJKxUOYy1UhX68ccfo6OjS89bZ2Zm9uuvv/r7+1dB+JEjR7Zu3dqwYcPSC69cubJgwYIqCE9J\nSVm4cOEjE65nZWXNmDEjMzPzaYcrLl68GBAQMHPmzBs3bqgMKa2kpKR169YpKSkzZswwen+B\nRiUlJfPmzSspKVG+VPzxxx9RUVF//vnnjh07njS83ojhQogff/wxJiZGma/z4sWLR44ciYqK\n2r9//9y5c2t++JEjR7Zs2eLo6CiEuH//flBQUExMzOHDh1esWBEXF6cmA1BdZCrcdTrdjf+r\nuLi4e/fuq1atqlSe3NzcR/I0b968uLi4dMeSGhkZGY/kGTRo0N69e69cuSLpSrm6uhplhFyv\nXr20JxFCuLm5/VyWpzGJsqur64oVK37++Wd97+agQYPGjx+/b9++SuXp27fvxIkTFyxYcO/e\nPWXJRx995Onp+fjXvPJ98skn7u7uixYtKi4uFkLUrl176dKl9+7dK11oVoiVKoexVqp8J0+e\nnDNnTrNmzYQQc+bMEUK0aNFi4cKF+/fvr5rwcePGdevWTQixbt26hIQEW1vb2bNnZ2dn37p1\n62mHnz59ul+/fm+++aYQ4tChQ8oPBaNGjerRo4eajawxXG/gwIFjxoz5+uuvN23aVKmfdxS1\natUaN27c+PHjV69e/Z///Cc/P19NlKOjY+fOnSv7WpWSmJhoZWU1adIkU1NTIcQbb7zRqFGj\nmTNnfvDBB1UQLoQ4ceKEr6+vs7OzEMLLy0un002fPn3+/PlKwhoeHh4e/uGHH7q7uwshPDw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7xo9aY31SQ1ZVP6xei/v370dERCQnJytP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\nZ9GiRZM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tLSgh7V7+3tHcsR7Gwlo9XX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\nu9paWgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 100)\n",
    "ps.rel = transform_sample_counts(ps, function(x) x/sum(x)*100)\n",
    "# agglomerate taxa\n",
    "glom <- tax_glom(ps.rel, taxrank = 'Phylum', NArm = FALSE)\n",
    "ps.melt <- psmelt(glom)\n",
    "# change to character for easy-adjusted level\n",
    "ps.melt$Phylum <- as.character(ps.melt$Phylum)\n",
    "\n",
    "ps.melt <- ps.melt %>%\n",
    "  group_by(body.site, Phylum) %>%\n",
    "  mutate(median=median(Abundance))\n",
    "# select group median > 1\n",
    "keep <- unique(ps.melt$Phylum[ps.melt$median > 1])\n",
    "ps.melt$Phylum[!(ps.melt$Phylum %in% keep)] <- \"< 1%\"\n",
    "#to get the same rows together\n",
    "ps.melt_sum <- ps.melt %>%\n",
    "  group_by(Sample,body.site,Phylum) %>%\n",
    "  summarise(Abundance=sum(Abundance))\n",
    "\n",
    "ggplot(ps.melt_sum, aes(x = Sample, y = Abundance, fill = Phylum)) + \n",
    "  geom_bar(stat = \"identity\", aes(fill=Phylum)) + \n",
    "  labs(x=\"\", y=\"%\") +\n",
    "  facet_wrap(~body.site, scales= \"free_x\", nrow=1) +\n",
    "  theme_classic() + \n",
    "  theme(strip.background = element_blank(), \n",
    "        axis.text.x.bottom = element_text(angle = -90))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00f2970",
   "metadata": {},
   "source": [
    "Genus-level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9046743c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'Sample', 'body.site'. You can override using the `.groups` argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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4+fOnXqSy+99MEHH+Tl5d2mEwAAAMCt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\neTAwMMjKyqIoytLSsqioiLmcrq6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1tS\nUiLzRthWJRKJ6EwOHyr9OeTn5+vq6rYou047QgMglpWVRSfOIkDQb3SE7qJF6Ek1BCmDAODD\nkrOBOwAAAADAp4lt+lgAAAAAAGhHGLgDAAAAAMgBDNwBAAAAAOQABu4AAAAAAHIAA3cAAAAA\nADmAgTsAAAAAgBzAwB0AAAAAQA5g4A4AAAAAIAcwcAcAAAAAkAMYuAMAAAAAyAEM3AEAAAAA\n5AAG7gAAAAAAcgADdwAAAAAAOYCBOwAAAACAHMDAHQAAAABADmDgDgAAAAAgBzBwBwAAAACQ\nAxi4AwAAAADIAQzcAQAAAADkAAbuAAAAAAByAAN3AAAAAAA5gIE7AAAAAIAcwMAdAAAAAEAO\n/C9PQh3gtMpb4AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 100)\n",
    "ps.rel = transform_sample_counts(ps, function(x) x/sum(x)*100)\n",
    "# agglomerate taxa\n",
    "glom <- tax_glom(ps.rel, taxrank = 'Genus', NArm = FALSE)\n",
    "ps.melt <- psmelt(glom)\n",
    "# change to character for easy-adjusted level\n",
    "ps.melt$Genus <- as.character(ps.melt$Genus)\n",
    "\n",
    "ps.melt <- ps.melt %>%\n",
    "  group_by(body.site, Genus) %>%\n",
    "  mutate(median=median(Abundance))\n",
    "# select group mean > 1\n",
    "keep <- unique(ps.melt$Genus[ps.melt$median > 2.5])\n",
    "ps.melt$Genus[!(ps.melt$Genus %in% keep)] <- \"< 2.5%\"\n",
    "#to get the same rows together\n",
    "ps.melt_sum <- ps.melt %>%\n",
    "  group_by(Sample,body.site,Genus) %>%\n",
    "  summarise(Abundance=sum(Abundance))\n",
    "\n",
    "ggplot(ps.melt_sum, aes(x = Sample, y = Abundance, fill = Genus)) + \n",
    "  geom_bar(stat = \"identity\", aes(fill=Genus)) + \n",
    "  labs(x=\"\", y=\"%\") +\n",
    "  facet_wrap(~body.site, scales= \"free_x\", nrow=1) +\n",
    "  theme_classic() + \n",
    "  theme(legend.position = \"right\", \n",
    "        strip.background = element_blank(), \n",
    "        axis.text.x.bottom = element_text(angle = -90))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b53432",
   "metadata": {},
   "source": [
    "# 9. Differential abundancce analysis\n",
    "\n",
    "Differential abundance analysis allows you to identify differentially abundant \n",
    "taxa between groups. There’s many methods, here DESeq2 is given as anexample. \n",
    "Features are collapsed at the species-level prior to the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0b436861",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data(ps)$body.site <- as.factor(sample_data(ps)$body.site) # factorize for DESeq2\n",
    "ps.taxa <- tax_glom(ps, taxrank = 'Species', NArm = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb60b36",
   "metadata": {},
   "source": [
    "# 9.1 DESeq2\n",
    "\n",
    "DESeq2 is a software designed for RNA-seq, but also used in microbiome analysis.\n",
    "You may be troubled by the “zero” issues in microbiome analysis. A pseudo count \n",
    "is added to avoid the errors in logarithm transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e833cd9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "converting counts to integer mode\n",
      "\n",
      "using pre-existing size factors\n",
      "\n",
      "estimating dispersions\n",
      "\n",
      "gene-wise dispersion estimates\n",
      "\n",
      "mean-dispersion relationship\n",
      "\n",
      "final dispersion estimates\n",
      "\n",
      "fitting model and testing\n",
      "\n",
      "-- replacing outliers and refitting for 6 genes\n",
      "-- DESeq argument 'minReplicatesForReplace' = 7 \n",
      "-- original counts are preserved in counts(dds)\n",
      "\n",
      "estimating dispersions\n",
      "\n",
      "fitting model and testing\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# pairwise comparison between gut and tongue\n",
    "ps.taxa.sub <- subset_samples(ps.taxa, body.site %in% c(\"gut\", \"tongue\"))\n",
    "# filter sparse features, with > 90% zeros\n",
    "ps.taxa.pse.sub <- prune_taxa(rowSums(otu_table(ps.taxa.sub) == 0) < ncol(otu_table(ps.taxa.sub)) * 0.9, ps.taxa.sub)\n",
    "ps_ds = phyloseq_to_deseq2(ps.taxa.pse.sub, ~ body.site)\n",
    "# use alternative estimator on a condition of \"every gene contains a sample with a zero\"\n",
    "ds <- estimateSizeFactors(ps_ds, type=\"poscounts\")\n",
    "ds = DESeq(ds, test=\"Wald\", fitType=\"parametric\")\n",
    "alpha = 0.05 \n",
    "res = results(ds, alpha=alpha)\n",
    "res = res[order(res$padj, na.last=NA), ]\n",
    "taxa_sig = rownames(res[1:20, ]) # select bottom 20 with lowest p.adj values\n",
    "ps.taxa.rel <- transform_sample_counts(ps, function(x) x/sum(x)*100)\n",
    "ps.taxa.rel.sig <- prune_taxa(taxa_sig, ps.taxa.rel)\n",
    "# Only keep gut and tongue samples\n",
    "ps.taxa.rel.sig <- prune_samples(colnames(otu_table(ps.taxa.pse.sub)), ps.taxa.rel.sig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9ebac6",
   "metadata": {},
   "source": [
    "# 9.2 Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5975784f",
   "metadata": {},
   "outputs": [
    {
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Td+oc8OGAEmZRBCCCGEEEII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48KCqqmpKSgrtgC+Q/58S28XFZfny5dLyNWvWuLi40Msla9Gi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\nFizIycn5+++/p0yZwkzIxRJvQdUTQkJCQ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 750
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 15, repr.plot.height = 10, repr.plot.res = 100)\n",
    "matrix <- as.matrix(data.frame(otu_table(ps.taxa.rel.sig)))\n",
    "rownames(matrix) <- as.character(tax_table(ps.taxa.rel.sig)[, \"Species\"])\n",
    "metadata_sub <- data.frame(sample_data(ps.taxa.rel.sig))\n",
    "# Define the annotation color for columns and rows\n",
    "annotation_col = data.frame(\n",
    "    Subject = as.factor(metadata_sub$subject), \n",
    "    `Body site` = as.factor(metadata_sub$body.site), \n",
    "    check.names = FALSE\n",
    ")\n",
    "rownames(annotation_col) = rownames(metadata_sub)\n",
    "\n",
    "annotation_row = data.frame(\n",
    "    Phylum = as.factor(tax_table(ps.taxa.rel.sig)[, \"Phylum\"])\n",
    ")\n",
    "rownames(annotation_row) = rownames(matrix)\n",
    "\n",
    "# ann_color should be named vectors\n",
    "phylum_col = RColorBrewer::brewer.pal(length(levels(annotation_row$Phylum)), \"Paired\")\n",
    "names(phylum_col) = levels(annotation_row$Phylum)\n",
    "ann_colors = list(\n",
    "    Subject = c(`subject-1` = \"red\", `subject-2` = \"blue\"),\n",
    "    `Body site` = c(gut = \"purple\", tongue = \"yellow\"),\n",
    "    Phylum = phylum_col\n",
    ")\n",
    "\n",
    "ComplexHeatmap::pheatmap(matrix, scale= \"row\", \n",
    "                         annotation_col = annotation_col, \n",
    "                         annotation_row = annotation_row, \n",
    "                         annotation_colors = ann_colors)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}