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<h1 class="title toc-ignore">OBEGLU Mâles</h1>
<h4 class="author">Magali Monnoye</h4>
<address class="author_afil">
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INRAE - Migale &amp; MICALIS<br><h4 class="date">2021-06-23 (Last updated: 2021-11-19)</h4>
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</div>


<ul>
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<li><p>Espace de travail collaboratif <a href="https://forgemia.inra.fr/magali.monnoye/analyses_16s">GitLab</a></p>
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<ul>
<li>Adresse du dépot: <a href="mailto:git@forgemia.inra.fr" class="email">git@forgemia.inra.fr</a>:magali.monnoye/analyses_16s.git</li>
</ul></li>
<li><p>Repository dans save_home/mmonnoye/analyses_16s</p></li>
<li><p>En premier, créer un nouveau repository dans GitLab et l’ouvrir dans Rstudio</p></li>
</ul>
<pre class="bash"><code>#Pour faire des git clone / git add.....:
#Via le terminal, se positionner dans le repertoire ou se trouve le depot donc ici:
#save_home/mmonnoye/analyses_16s/202106_StEtienne/ 
#et faire les git souhaités...

#Si gitlab fatal: pack exceeds maximum allowed size
#Pour supprimer dernier commit:
#git reset HEAD~
</code></pre>
<div id="metabarcoding-analysis" class="section level1">
<h1>Metabarcoding analysis</h1>
<div id="data" class="section level2">
<h2>Data</h2>
<pre class="bash"><code>#Je copie dossiers pour export html
#sur le terminal, je me mets dans le repertoire ~/save/dubii_mmonnoye/
#puis je copie les fichiers ci dessous:
cp -r report_files/ ~/save/analyses_16s/202106_Obeglu/
cp -r resources/ ~/save/analyses_16s/202106_Obeglu/
cp -r site_libs/ ~/save/analyses_16s/202106_Obeglu/
cp -r css/ ~/save/analyses_16s/202106_Obeglu/
cp -r img/ ~/save/analyses_16s/202106_Obeglu/</code></pre>
<p>Je me place dans le repertoire 202106_Obeglu</p>
<p>Import des séquences (fichiers FASTQ) sur migale</p>
<p>Je crée un .zip de mon fichier séquences et j’importe avec Upload</p>
</div>
<div id="contrôle-qualité" class="section level2">
<h2>Contrôle qualité</h2>
<p>Je lance l’outil <strong class="tool">fastqc</strong> dédié à l’analyse de la qualité des basemodules issues d’un séquençage haut-débit</p>
<p>J’utilise aussi <strong class="tool">multiqc</strong></p>
<pre class="bash"><code>#Creation dossier FASTQC
mkdir FASTQC

#Je lance fastq
for i in *.fastq.gz ; do echo &quot;conda activate fastqc-0.11.8 &amp;&amp; fastqc $i -o FASTQC &amp;&amp; conda deactivate&quot; &gt;&gt; fastqc.sh ; done
qarray -cwd -V -N fastqc -o LOGS -e LOGS fastqc.sh

#Je lance multiqc (-o pour specifier le dossier de destination)
qsub -cwd -V -N multiqc -o LOGS -e LOGS -b y &quot;conda activate multiqc-1.8 &amp;&amp; multiqc FASTQC -o MULTIQC &amp;&amp; conda deactivate&quot;
</code></pre>
<p>Lien vers le <a href="MULTIQC/multiqc_report.html">rapport multiqc</a></p>
</div>
<div id="frogs" class="section level2">
<h2>FROGS</h2>
<div id="nettoyage-des-reads" class="section level3">
<h3>Nettoyage des reads</h3>
<p>Je vais d’abord utiliser l’outil <strong class="tool">preprocess</strong></p>
<pre class="bash"><code>#Creer archive 
tar zcvf data.tar.gz *.fastq.gz
#suppression des fichiers fastq car ils sont dans l&#39;archive
rm -f *.fastq.gz

#Creer fichier FROGS
mkdir FROGS

#Commandes de filtrage pour séquences non contiguées
qsub -cwd -V -N preprocess -o LOGS -e LOGS -pe thread 8 -R y -b y &quot;conda activate frogs-3.2.2 &amp;&amp; preprocess.py illumina --input-archive data.tar.gz --min-amplicon-size 200 --max-amplicon-size 490 --merge-software pear --five-prim-primer ACGGGAGGCAGCAG --three-prim-primer GGATTAGATACCCTGGTA --R1-size 250 --R2-size 250 --nb-cpus 8 --output-dereplicated FROGS/preprocess.fasta --output-count FROGS/preprocess.tsv --summary FROGS/preprocess.html --log-file FROGS/preprocess.log &amp;&amp; conda deactivate&quot;

#Pour séquences déjà contiguées
qsub -cwd -V -N preprocess -o LOGS -e LOGS -pe thread 8 -R y -b y &quot;conda activate frogs-3.2.2 &amp;&amp; preprocess.py illumina --input-archive data.tar.gz --min-amplicon-size 400 --max-amplicon-size 500 --merge-software pear --five-prim-primer ACGGGAGGCAGCAG --three-prim-primer GGATTAGATACCCTGGTA --already-contiged --nb-cpus 8 --output-dereplicated FROGS/preprocess.fasta --output-count FROGS/preprocess.tsv --summary FROGS/preprocess.html --log-file FROGS/preprocess.log &amp;&amp; conda deactivate&quot;

#Pour voir avancé d&#39;un job
qstat
#Pour voir infos quand job terminé
qacct -j 3634840</code></pre>
<p>Les paramètres suivants ont été choisis :</p>
<table>
<thead>
<tr class="header">
<th>Parametre</th>
<th>Valeur</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>–min-amplicon-size</td>
<td>200</td>
</tr>
<tr class="even">
<td>–max-amplicon-size</td>
<td>490</td>
</tr>
<tr class="odd">
<td>–already-contiged</td>
<td>Séquences contiguées</td>
</tr>
</tbody>
</table>
<p>Lien vers le <a href="FROGS/preprocess.html">rapport preprocess</a></p>
<ul>
<li>Le nombre de séquences par échantillon</li>
</ul>
<table>
<thead>
<tr class="header">
<th>Preprocess</th>
<th>Min</th>
<th>Max</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Avant</td>
<td>50000</td>
<td>161000</td>
</tr>
<tr class="even">
<td>Après</td>
<td>45000</td>
<td>136000</td>
</tr>
</tbody>
</table>
<div class="alert comment">
Le pourcentrage de séquences gardées oscille entre 80 à 90 %
</div>
</div>
<div id="clustering" class="section level3">
<h3>Clustering</h3>
<p>Clustering à l’aide de <strong class="tool">swarm</strong> et d=1</p>
<pre class="bash"><code>qsub -cwd -V -N clustering -o LOGS -e LOGS -pe thread 8 -R y -b y &quot;conda activate frogs-3.2.2 &amp;&amp; clustering.py --input-fasta FROGS/preprocess.fasta --input-count FROGS/preprocess.tsv --distance 1 --fastidious --nb-cpus 8 --log-file FROGS/clustering.log --output-biom FROGS/clustering.biom --output-fasta FROGS/clustering.fasta --output-compo FROGS/clustering_otu_compositions.tsv &amp;&amp; conda deactivate&quot;

#Pour voir infos quand job en cours:
qstat
#Pour avoir infos quand job fini:
qacct -j 3634848
</code></pre>
</div>
<div id="remove-chimera" class="section level3">
<h3>Remove Chimera</h3>
<p>On détécte les chimeres avec <strong class="tool">vsearch</strong></p>
<pre class="bash"><code>qsub -cwd -V -N chimera -o LOGS -e LOGS -pe thread 8 -R y -b y &quot;conda activate frogs-3.2.2 &amp;&amp; remove_chimera.py --input-fasta FROGS/clustering.fasta --input-biom FROGS/clustering.biom --non-chimera FROGS/remove_chimera.fasta --nb-cpus 8 --log-file FROGS/remove_chimera.log --out-abundance FROGS/remove_chimera.biom --summary FROGS/remove_chimera.html &amp;&amp; conda deactivate&quot;

qacct -j 3634866
</code></pre>
<p>Lien vers le <a href="FROGS/remove_chimera.html">rapport remove_chimera</a></p>
<ul>
<li>Voici le nombre de séquences gardées et retirées</li>
</ul>
<table>
<thead>
<tr class="header">
<th>Remove Chimera</th>
<th>Kept</th>
<th>Removed</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Clusters</td>
<td>281981</td>
<td>589888</td>
</tr>
<tr class="even">
<td>Abundance</td>
<td>3294033</td>
<td>1552410</td>
</tr>
</tbody>
</table>
</div>
<div id="filtres-sur-labondance-et-la-prevalence" class="section level3">
<h3>Filtres sur l’abondance et la prevalence</h3>
<p>On retire les OTUs les moins abondants, limite 0,005%</p>
<p>On retire egalement les séquences phiX</p>
<p>J’utlise l’outil <strong class="tool">otu_filters</strong></p>
<pre class="bash"><code>qsub -cwd -V -N filters -o LOGS -e LOGS -pe thread 8 -R y -b y &quot;conda activate frogs-3.2.2 &amp;&amp; otu_filters.py --input-fasta FROGS/remove_chimera.fasta --input-biom FROGS/remove_chimera.biom --output-fasta FROGS/filters.fasta --nb-cpus 8 --log-file FROGS/filters.log --output-biom FROGS/filters.biom --summary FROGS/filters.html --excluded FROGS/filters_excluded.tsv --contaminant /db/frogs_databanks/contaminants/phi.fa --min-sample-presence 1 --min-abundance 0.00005 &amp;&amp; conda deactivate&quot;

qacct -j 3634896</code></pre>
<p>Lien vers le <a href="FROGS/filters.html">rapport filters</a></p>
<ul>
<li>Voici le nombre d’OTUs et de séquences gardés et retirés</li>
</ul>
<table>
<thead>
<tr class="header">
<th>Filters</th>
<th>Kept</th>
<th>Removed</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>OTUs</td>
<td>921</td>
<td>281060</td>
</tr>
<tr class="even">
<td>Abundance</td>
<td>2803366</td>
<td>490667</td>
</tr>
</tbody>
</table>
</div>
<div id="affiliation" class="section level3">
<h3>Affiliation</h3>
<p>Maintenant, je vais affillier les séquences sur la database <strong class="tool">silva</strong> v138</p>
<p>Création fichier <strong>affiliation.biom</strong> pour import <strong>affiliationExplorer</strong></p>
<pre class="bash"><code>qsub -cwd -V -N affiliation -o LOGS -e LOGS -pe thread 8 -R y -b y &quot;conda activate frogs-3.2.2 &amp;&amp; affiliation_OTU.py --input-fasta FROGS/filters.fasta --input-biom FROGS/filters.biom --nb-cpus 8 --log-file FROGS/affiliation.log --output-biom FROGS/affiliation.biom --summary FROGS/affiliation.html --reference /db/frogs_databanks/assignation/silva_138_16S/silva_138_16S.fasta &amp;&amp; conda deactivate&quot;

Job 3634906
qstat

#Rapport de l&#39;affiliation des OTUs
qsub -cwd -V -N affiliations_stats -o LOGS -e LOGS -b y &quot;conda activate frogs-3.2.2 &amp;&amp; affiliations_stat.py --input-biom FROGS/affiliation.biom --output-file FROGS/affiliations_stats.html --log-file FROGS/affiliations_stats.log --multiple-tag blast_affiliations --tax-consensus-tag blast_taxonomy --identity-tag perc_identity --coverage-tag perc_query_coverage  &amp;&amp; conda deactivate&quot;

Job 3634921
</code></pre>
<p>Lien vers le <a href="FROGS/affiliations_stats.html">rapport affiliations_stats</a></p>
<p>Lien vers le <a href="FROGS/affiliation.html">rapport affiliation_OTU</a></p>
<div class="alert comment">
La quasi totalité des échantillons ont un pourcentage d’identité &gt;99% et un pourcentage de couverture de 100%
</div>
</div>
<div id="multi-affiliation" class="section level3">
<h3>Multi affiliation</h3>
<p>Création fichiers <strong>multi_aff.tsv</strong> pour import <strong>affiliationExplorer</strong> et affiliation.tsv qui est la table d’abondance</p>
<pre class="bash"><code>qsub -cwd -V -N biom_to_tsv -o LOGS -e LOGS -b y &quot;conda activate frogs-3.2.2 &amp;&amp; biom_to_tsv.py --input-biom FROGS/affiliation.biom --input-fasta FROGS/filters.fasta --output-tsv FROGS/affiliation.tsv --output-multi-affi FROGS/multi_aff.tsv --log-file FROGS/biom_to_tsv.log  &amp;&amp; conda deactivate&quot;

Job 3634925
</code></pre>
<p>Je vais rectifier toutes les multis affiliations sur “affiliationExplorer”</p>
<ul>
<li>Lien pour accéder au module: <a href="https://shiny.migale.inrae.fr/app/affiliationexplorer">affiliationExplorer</a></li>
</ul>
<p>Je refais un <strong class="tool">affiliations_stats</strong> sur le biom nettoyé des multis affiliations</p>
<pre class="bash"><code>
#Affiliation stat pour comparer avant et après correction avec affiliationExplorer
qsub -cwd -V -N affiliations_stats_cleaned -o LOGS -e LOGS -b y &quot;conda activate frogs-3.2.2 &amp;&amp; affiliations_stat.py --input-biom cleaned_biom-2021-06-23.biom --output-file FROGS/affiliations_stats_cleaned.html --log-file FROGS/affiliations_stats_cleaned.log --multiple-tag blast_affiliations --tax-consensus-tag blast_taxonomy --identity-tag perc_identity --coverage-tag perc_query_coverage  &amp;&amp; conda deactivate&quot;

Job 3635034
</code></pre>
<p>Lien vers le <a href="FROGS/affiliations_stats_cleaned.html">rapport affiliations_stats_cleaned</a></p>
</div>
<div id="tree" class="section level3">
<h3>Tree</h3>
<p>Je crée l’arbre phylogénique avec <strong class="tool">tree</strong></p>
<pre class="bash"><code>
#Pour avoir aide sur outils tree.py
conda activate frogs-3.2.2 &amp;&amp; tree.py --help

#Creation arbre phylogenique
qsub -cwd -V -N tree -o LOGS -e LOGS -b y &quot;conda activate frogs-3.2.2 &amp;&amp; tree.py --input-sequences  FROGS/filters.fasta --biom-file cleaned_biom-2021-06-23.biom --out-tree FROGS/tree.nhx --html FROGS/tree.html --log-file FROGS/tree.log  &amp;&amp; conda deactivate&quot;

Job 3635036
</code></pre>
<p>Lien vers l’arbre <a href="FROGS/tree.nwk">Tree</a></p>
</div>
</div>
<div id="metagenomic-phyloseq-analysis" class="section level2">
<h2>Metagenomic phyloseq analysis</h2>
<div id="import-données" class="section level3">
<h3>Import données</h3>
<p>J’importe les données</p>
<pre class="r"><code>#si impossible de trouver la fonction &quot;read.tree&quot;
library(ape) 

 if(file.exists(&quot;frogs.data.m.rds&quot;)){
   frogs.data.m &lt;- readRDS(&quot;frogs.data.m.rds&quot;)
 }else{
  frogsBiom &lt;- &quot;cleaned_biom-2021-06-23.biom&quot;
  frogs.data.biom &lt;- import_frogs(frogsBiom, taxMethod = &quot;blast&quot;)
  
  metadata&lt;-read.table(&quot;metadataObegluM.txt&quot;,row.names=1, header=T)
  
  sample_data(frogs.data.biom)&lt;-metadata
  sample_names(frogs.data.biom) &lt;- get_variable(frogs.data.biom, &quot;Name&quot;)
  frogs.data.m&lt;-frogs.data.biom
  
  treefile&lt;- read.tree(&quot;FROGS/tree.nhx&quot;) 
  phy_tree(frogs.data.m) &lt;- treefile
  
  tax_table(frogs.data.m) &lt;- gsub(&quot;\&quot;&quot;, &quot;&quot;, tax_table(frogs.data.m))
  tax_table(frogs.data.m) &lt;- gsub(&quot;unknown.*&quot;, &quot;Unknown&quot;, tax_table(frogs.data.m))
  
  #sauvegarde de l&#39;objet forgs.data et .rds
  saveRDS(frogs.data.m, file = &quot;frogs.data.m.rds&quot;)
  # Chargement du fichier .rds dans l&#39;objet frogs.data
  #frogs.data &lt;- readRDS(&quot;frogs.data.rds&quot;)
}

# Sauvegarde des fichiers importés dans le fichier .RData
#save(frogs.data.biom, metadata, file = &quot;frogs.data.RData&quot;)
#save(frogs.data, file = &quot;frogs.data2.RData&quot;)
#chargement de l&#39;objet RData
#load(&quot;frogs.data.RData&quot;)</code></pre>
<p>Je dispose de l’object phyloseq suivant:</p>
<pre class="r"><code>frogs.data &lt;- frogs.data.m
frogs.data</code></pre>
<pre><code>## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 921 taxa and 48 samples ]
## sample_data() Sample Data:       [ 48 samples by 6 sample variables ]
## tax_table()   Taxonomy Table:    [ 921 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 921 tips and 920 internal nodes ]</code></pre>
<p>qui contient les métadonnées suivantes:</p>
<pre class="r"><code>sample_variables(frogs.data)</code></pre>
<pre><code>## [1] &quot;Name&quot;                 &quot;Number&quot;               &quot;Group&quot;               
## [4] &quot;reads_before_process&quot; &quot;pourc_kept&quot;           &quot;reads_final&quot;</code></pre>
<pre class="r"><code>table(get_variable(frogs.data, &quot;Group&quot;))</code></pre>
<pre><code>## 
##     HFD HFD_Gln      SD  SD_Gln 
##      12      12      12      12</code></pre>
</div>
<div id="visualisation-de-labondance-par-échantillon" class="section level3 tabset">
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<h3 class="tabset">Visualisation de l’abondance par échantillon</h3>
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<div id="données-brutes" class="section level4">
<h4>Données brutes</h4>
<pre class="r"><code>p&lt;-plot_bar(frogs.data)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/1-plotcomposition-1.png" width="1008" /></p>
</div>
<div id="niveau-phylum" class="section level4">
<h4>Niveau Phylum</h4>
<pre class="r"><code>p &lt;- plot_bar(frogs.data, fill = &quot;Phylum&quot;)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/1bis-plotcomposition-1.png" width="1008" /></p>
</div>
<div id="niveau-family" class="section level4">
<h4>Niveau Family</h4>
<pre class="r"><code>p &lt;- plot_bar(frogs.data, fill = &quot;Family&quot;)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/2-plotcomposition-1.png" width="1008" /></p>
</div>
</div>
<div id="filtres" class="section level3">
<h3>Filtres</h3>
<p>On va commencer par filtrer les OTUs sur la base de leur prévalence: on ne garde que les OTUs qui sont présents dans au moins 50% des échantillons d’au moins un groupe.</p>
<p>Nombre d’OTUs qui passent les filtres:</p>
<pre class="r"><code>library(VennDiagram)
venn.plot &lt;- venn.diagram(list(&quot;HFD&quot;  = which(taxa.1),
                               &quot;HFD_Gln&quot; = which(taxa.2),&quot;SD&quot; = which(taxa.3),&quot;SD_Gln&quot; = which(taxa.4)),
                        filename = NULL, fill = c(&quot;#FE642E&quot;, &quot;#F7D358&quot;, &quot;#5FB404&quot;, &quot;#00BFFF&quot;), lty = &quot;blank&quot;,cex = 1.5, cat.cex = 1.5, cat.col = c(&quot;#FE642E&quot;, &quot;#F7D358&quot;, &quot;#5FB404&quot;, &quot;#00BFFF&quot;))  #Place des noms: , cat.pos=c(-20,20,180)
grid.newpage(); grid.draw(venn.plot)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/4-venn-filter-1.png" width="768" /></p>
</div>
<div id="composition-des-communautés" class="section level3">
<h3>Composition des communautés</h3>
<div id="représentation-des-communautés-au-niveau-phylum" class="section level4 tabset">
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<h4 class="tabset">Représentation des communautés au niveau Phylum</h4>
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<div id="tableaux-relatives-abondances" class="section level5">
<h5>Tableaux relatives abondances</h5>
<pre class="r"><code>#Get count of phyla
table&lt;-table(phyloseq::tax_table(frogs.data)[, &quot;Phylum&quot;])
table</code></pre>
<pre><code>## 
## Actinobacteriota     Bacteroidota Desulfobacterota       Firmicutes 
##               14              121                7              775 
##   Proteobacteria 
##                4</code></pre>
<p>Relative Abondance par échantillon</p>
<pre class="r"><code>#Convert to relative abundance
data_rel_abund = phyloseq::transform_sample_counts(frogs.data, function(x) 100 * x/sum(x)) 

#Agglomerate to phylum-level and rename
data_phylum &lt;- phyloseq::tax_glom(data_rel_abund, &quot;Phylum&quot;)

phyloseq::taxa_names(data_phylum) &lt;- phyloseq::tax_table(data_phylum)[, &quot;Phylum&quot;]
rel_abun&lt;-phyloseq::otu_table(data_phylum)

rel_abun %&gt;%
 DT::datatable(extensions = &#39;Buttons&#39;, 
                                        options = list(dom = &#39;Bfrtip&#39;, 
                                                       pageLength = 200,
                                                       buttons = list(&#39;copy&#39;, &#39;csv&#39;, &#39;excel&#39;))) </code></pre>
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<div id="htmlwidget-c4e9d042d61c4add3787" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" 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<p>Relative abondance par groupe</p>
<pre class="r"><code>#Convert to relative abundance
data_rel_abund = phyloseq::transform_sample_counts(frogs.data, function(x) 100 * x/sum(x))

#Agglomerate to phylum-level and rename
data_phylum &lt;- phyloseq::tax_glom(data_rel_abund, &quot;Phylum&quot;)

ps1 &lt;- merge_samples(data_phylum, &quot;Group&quot;)
data_phylum_2 &lt;- transform_sample_counts(ps1, function(x) 100 * x/sum(x))

phyloseq::taxa_names(data_phylum_2) &lt;- phyloseq::tax_table(data_phylum_2)[, &quot;Phylum&quot;]
rel_abun2&lt;-phyloseq::otu_table(data_phylum_2)
rel_abun2 %&gt;%
 DT::datatable(extensions = &#39;Buttons&#39;, 
                                        options = list(dom = &#39;Bfrtip&#39;, 
                                                       pageLength = 200,
                                                       buttons = list(&#39;copy&#39;, &#39;csv&#39;, &#39;excel&#39;)))</code></pre>
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<script type="application/json" data-for="htmlwidget-817b437edf47c36990c9">{"x":{"filter":"none","extensions":["Buttons"],"data":[["HFD","HFD_Gln","SD","SD_Gln"],[16.0325894508914,14.2871288258499,37.3816426895291,34.8421501112769],[63.8272230540045,63.265488627667,59.3633982184125,62.8081836454644],[19.2402609293187,20.8434895028875,2.68435616772057,1.44700528776808],[0.0509419850201465,0.0592551620839513,0.202705863586771,0.282332938145493],[0.848984580765226,1.54463788151166,0.367897060751017,0.62032801734516]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Bacteroidota<\/th>\n      <th>Firmicutes<\/th>\n      <th>Desulfobacterota<\/th>\n      <th>Proteobacteria<\/th>\n      <th>Actinobacteriota<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":200,"scrollX":true,"language":{"search":"Filter:"},"dom":"Bfrtip","buttons":["copy","csv","excel"],"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[10,25,50,100,200]}},"evals":[],"jsHooks":[]}</script>
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</div>
<div id="boxplot-phylum-avec-stats" class="section level5">
<h5>Boxplot Phylum avec stats</h5>
<p>On va se focaliser sur les 5 Phylum et représenter leurs abondances dans les différents groupe sous forme de boxplot. On va utiliser CTRL comme groupe de référence et le comparer à tout les autres avec un test de wilcoxon pour voir desquels il diffère. Le nombre d’étoiles codent le niveau de significativité, les comparaisons non-significatives ne sont pas indiqués</p>
<pre class="r"><code>library(ggpubr)
## Select only some families  
families &lt;- c(&quot;Actinobacteriota&quot;,&quot;Bacteroidota&quot;,&quot;Desulfobacterota&quot; ,&quot;Firmicutes&quot;, &quot;Proteobacteria&quot;)
phy &lt;- frogs.data %&gt;% subset_taxa(Phylum %in% families) %&gt;% tax_glom(taxrank=&quot;Phylum&quot;) #agglomerate at family level
 
 ## Transform count to relative abundances 
depth &lt;- sample_sums(frogs.data)[1] 
plotdata&lt;-psmelt(phy) %&gt;% 
  mutate(Abundance  = Abundance / depth, 
         Group = factor(Group, labels = c(&quot;HFD&quot;, &quot;HFD_Gln&quot;,&quot;SD&quot;,&quot;SD_Gln&quot;)))  
  
p &lt;- ggplot(plotdata,aes(x = Group,y=Abundance, color = Group, Group = Group)) + 
  stat_boxplot(geom = &quot;errorbar&quot;, width = 0.5) + 
  geom_boxplot(outlier.alpha = 1, 
               outlier.size = 0.8) + 
  facet_wrap(~Phylum, scales = &quot;free_y&quot;, ncol = 5) + 
  scale_color_manual(values = c(&quot;HFD&quot; = &quot;#FE642E&quot;,&quot;HFD_Gln&quot; = &quot;#F7D358&quot;,   
                                &quot;SD&quot; = &quot;#5FB404&quot;,&quot;SD_Gln&quot; = &quot;#00BFFF&quot;),
                     guide = &quot;none&quot;) + 
  
  # theme_classic() + ## fond blanc
  labs(x = NULL) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))

## Compare reference level T0 to all other levels using a wilcoxon test and adjust p-values using the holm correction. 
p &lt;- p + stat_compare_means(aes(label = ..p.signif..), method = &quot;wilcox.test&quot;, p.adjust.method = &quot;holm&quot;, ref.group = &quot;HFD&quot;, hide.ns = T, 
                       label.y.npc = c(0.90),
                       size = 7, 
                       fontface = &quot;bold&quot;)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/Phylum%20stats-1.png" width="960" /></p>
<div class="alert comment">
Le test wilcon compare HFD aux autres groupes
</div>
</div>
<div id="plot-composition-phylum" class="section level5">
<h5>Plot composition Phylum</h5>
<pre class="r"><code>correct.order &lt;- c(&quot;HFD&quot;, &quot;HFD_Gln&quot;,&quot;SD&quot;,&quot;SD_Gln&quot;)
sample_data(frogs.data)$Group &lt;- factor(sample_data(frogs.data)$Group,
levels = correct.order)

p &lt;- plot_composition(frogs.data, &quot;Kingdom&quot;, &quot;Bacteria&quot;, &quot;Phylum&quot;,numberOfTaxa = 6, fill = &quot;Phylum&quot;)
p&lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1) +
    theme(strip.text.x = element_text(size = 14, color = &quot;black&quot;))  +
      scale_y_continuous(label = scales::percent)

plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/7-composition-phylum-1.png" width="1008" /></p>
<pre class="r"><code>frogs.data.merged&lt;-merge_samples(frogs.data,group=&quot;Group&quot;)
sample_data(frogs.data.merged)$Group &lt;- sample_names(frogs.data.merged)

correct.order &lt;- c(&quot;HFD&quot;, &quot;HFD_Gln&quot;,&quot;SD&quot;,&quot;SD_Gln&quot;)
sample_data(frogs.data.merged)$Group &lt;- factor(sample_data(frogs.data.merged)$Group,
levels = correct.order)

p &lt;- plot_composition(frogs.data.merged, &quot;Kingdom&quot;, &quot;Bacteria&quot;, &quot;Phylum&quot;,numberOfTaxa = 6, fill = &quot;Phylum&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)
p &lt;- p + ggtitle(&quot;Phylum Composition (12 top Phylum)&quot;)+
      scale_y_continuous(label = scales::percent)  ## Changer le titre 

plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/11bis-composition-phylum-merged-1.png" width="1008" /></p>
</div>
<div id="stats-phylum" class="section level5">
<h5>Stats Phylum</h5>
<p>Test de Kruskal (Anova non paramétrique) pour chaque Phylum pour tester si les abondances sont similaires (pour ce Phylum) entre les différents groupes.</p>
<pre class="r"><code># depth &lt;- sample_sums(frogs.data)[1] 
data.phylum &lt;- frogs.data %&gt;% 
  transform_sample_counts(function(x) { x / sum(x)}) %&gt;% ## transform counts to proportions
  fast_tax_glom(taxrank = &quot;Phylum&quot;) %&gt;% 
  psmelt() %&gt;% 
  mutate(Group = factor(Group, labels = c(&quot;HFD&quot;, &quot;HFD_Gln&quot;,&quot;SD&quot;,&quot;SD_Gln&quot;)))</code></pre>
<pre class="r"><code>data.test &lt;- compare_means(Abundance ~ Group, data = data.phylum, method = &quot;kruskal&quot;, group.by = &quot;Phylum&quot;)
data.test %&gt;%
 DT::datatable(extensions = &#39;Buttons&#39;, 
                                        options = list(dom = &#39;Bfrtip&#39;, 
                                                       pageLength = 10,
                                                       buttons = list(&#39;copy&#39;, &#39;csv&#39;, &#39;excel&#39;)))</code></pre>
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<script type="application/json" data-for="htmlwidget-90409630c7d94016e710">{"x":{"filter":"none","extensions":["Buttons"],"data":[["1","2","3","4","5"],["Firmicutes","Bacteroidota","Desulfobacterota","Actinobacteriota","Proteobacteria"],["Abundance","Abundance","Abundance","Abundance","Abundance"],[0.334125264149217,3.24652137090235e-06,2.19050146447033e-07,0.395310722960241,0.00011868942995511],[0.67,1.3e-05,1.1e-06,0.67,0.00036],["0.33413","3.2e-06","2.2e-07","0.39531","0.00012"],["ns","****","****","ns","***"],["Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Phylum<\/th>\n      <th>.y.<\/th>\n      <th>p<\/th>\n      <th>p.adj<\/th>\n      <th>p.format<\/th>\n      <th>p.signif<\/th>\n      <th>method<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":10,"scrollX":true,"language":{"search":"Filter:"},"dom":"Bfrtip","buttons":["copy","csv","excel"],"columnDefs":[{"className":"dt-right","targets":[3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
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</div>
</div>
<div id="représentation-des-communautés-au-niveau-family" class="section level4 tabset">
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<h4 class="tabset">Représentation des communautés au niveau Family</h4>
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<div id="plot-composition-family" class="section level5">
<h5>Plot composition Family</h5>
<pre class="r"><code>correct.order &lt;- c(&quot;HFD&quot;, &quot;HFD_Gln&quot;,&quot;SD&quot;,&quot;SD_Gln&quot;)
sample_data(frogs.data)$Group &lt;- factor(sample_data(frogs.data)$Group,
levels = correct.order)

p &lt;- plot_composition(frogs.data, &quot;Kingdom&quot;, &quot;Bacteria&quot;, &quot;Family&quot;,numberOfTaxa = 12, fill = &quot;Family&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)

plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/11-composition-phylum-1.png" width="1008" /></p>
<pre class="r"><code>frogs.data.merged&lt;-merge_samples(frogs.data,group=&quot;Group&quot;)
sample_data(frogs.data.merged)$Group &lt;- sample_names(frogs.data.merged)

correct.order &lt;- c(&quot;HFD&quot;, &quot;HFD_Gln&quot;,&quot;SD&quot;,&quot;SD_Gln&quot;)
sample_data(frogs.data.merged)$Group &lt;- factor(sample_data(frogs.data.merged)$Group,
levels = correct.order)

p &lt;- plot_composition(frogs.data.merged, &quot;Kingdom&quot;, &quot;Bacteria&quot;, &quot;Family&quot;,numberOfTaxa = 12, fill = &quot;Family&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)
p &lt;- p + ggtitle(&quot;Family Composition (12 top Family)&quot;)+
      scale_y_continuous(label = scales::percent)  ## Changer le titre 

plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/12bis-composition-phylum-merged-1.png" width="1008" /></p>
</div>
<div id="stats-family" class="section level5">
<h5>Stats Family</h5>
<p>Je fais cette fois le test de Kruskal (Anova non paramétrique) pour chaque Family pour tester si les abondances sont similaires (pour cette famille) entre les différents groupes.</p>
<pre class="r"><code># depth &lt;- sample_sums(frogs.data)[1] 
data.phylum &lt;- frogs.data %&gt;% 
  transform_sample_counts(function(x) { x / sum(x)}) %&gt;% ## transform counts to proportions
  fast_tax_glom(taxrank = &quot;Family&quot;) %&gt;% 
  psmelt() %&gt;% 
  mutate(Group = factor(Group, labels = c(&quot;HFD&quot;, &quot;HFD_Gln&quot;,&quot;SD&quot;,&quot;SD_Gln&quot;)))</code></pre>
<pre class="r"><code>data.test &lt;- compare_means(Abundance ~ Group, data = data.phylum, method = &quot;kruskal&quot;, group.by = &quot;Family&quot;)
data.test %&gt;%
 DT::datatable(extensions = &#39;Buttons&#39;, 
                                        options = list(dom = &#39;Bfrtip&#39;, 
                                                       pageLength = 20,
                                                       buttons = list(&#39;copy&#39;, &#39;csv&#39;, &#39;excel&#39;)))</code></pre>
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<script type="application/json" data-for="htmlwidget-9ebd7a953821a33f9308">{"x":{"filter":"none","extensions":["Buttons"],"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29"],["Lachnospiraceae","Muribaculaceae","Desulfovibrionaceae","Oscillospiraceae","Rikenellaceae","Prevotellaceae","Atopobiaceae","Bacteroidaceae","Tannerellaceae","Ruminococcaceae","Lactobacillaceae","Marinifilaceae","Butyricicoccaceae","Peptostreptococcaceae","Bifidobacteriaceae","Unknown","Sutterellaceae","Streptococcaceae","Eggerthellaceae","[Eubacterium] coprostanoligenes group","Monoglobaceae","Peptococcaceae","Acholeplasmataceae","Erysipelotrichaceae","Anaerovoracaceae","Christensenellaceae","Clostridiaceae","Enterobacteriaceae","UCG-010"],["Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance","Abundance"],[0.0969411437522631,4.15004099728181e-07,2.19050146447033e-07,3.68022897705655e-07,1.58289310021045e-06,0.141514668235106,0.305608124125293,0.0260845807256609,0.0107308213630201,0.00857953451375149,0.43080236077997,0.0329327881635881,3.83378024957135e-07,1.39473812813757e-06,0.0194220314128246,7.14032239749834e-10,1.45279859887059e-05,5.79135119300485e-07,0.0448831030011406,0.685020656352322,1.17306014266563e-05,0.551235775479878,6.69000661103482e-07,0.00522774331881675,0.00155160966561651,1.76507439865408e-06,0.00718851448125528,0.000152075766578479,0.000269828999763917],[0.58,1e-05,6.1e-06,9.9e-06,3.3e-05,0.71,1,0.23,0.12,0.1,1,0.26,1e-05,3.1e-05,0.19,2.1e-08,0.00026,1.4e-05,0.31,1,0.00022,1,1.5e-05,0.073,0.023,3.5e-05,0.093,0.0026,0.0043],["0.09694","4.2e-07","2.2e-07","3.7e-07","1.6e-06","0.14151","0.30561","0.02608","0.01073","0.00858","0.43080","0.03293","3.8e-07","1.4e-06","0.01942","7.1e-10","1.5e-05","5.8e-07","0.04488","0.68502","1.2e-05","0.55124","6.7e-07","0.00523","0.00155","1.8e-06","0.00719","0.00015","0.00027"],["ns","****","****","****","****","ns","ns","*","*","**","ns","*","****","****","*","****","****","****","*","ns","****","ns","****","**","**","****","**","***","***"],["Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis","Kruskal-Wallis"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Family<\/th>\n      <th>.y.<\/th>\n      <th>p<\/th>\n      <th>p.adj<\/th>\n      <th>p.format<\/th>\n      <th>p.signif<\/th>\n      <th>method<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":20,"scrollX":true,"language":{"search":"Filter:"},"dom":"Bfrtip","buttons":["copy","csv","excel"],"columnDefs":[{"className":"dt-right","targets":[3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[10,20,25,50,100]}},"evals":[],"jsHooks":[]}</script>
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<p>Les 5 phylums d’intérêts sont les Actinobacteriota, Bacteroidota, les Desulfobacterota, les Firmicutes et les Proteobacteria.</p>
</div>
</div>
<div id="zoom-au-niveau-famille-dans-chacun-des-phylums" class="section level4 tabset">
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<h4 class="tabset">Zoom au niveau Famille dans chacun des phylums</h4>
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<div id="actinobacteriota" class="section level5">
<h5>Actinobacteriota</h5>
<pre class="r"><code>p &lt;- plot_composition(frogs.data, &quot;Phylum&quot;, &quot;Actinobacteriota&quot;, &quot;Family&quot;,numberOfTaxa = 12, fill = &quot;Family&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/13-composition-bacteroidetes-1.png" width="1008" /></p>
</div>
<div id="bacteroidota" class="section level5">
<h5>Bacteroidota</h5>
<pre class="r"><code>p &lt;- plot_composition(frogs.data, &quot;Phylum&quot;, &quot;Bacteroidota&quot;, &quot;Family&quot;,numberOfTaxa = 6, fill = &quot;Family&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/16-composition-Bacteroidota-1.png" width="1008" /></p>
</div>
<div id="desulfobacterota" class="section level5">
<h5>Desulfobacterota</h5>
<pre class="r"><code>p &lt;- plot_composition(frogs.data, &quot;Phylum&quot;, &quot;Desulfobacterota&quot;, &quot;Family&quot;,numberOfTaxa = 6, fill = &quot;Family&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)

plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/20-composition-proteobacteria-1.png" width="1008" /></p>
</div>
<div id="firmicutes" class="section level5">
<h5>Firmicutes</h5>
<pre class="r"><code>p &lt;- plot_composition(frogs.data, &quot;Phylum&quot;, &quot;Firmicutes&quot;, &quot;Family&quot;,numberOfTaxa = 12, fill = &quot;Family&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)

plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/22-composition-proteobacteria-1.png" width="1008" /></p>
</div>
<div id="proteobacteria" class="section level5">
<h5>Proteobacteria</h5>
<pre class="r"><code>p &lt;- plot_composition(frogs.data, &quot;Phylum&quot;, &quot;Proteobacteria&quot;, &quot;Family&quot;,numberOfTaxa = 6, fill = &quot;Family&quot;)
p &lt;- p + facet_wrap(~Group, scales = &quot;free_x&quot;, nrow = 1)

plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/24-composition-bacteroidetes-1.png" width="1008" /></p>
</div>
</div>
</div>
<div id="diversité-alpha" class="section level3">
<h3>Diversité <span class="math inline">\(\alpha\)</span></h3>
<div id="courbes-de-raréfaction" class="section level4">
<h4>Courbes de raréfaction</h4>
<p>Comme ces distances sont sensibles à la profondeur d’échantillonnage, on va raréfier les échantillons avant de calculer les distances. Normalisation par rarefaction: même nombre de séquences pour chaque échantillon.</p>
<pre class="r"><code>frogs.data.rare&lt;-rarefy_even_depth(frogs.data,rngseed=20170329)
sample_sums(frogs.data.rare)[1:5]</code></pre>
<pre><code>##  C3S2  C3S3  C3S4  C4S1  C4S2 
## 25270 25270 25270 25270 25270</code></pre>
<p>Avant de calculer les diversités <span class="math inline">\(\alpha\)</span>, on va faire des courbes de raréfaction pour vérifier si on a saturé la richesse sous-dominante (i.e. celle qui passe les filtres d’abondances).</p>
<p>Infos Richesse: nombre d’OTUs par échantillon représenté par les courbes de raréfaction</p>
<p>Principe courbes de rarefaction : Compter le nombre d’OTU pour un ensemble de sous-échantillon à différents intervalles de profondeur (x= nombre de séquences et y= nombre d’OTU)</p>
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<pre class="r"><code>if(file.exists(&quot;frogs.data.courbes.rare_M.rds&quot;)){
   p &lt;- readRDS(&quot;frogs.data.courbes.rare_M.rds&quot;)
 }else{
  p &lt;- ggrare(frogs.data, step = 100, color = &quot;Group&quot;, plot = FALSE)

    saveRDS(p, file = &quot;frogs.data.courbes.rare_M.rds&quot;)
  }

rare.level &lt;- min(sample_sums(frogs.data))


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p &lt;- p + facet_wrap(~Group) + geom_vline(xintercept = rare.level, color = &quot;Gray60&quot;)+
  xlab(&quot;Sample Size (nb de séquences)&quot;) + ylab(&quot;Species Richness (nb d&#39;OTU)&quot;)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/ggrare-1.png" width="1008" /></p>
</div>
<div id="effet-du-régime-sur-la-diversité" class="section level4">
<h4>Effet du régime sur la diversité</h4>
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<pre class="r"><code>p &lt;- plot_richness(frogs.data, color = &quot;Group&quot;,measures = c(&quot;Observed&quot;, &quot;Chao1&quot;,  &quot;Shannon&quot;, &quot;InvSimpson&quot;, &quot;Fisher&quot;),x = &quot;Group&quot;, title = &quot;Alpha diversity Comparaison microbiote&quot;) +theme_bw() + geom_boxplot(aes(fill = Group)) +  geom_point() + theme(axis.text.x = element_blank()) 
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plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/30-plot-richness-1.png" width="1008" /></p>
</div>
<div id="richness-table" class="section level4">
<h4>Richness Table</h4>
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<pre class="r"><code>richness.table &lt;- estimate_richness(frogs.data, measures = c(&quot;Observed&quot;, &quot;Chao1&quot;, &quot;Shannon&quot;, &quot;Simpson&quot;, &quot;InvSimpson&quot;, &quot;Fisher&quot;))
richness.table &lt;- cbind(richness.table, sample_data(frogs.data))
richness.table$Depth &lt;- sample_sums(frogs.data)
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richness.table %&gt;% DT::datatable(extensions = &#39;Buttons&#39;, 
                                        options = list(dom = &#39;Bfrtip&#39;, 
                                                       pageLength = 10,
                                                       buttons = list(&#39;copy&#39;, &#39;csv&#39;, &#39;excel&#39;)))</code></pre>
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<div id="htmlwidget-11d7d0fe3ee2a0886f6a" style="width:100%;height:auto;" class="datatables html-widget"></div>
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class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Observed<\/th>\n      <th>Chao1<\/th>\n      <th>se.chao1<\/th>\n      <th>Shannon<\/th>\n      <th>Simpson<\/th>\n      <th>InvSimpson<\/th>\n      <th>Fisher<\/th>\n      <th>Name<\/th>\n      <th>Number<\/th>\n      <th>Group<\/th>\n      <th>reads_before_process<\/th>\n      <th>pourc_kept<\/th>\n      <th>reads_final<\/th>\n      <th>Depth<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":10,"scrollX":true,"language":{"search":"Filter:"},"dom":"Bfrtip","buttons":["copy","csv","excel"],"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6,7,9,11,12,13,14]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
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</div>
<div id="statistiques-sur-la-diversité" class="section level4 tabset">
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<h4 class="tabset">Statistiques sur la diversité</h4>
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<div id="statistiques-sur-richness-observed" class="section level5">
<h5>Statistiques sur Richness Observed</h5>
<ul>
<li>Anova sur la Richness Observed</li>
</ul>
<p>Analyse de la variance, compare les moyennes d’échantillons.</p>
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<pre class="r"><code>div_data &lt;- cbind(estimate_richness(frogs.data, measures = &quot;Observed&quot;),  
                  sample_data(frogs.data))
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model &lt;- aov(Observed ~ 0 + Group, data = div_data)
anova(model)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Df"],"name":[1],"type":["int"],"align":["right"]},{"label":["Sum Sq"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Mean Sq"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["F value"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>F)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"4","2":"11035400.8","3":"2758850.188","4":"868.8903","5":"3.088264e-41","_rn_":"Group"},{"1":"44","2":"139706.2","3":"3175.142","4":"NA","5":"NA","_rn_":"Residuals"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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  </script>
</div>
<p>Le <code>~ 0 +</code>, ça permet d’interpréter les coefficients comme richesse moyenne de la modalité plutôt que comme écart moyen de richesse par rapport à une modalité de référence</p>
<p>Df: nombre de degrés de liberté Sum Sq: inertie inter et intra respectivement Mean Sq: c’est la variance, obtenue en faisant le quotient de la 2eme colonne par la 1ere (moyenne des inerties) F value: obtenue en faisant quotient des 2 valeurs trouvées dans la 3eme colonne Pr(&gt;F): pvalue</p>
<ul>
<li>Coefficient Richness Observed</li>
</ul>
<p>coef function: permet d’extraire que les coefficients estimés, mesure l’écart des groupes à la moyenne. Groupes équilibrés si les valeurs sont du même ordre de grandeur.</p>
<pre class="r"><code>coef(model)</code></pre>
<pre><code>##     GroupHFD GroupHFD_Gln      GroupSD  GroupSD_Gln 
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##     391.9167     419.3333     550.8333     535.5000</code></pre>
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<ul>
<li>Test de comparaisons multiples sur Richness Observed</li>
</ul>
<p>Utilisé pour déterminer les différences significatives entre les moyennes de groupes dans une analyse de variance. Pour étudier les différences inter-groupes, permet de distinguer parmi les éechantillons s’il y en a qui différent significativement des autres.</p>
<pre class="r"><code>TukeyHSD(model)     </code></pre>
<pre><code>##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Observed ~ 0 + Group, data = div_data)
## 
## $Group
##                     diff       lwr       upr     p adj
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## HFD_Gln-HFD     27.41667 -34.00451  88.83784 0.6351797
## SD-HFD         158.91667  97.49549 220.33784 0.0000001
## SD_Gln-HFD     143.58333  82.16216 205.00451 0.0000009
## SD-HFD_Gln     131.50000  70.07882 192.92118 0.0000051
## SD_Gln-HFD_Gln 116.16667  54.74549 177.58784 0.0000472
## SD_Gln-SD      -15.33333 -76.75451  46.08784 0.9090906</code></pre>
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<p>diff: différences entre les moyennes obséervées lwr et upr: donnent les bornes inférieure et supérieur de l’intervalle p adj: pvalue après ajustement pour les comparaisons multiples</p>
<pre class="r"><code>comparison_data &lt;- compare_means(
  Observed ~ Group,
  data = div_data, 
  method = &quot;wilcox.test&quot;
  ) %&gt;% filter(p.adj &lt;= 0.05)
comparison_data</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
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{"columns":[{"label":[".y."],"name":[1],"type":["chr"],"align":["left"]},{"label":["group1"],"name":[2],"type":["chr"],"align":["left"]},{"label":["group2"],"name":[3],"type":["chr"],"align":["left"]},{"label":["p"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["p.adj"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["p.format"],"name":[6],"type":["chr"],"align":["left"]},{"label":["p.signif"],"name":[7],"type":["chr"],"align":["left"]},{"label":["method"],"name":[8],"type":["chr"],"align":["left"]}],"data":[{"1":"Observed","2":"HFD","3":"SD","4":"2.958409e-06","5":"1.8e-05","6":"3e-06","7":"****","8":"Wilcoxon"},{"1":"Observed","2":"HFD","3":"SD_Gln","4":"6.565530e-04","5":"2.6e-03","6":"0.00066","7":"***","8":"Wilcoxon"},{"1":"Observed","2":"HFD_Gln","3":"SD","4":"4.763248e-04","5":"2.4e-03","6":"0.00048","7":"***","8":"Wilcoxon"},{"1":"Observed","2":"HFD_Gln","3":"SD_Gln","4":"4.256420e-03","5":"1.3e-02","6":"0.00426","7":"**","8":"Wilcoxon"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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  </script>
</div>
</div>
<div id="statistiques-sur-richness-chao1" class="section level5">
<h5>Statistiques sur Richness Chao1</h5>
<ul>
<li>Anova sur la Richness Chao1</li>
</ul>
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<pre class="r"><code>div_data &lt;- cbind(estimate_richness(frogs.data, measures = &quot;Chao1&quot;),  
                  sample_data(frogs.data))
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model &lt;- aov(Chao1 ~ 0 + Group, data = div_data)
anova(model)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Df"],"name":[1],"type":["int"],"align":["right"]},{"label":["Sum Sq"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Mean Sq"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["F value"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>F)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"4","2":"13618402.2","3":"3404600.561","4":"1045.971","5":"5.478115e-43","_rn_":"Group"},{"1":"44","2":"143218.5","3":"3254.965","4":"NA","5":"NA","_rn_":"Residuals"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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  </script>
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<ul>
<li>Coefficient Chao1</li>
</ul>
<pre class="r"><code>coef(model)</code></pre>
<pre><code>##     GroupHFD GroupHFD_Gln      GroupSD  GroupSD_Gln 
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##     447.5513     476.8925     604.6118     584.4509</code></pre>
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<ul>
<li>Test de comparaisons multiples sur Richness Chao1</li>
</ul>
<pre class="r"><code>TukeyHSD(model)     </code></pre>
<pre><code>##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Chao1 ~ 0 + Group, data = div_data)
## 
## $Group
##                     diff       lwr       upr     p adj
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## HFD_Gln-HFD     29.34122 -32.84723  91.52967 0.5929288
## SD-HFD         157.06048  94.87202 219.24893 0.0000002
## SD_Gln-HFD     136.89959  74.71114 199.08804 0.0000030
## SD-HFD_Gln     127.71925  65.53080 189.90770 0.0000112
## SD_Gln-HFD_Gln 107.55836  45.36991 169.74682 0.0001919
## SD_Gln-SD      -20.16089 -82.34934  42.02756 0.8224624</code></pre>
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<pre class="r"><code># comparison_data &lt;- compare_means(
#   Chao1 ~ Group,
#   data = div_data, 
#   method = &quot;wilcox.test&quot;
#   ) %&gt;% filter(p.adj &lt;= 0.05)
# comparison_data</code></pre>
</div>
<div id="statistiques-sur-richness-shannon" class="section level5">
<h5>Statistiques sur Richness Shannon</h5>
<ul>
<li>Anova sur la Richness Shannon</li>
</ul>
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<pre class="r"><code>div_data &lt;- cbind(estimate_richness(frogs.data, measures = &quot;Shannon&quot;),  
                  sample_data(frogs.data))
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model &lt;- aov(Shannon ~ 0 + Group, data = div_data)
anova(model)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Df"],"name":[1],"type":["int"],"align":["right"]},{"label":["Sum Sq"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Mean Sq"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["F value"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>F)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"4","2":"779.436945","3":"194.8592363","4":"917.4061","5":"9.488144e-42","_rn_":"Group"},{"1":"44","2":"9.345705","3":"0.2124024","4":"NA","5":"NA","_rn_":"Residuals"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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  </script>
</div>
<ul>
<li>Coefficient Shannon</li>
</ul>
<pre class="r"><code>coef(model)</code></pre>
<pre><code>##     GroupHFD GroupHFD_Gln      GroupSD  GroupSD_Gln 
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##     3.727687     3.821894     4.369194     4.166618</code></pre>
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<ul>
<li>Test de comparaisons multiples sur Richness Shannon</li>
</ul>
<pre class="r"><code>TukeyHSD(model)     </code></pre>
<pre><code>##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Shannon ~ 0 + Group, data = div_data)
## 
## $Group
##                       diff         lwr       upr     p adj
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## HFD_Gln-HFD     0.09420716 -0.40815422 0.5965685 0.9584756
## SD-HFD          0.64150668  0.13914530 1.1438681 0.0073961
## SD_Gln-HFD      0.43893086 -0.06343052 0.9412922 0.1060332
## SD-HFD_Gln      0.54729952  0.04493814 1.0496609 0.0279571
## SD_Gln-HFD_Gln  0.34472369 -0.15763769 0.8470851 0.2723000
## SD_Gln-SD      -0.20257582 -0.70493720 0.2997856 0.7053949</code></pre>
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<pre class="r"><code># comparison_data &lt;- compare_means(
#   Shannon ~ Group,
#   data = div_data, 
#   method = &quot;wilcox.test&quot;
#   ) %&gt;% filter(p.adj &lt;= 0.05)
# comparison_data</code></pre>
</div>
<div id="statistiques-sur-richness-invsimpson" class="section level5">
<h5>Statistiques sur Richness InvSimpson</h5>
<ul>
<li>Anova sur la Richness InvSimpson</li>
</ul>
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<pre class="r"><code>div_data &lt;- cbind(estimate_richness(frogs.data, measures = &quot;InvSimpson&quot;),  
                  sample_data(frogs.data))
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model &lt;- aov(InvSimpson ~ 0 + Group, data = div_data)
anova(model)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Df"],"name":[1],"type":["int"],"align":["right"]},{"label":["Sum Sq"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Mean Sq"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["F value"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>F)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"4","2":"22727.342","3":"5681.8356","4":"41.90219","5":"1.818186e-14","_rn_":"Group"},{"1":"44","2":"5966.293","3":"135.5976","4":"NA","5":"NA","_rn_":"Residuals"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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  </script>
</div>
<ul>
<li>Coefficient InvSimpson</li>
</ul>
<pre class="r"><code>coef(model)</code></pre>
<pre><code>##     GroupHFD GroupHFD_Gln      GroupSD  GroupSD_Gln 
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##     14.86480     16.76084     24.58072     28.06858</code></pre>
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<ul>
<li>Test de comparaisons multiples sur Richness InvSimpson</li>
</ul>
<pre class="r"><code>TukeyHSD(model)     </code></pre>
<pre><code>##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = InvSimpson ~ 0 + Group, data = div_data)
## 
## $Group
##                     diff         lwr      upr     p adj
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## HFD_Gln-HFD     1.896040 -10.7969083 14.58899 0.9782454
## SD-HFD          9.715925  -2.9770237 22.40887 0.1878620
## SD_Gln-HFD     13.203785   0.5108366 25.89673 0.0386731
## SD-HFD_Gln      7.819885  -4.8730642 20.51283 0.3647238
## SD_Gln-HFD_Gln 11.307745  -1.3852038 24.00069 0.0962410
## SD_Gln-SD       3.487860  -9.2050883 16.18081 0.8830155</code></pre>
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<pre class="r"><code># comparison_data &lt;- compare_means(
#   InvSimpson ~ Group,
#   data = div_data, 
#   method = &quot;wilcox.test&quot;
#   ) %&gt;% filter(p.adj &lt;= 0.05)
# comparison_data</code></pre>
</div>
<div id="statistiques-sur-richness-fisher" class="section level5">
<h5>Statistiques sur Richness Fisher</h5>
<ul>
<li>Anova sur la Richness Fisher</li>
</ul>
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<pre class="r"><code>div_data &lt;- cbind(estimate_richness(frogs.data, measures = &quot;Fisher&quot;),  
                  sample_data(frogs.data))
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model &lt;- aov(Fisher ~ 0 + Group, data = div_data)
anova(model)</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
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{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Df"],"name":[1],"type":["int"],"align":["right"]},{"label":["Sum Sq"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Mean Sq"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["F value"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>F)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"4","2":"253795.631","3":"63448.9078","4":"615.6138","5":"5.382869e-38","_rn_":"Group"},{"1":"44","2":"4534.908","3":"103.0661","4":"NA","5":"NA","_rn_":"Residuals"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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  </script>
</div>
<ul>
<li>Coefficient Fisher</li>
</ul>
<pre class="r"><code>coef(model)</code></pre>
<pre><code>##     GroupHFD GroupHFD_Gln      GroupSD  GroupSD_Gln 
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##     56.38887     60.15067     86.83835     82.52836</code></pre>
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<ul>
<li>Test de comparaisons multiples sur Richness Fisher</li>
</ul>
<pre class="r"><code>TukeyHSD(model)     </code></pre>
<pre><code>##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Fisher ~ 0 + Group, data = div_data)
## 
## $Group
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##                    diff        lwr       upr     p adj
## HFD_Gln-HFD     3.76180  -7.304297 14.827896 0.8008528
## SD-HFD         30.44948  19.383381 41.515575 0.0000000
## SD_Gln-HFD     26.13949  15.073392 37.205585 0.0000007
## SD-HFD_Gln     26.68768  15.621582 37.753775 0.0000004
## SD_Gln-HFD_Gln 22.37769  11.311592 33.443786 0.0000149
## SD_Gln-SD      -4.30999 -15.376086  6.756107 0.7271214</code></pre>
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<pre class="r"><code># comparison_data &lt;- compare_means(
#   Fisher ~ Group,
#   data = div_data, 
#   method = &quot;wilcox.test&quot;
#   ) %&gt;% filter(p.adj &lt;= 0.05)
# comparison_data</code></pre>
</div>
</div>
</div>
<div id="diversité-beta" class="section level3">
<h3>Diversité <span class="math inline">\(\beta\)</span></h3>
<div id="calcul-des-distances" class="section level4">
<h4>Calcul des distances</h4>
<pre class="r"><code>SampleOrder &lt;- levels(reorder(sample_names(frogs.data.rare), as.numeric(get_variable(frogs.data.rare, &quot;Group&quot;))))
dist.bc &lt;- distance(frogs.data.rare, &quot;bray&quot;)
p.bc&lt;-plot_dist_as_heatmap(dist.bc, order = SampleOrder,title=&quot;Bray Distances&quot;, show.names = T)

SampleOrder &lt;- levels(reorder(sample_names(frogs.data.rare), as.numeric(get_variable(frogs.data.rare, &quot;Group&quot;))))
dist.jac &lt;- distance(frogs.data.rare, &quot;cc&quot;)
p.jac&lt;-plot_dist_as_heatmap(dist.jac, order = SampleOrder,title=&quot;Jaccard Distances&quot;, show.names = T)

SampleOrder &lt;- levels(reorder(sample_names(frogs.data.rare), as.numeric(get_variable(frogs.data.rare, &quot;Group&quot;))))
dist.uf &lt;- distance(frogs.data.rare, &quot;unifrac&quot;)
p.uf&lt;-plot_dist_as_heatmap(dist.uf, order = SampleOrder,title=&quot;Unifrac Distances&quot;, show.names = T)

SampleOrder &lt;- levels(reorder(sample_names(frogs.data.rare), as.numeric(get_variable(frogs.data.rare, &quot;Group&quot;))))
dist.wuf &lt;- distance(frogs.data.rare, &quot;wunifrac&quot;)
p.wuf&lt;- plot_dist_as_heatmap(dist.wuf, order = SampleOrder,title=&quot;Weighted Unifrac Distances&quot;, show.names = T)

plot_grid(
  p.bc, p.jac, p.uf, p.wuf, ncol = 2)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/27-dist-as-heatmap-bc-1.png" width="960" /></p>
</div>
<div id="ordination-des-échantillons" class="section level4 tabset">
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<h4 class="tabset">Ordination des échantillons</h4>
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<p>On va faire une MDS (non-parametric multi-dimensional scaling) aussi appelé PCoA pour avoir une représentation en 2D de la distance entre tous les échantillons. Permet de visusaliser les similitudes entre les groupes.</p>
<p>La mise à l’échelle multidimensionnelle (MDS) est une approche populaire pour représenter graphiquement les relations entre des objets (par exemple, des tracés ou des échantillons) dans un espace multidimensionnel.</p>
<p>MDS fonctionne toujours alors que NMDS non. Et parce que la NMDS “déforme” l’espace alors que la MDS essaie de le préserver.</p>
<div id="plot-ordination-bray-curtis" class="section level5">
<h5>Plot ordination Bray Curtis</h5>
<pre class="r"><code>ord &lt;- ordinate(frogs.data.rare, method = &quot;MDS&quot;, distance = &quot;bray&quot;)

p &lt;- plot_ordination(frogs.data.rare, ord, color = &quot;Group&quot;,shape=&quot;Group&quot;)
p &lt;- p + theme_bw() + ggtitle(&quot;MDS + Bray Curtis Mâles&quot;)
p &lt;- p + stat_ellipse(aes(group = Group))+geom_point(size=2)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/30-ord-plot-bray-1.png" width="1008" /></p>
<p>Pour confirmer l’effet du régime sur les distances, on va faire une permanova. Analyse multivariée de la variance par permutations basée sur les matrices de distances.</p>
<pre class="r"><code>library(vegan)
adonis(dist.bc ~ Group,data = as(sample_data(frogs.data.rare), &#39;data.frame&#39;))</code></pre>
<pre><code>## 
## Call:
## adonis(formula = dist.bc ~ Group, data = as(sample_data(frogs.data.rare),      &quot;data.frame&quot;)) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(&gt;F)    
## Group      3    6.0064 2.00212   12.76 0.46524  0.001 ***
## Residuals 44    6.9040 0.15691         0.53476           
## Total     47   12.9103                 1.00000           
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<p>Df: degree of freedom. Sums Of Sqs: sum of squares. MeanSqs: sum of squares by degree of freedom. F: F statistics. R2: partial R-squared. Pr(&gt;F) p-value based</p>
</div>
<div id="plot-ordination-jaccard" class="section level5">
<h5>Plot ordination Jaccard</h5>
<pre class="r"><code>ord &lt;- ordinate(frogs.data.rare, method = &quot;MDS&quot;, distance = &quot;cc&quot;)

p &lt;- plot_ordination(frogs.data.rare, ord, color = &quot;Group&quot;,shape=&quot;Group&quot;)
p &lt;- p + theme_bw() + ggtitle(&quot;MDS + Jaccard Mâles&quot;)
p &lt;- p + stat_ellipse(aes(group = Group))+ geom_point(size = 2)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/31-ord-plot-jac-1.png" width="1008" /></p>
<pre class="r"><code>adonis(dist.jac ~ Group,data = as(sample_data(frogs.data.rare), &#39;data.frame&#39;))</code></pre>
<pre><code>## 
## Call:
## adonis(formula = dist.jac ~ Group, data = as(sample_data(frogs.data.rare),      &quot;data.frame&quot;)) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(&gt;F)    
## Group      3    3.7230 1.24100   12.52 0.46052  0.001 ***
## Residuals 44    4.3613 0.09912         0.53948           
## Total     47    8.0843                 1.00000           
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
</div>
<div id="plot-ordination-unifrac" class="section level5">
<h5>Plot ordination Unifrac</h5>
<pre class="r"><code>ord &lt;- ordinate(frogs.data.rare, method = &quot;MDS&quot;, distance = &quot;Unifrac&quot;)
p &lt;- plot_ordination(frogs.data.rare, ord, color = &quot;Group&quot;,shape=&quot;Group&quot;)
p &lt;- p + theme_bw() + ggtitle(&quot;MDS + Unifrac Mâles&quot;)
p &lt;- p + stat_ellipse(aes(group = Group))+ geom_point(size = 2)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/55-ord-plot-unifrac-1.png" width="1008" /></p>
<pre class="r"><code>adonis(dist.uf ~ Group,data = as(sample_data(frogs.data.rare), &#39;data.frame&#39;))</code></pre>
<pre><code>## 
## Call:
## adonis(formula = dist.uf ~ Group, data = as(sample_data(frogs.data.rare),      &quot;data.frame&quot;)) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(&gt;F)    
## Group      3    1.5445 0.51484  14.794 0.50216  0.001 ***
## Residuals 44    1.5312 0.03480         0.49784           
## Total     47    3.0758                 1.00000           
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
</div>
<div id="plot-ordination-weighted-unifrac" class="section level5">
<h5>Plot ordination Weighted Unifrac</h5>
<pre class="r"><code>ord &lt;- ordinate(frogs.data.rare, method = &quot;MDS&quot;, distance = &quot;wUnifrac&quot;)
p &lt;- plot_ordination(frogs.data.rare, ord, color = &quot;Group&quot;,shape=&quot;Group&quot;)
p &lt;- p + theme_bw() + ggtitle(&quot;MDS + wUnifrac Mâles&quot;)
p &lt;- p + stat_ellipse(aes(group = Group))+ geom_point(size = 2)
plot(p)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/33-ord-plot-unifrac-1.png" width="1008" /></p>
<pre class="r"><code>adonis(dist.wuf ~ Group,data = as(sample_data(frogs.data.rare), &#39;data.frame&#39;))</code></pre>
<pre><code>## 
## Call:
## adonis(formula = dist.wuf ~ Group, data = as(sample_data(frogs.data.rare),      &quot;data.frame&quot;)) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(&gt;F)    
## Group      3   1.42555 0.47518  22.384 0.60414  0.001 ***
## Residuals 44   0.93408 0.02123         0.39586           
## Total     47   2.35963                 1.00000           
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
</div>
</div>
<div id="pca" class="section level4">
<h4>PCA</h4>
<p>L’analyse des composants principaux permet d’extraire les informations importantes d’un tableau de données multivariées et d’exprimer ces informations sous la forme d’un ensemble de quelques nouvelles variables appelées composants principaux. L’ACP va conduire à un sous espace de plus petite dimension, tel que la projection sur ce sous-espace retienne la majeure partie de l’information.</p>
<p>PCA sur les 50 OTU les plus représentés</p>
<pre class="r"><code>#if(!require(devtools)) install.packages(&quot;devtools&quot;)
#devtools::install_github(&quot;kassambara/factoextra&quot;)
library(&quot;factoextra&quot;)

m &lt;- as.data.frame(t(otu_table(frogs.data)))
m &lt;- sqrt(m)
pca &lt;- prcomp(m[colSums(m) != 0], center = TRUE, scale = TRUE)

p &lt;- fviz_pca_biplot(pca, axes = c(1, 2), geom.ind = c(&quot;point&quot;, &quot;text&quot;), habillage = get_variable(frogs.data, &quot;Group&quot;), invisible = &quot;quali&quot;, geom.var = c(&quot;arrow&quot;, &quot;text&quot;), select.var = list(contrib = 50), title = &quot;Principal Component Analysis&quot;)

plot(p + theme_bw())</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/34-Plot%20PCA-1.png" width="1728" /></p>
</div>
<div id="clustering-des-échantillons" class="section level4 tabset">
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<h4 class="tabset">Clustering des échantillons</h4>
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<p>On peut aussi faire un clustering des échantillons pour vérifier s’ils se regroupent par groupe (ou autre).</p>
<p>Single : méthode du saut minimum prend en compte la plus petite distance entre A et B : <strong>non conseillé</strong></p>
<p>Complete : méthode du diamètre qui prend en compte la plus grande distance entre A et B : <strong>non conseillé</strong></p>
<p>Ward et ward.D2 : c’est la plus courante. Elle consiste à réunir les deux clusters dont le regroupement fera le moins baisser l’inertie interclasse. C’est la distance de Ward qui est utilisée : la distance entre deux classes est celle de leurs barycentres au carré, pondérée par les effectifs des deux clusters. Cette technique tend à regrouper les petites classes entre elles : <strong>conseillé</strong></p>
<p>Median : moyenne de tous les liens, qu’ils soient entre observations de deux clusters différents ou intraclasses. Cette méthode est la seule qui s’attache directement au cluster obtenu et non aux caractéristiques des clusters candidats.</p>
<p>Average : le logiciel mesure tous les liens entre chaque observation du cluster A et chaque observation du cluster B et en fait une moyenne. C’est une des méthodes les plus efficaces. Elle tend à réunir des clusters aux inerties faibles.</p>
<div id="distance-bray-curtis" class="section level5">
<h5>Distance Bray Curtis</h5>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.bc, method = &quot;ward.D2&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/33-plot-clust-bray%20curtis-1.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.bc, method = &quot;ward.D&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/33-plot-clust-bray%20curtis-2.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.bc, method = &quot;median&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/33-plot-clust-bray%20curtis-3.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.bc, method = &quot;average&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/33-plot-clust-bray%20curtis-4.png" width="960" /></p>
</div>
<div id="distance-jaccard" class="section level5">
<h5>Distance Jaccard</h5>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.jac, method = &quot;ward.D2&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/37-plot-clust-jaccard-1.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.jac, method = &quot;ward.D&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/37-plot-clust-jaccard-2.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.jac, method = &quot;median&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/37-plot-clust-jaccard-3.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.jac, method = &quot;average&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/37-plot-clust-jaccard-4.png" width="960" /></p>
</div>
<div id="distance-unifrac" class="section level5">
<h5>Distance Unifrac</h5>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.uf, method = &quot;ward.D2&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/38-plot-clust-jaccard-1.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.uf, method = &quot;ward.D&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/38-plot-clust-jaccard-2.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.uf, method = &quot;median&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/38-plot-clust-jaccard-3.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.uf, method = &quot;average&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/38-plot-clust-jaccard-4.png" width="960" /></p>
</div>
<div id="distance-wunifrac" class="section level5">
<h5>Distance wUnifrac</h5>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.wuf, method = &quot;ward.D2&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/42-plot-clust-jaccard-1.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.wuf, method = &quot;ward.D&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/42-plot-clust-jaccard-2.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.wuf, method = &quot;median&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/42-plot-clust-jaccard-3.png" width="960" /></p>
<pre class="r"><code>plot_clust(frogs.data.rare, dist = dist.wuf, method = &quot;average&quot;, color = &quot;Group&quot;)</code></pre>
<p><img src="report_Obeglu_M_files/figure-html/42-plot-clust-jaccard-4.png" width="960" /></p>
</div>
</div>
</div>
<div id="abondances-différentielles-mâles" class="section level3">
<h3>Abondances différentielles Mâles</h3>
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<p>Comparaisons entre les 4 groupes ‘HFD,’ ‘HFD_Gln,’ ‘SD’ et ‘SD_Gln’</p>
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<div class="alert alert-success" role="alert">
Je fais l’analyse différentielle avec pval = 0.05 et min.abundance=50
</div>
<pre class="r"><code>extract_daotus &lt;- function(cond1, cond2, physeq, pval = 0.05) {
  res &lt;- DESeq2::results(dds, contrast=c(&quot;Group&quot;, cond1, cond2)) %&gt;% as.data.frame() %&gt;% 
    mutate(OTU = taxa_names(physeq)) %&gt;% filter(padj &lt; pval) %&gt;% 
    inner_join(tax_table(physeq) %&gt;% as(&quot;matrix&quot;) %&gt;% as_tibble(rownames = &quot;OTU&quot;), by = &quot;OTU&quot;) %&gt;%
    arrange(log2FoldChange)
  return(res)
}
build_plotdata_daotus &lt;- function(da.otus, physeq, min.abundance = 50) {
  da.data &lt;- rarefy_even_depth(physeq, rng = 20170329, verbose = FALSE) %&gt;% 
    prune_taxa(da.otus$OTU, .)
  is_abundant &lt;- function(x) { x &gt; min.abundance }
  abundant_taxa &lt;- genefilter_sample(da.data, is_abundant, A = 1)
  da.data &lt;- prune_taxa(abundant_taxa, da.data)
  max.level &lt;- apply(as(tax_table(da.data), &quot;matrix&quot;), 1, function(x) { 
                         y &lt;- x[!is.na(x)]; 
                         y &lt;- y[y != &quot;&quot;];  
                         y &lt;- y[!grepl(&quot;unknown&quot;, y, ignore.case = T)]
                         y &lt;- y[y != &quot;Multi-affiliation&quot;]
                         # if (names(y)[length(y)] != &quot;Family&quot;) {
                         #     return(paste(y[&quot;Family&quot;], y[length(y)], sep = &quot;/&quot;))
                         #} else {
                             return(y[length(y)])
                         # } 
                     })
otu.formatted.names &lt;- data.frame(OTU2 = paste0(gsub(pattern = &quot;Cluster_&quot;, &quot;&quot;, names(max.level)), &quot;_&quot;, max.level), 
                          OTU = names(max.level), 
                          stringsAsFactors = FALSE) %&gt;% 
  inner_join(da.otus, by = &quot;OTU&quot;) %&gt;% arrange(log2FoldChange)
otu.order &lt;- otu.formatted.names %&gt;% pull(OTU2)
da.df &lt;- merge(psmelt(da.data), otu.formatted.names) %&gt;% mutate(OTU2 = factor(OTU2, levels = otu.order))
return(da.df)
}</code></pre>
<pre class="r"><code>is_present &lt;- function(comptage) {comptage &gt; 0} ## tester la présence d&#39;un OTU
prevalent_taxa &lt;- genefilter_sample(frogs.data, is_present, A = 2)
filtered.data &lt;- prune_taxa(prevalent_taxa, frogs.data)</code></pre>
<pre class="r"><code>cds &lt;- phyloseq_to_deseq2(filtered.data, design = ~ Group)
#L&#39;option poscounts est plus robuste à la sparsité / Robuste au sens valide même quand il y a énormément de zéros dans la matrice de #comptages (= matrice sparse) c&#39;est une matrice avec beaucoup de 0
dds &lt;- DESeq2::DESeq(cds, sfType = &quot;poscounts&quot;)</code></pre>
<pre class="r"><code>da.otus_1 &lt;- extract_daotus(&quot;HFD&quot;, &quot;HFD_Gln&quot;, filtered.data, pval = 0.05) 
da.otus_2 &lt;- extract_daotus(&quot;HFD&quot;, &quot;SD&quot;, filtered.data, pval = 0.05)
da.otus_3 &lt;- extract_daotus(&quot;HFD&quot;, &quot;SD_Gln&quot;, filtered.data, pval = 0.05)
da.otus_4 &lt;- extract_daotus(&quot;HFD_Gln&quot;, &quot;SD&quot;, filtered.data, pval = 0.05) 
da.otus_5 &lt;- extract_daotus(&quot;HFD_Gln&quot;, &quot;SD_Gln&quot;, filtered.data, pval = 0.05)
da.otus_6 &lt;- extract_daotus(&quot;SD&quot;, &quot;SD_Gln&quot;, filtered.data, pval = 0.05)

da.otus &lt;- bind_rows(da.otus_1,
                     da.otus_2,da.otus_3,da.otus_4,
                     da.otus_5,da.otus_6) %&gt;% 
  filter(!duplicated(OTU))

#Filtrer OTU avec pval&lt;0,01
#da.otu&lt;-subset(da.otus,padj&lt;0.01)
#dim(da.otus)</code></pre>
<div id="otus-qui-passent-les-filtres-avec-pval0.05" class="section level5">
<h5>OTUs qui passent les filtres avec pval=0.05</h5>
<pre class="r"><code>res &lt;- inner_join(da.otus, 
## Transformer la table taxonomique en tableau avec une colonne OTU
tax_table(frogs.data) %&gt;% as(&quot;matrix&quot;) %&gt;% as_tibble(rownames = &quot;OTU&quot;), 
                  by = &quot;OTU&quot;)

res%&gt;%
 DT::datatable(extensions = &#39;Buttons&#39;, options = list(dom = &#39;Bfrtip&#39;, 
                                  pageLength = 10,
                                  buttons = list(&#39;copy&#39;, &#39;csv&#39;, &#39;excel&#39;)))</code></pre>
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