---
title: "FROGS Phyloseq: Sample clustering using different linkage method (version `r params$version`)"
copyright: "Copyright (C) 2024 INRAE"
license: "GNU General Public License"
version: "5.0.0"
output: 
  html_notebook:
    code_folding: hide
params:
   phyloseq:
      value: x
   varExp:
      value: x
   distance:
      value: x
   libdir:
      value: x
   version:
      value: x
---
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<meta name="author" content="Ta Thi Ngan - SIGENAE/GABI & Maria Bernard - SIGENAE/GABI" />
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```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, error = TRUE)
```
Phyloseq `r packageVersion("phyloseq")`

```{=html}
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	<option selected disabled>Switch theme</option>
	<option value="Plum">Plum</option>
	<option value="Coral">Coral</option>
	<option value="FROGS4">FROGS4</option>
</select>
```

```{r import, message=FALSE, warning=FALSE}

## Import packages
library(phyloseq)
library(scales)
library(ape)
source(file.path(params$libdir, "graphical_methods.R"))
## Alternative to source all extra function from a github repo
## source("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/load-extra-functions.R")

## Setting variables
  ## The Phyloseq object (format rdata)
  # phyloseq <- ""

  ## The beta diversity distance matrix file
  # distance <- ""

  ## The experiment variable that you want to analyse
  # varExp <- ""

## Create input and parameters dataframe
  # params <- data.frame( "phyloseq" = phylose, "distance" = distance, "varExp" = varExp)

## Load data
  ## the phyloseq object
load(params$phyloseq)

  ## the distance matrix file
A        <- read.table(file=params$distance, row.names=1)
dist     <- as.dist(A)
```

# {.tabset .tabset-fade .tabset-pills}

## Ward D2
```{r ward, fig.width=8, fig.height=7, echo=FALSE}
plot_clust(data, dist, method = "ward.D2", color = params$varExp)
```

## Complete
```{r complete, fig.width=8, fig.height=7, echo=FALSE}
plot_clust(data, dist, method = "complete", color = params$varExp)
```

## Single
```{r single, fig.width=8, fig.height=7, echo=FALSE}
plot_clust(data, dist, method = "single", color = params$varExp)
```
