---
title: "Workflow with real data"
output: rmarkdown::html_vignette
description: |
  
vignette: >
  %\VignetteIndexEntry{Workflow with real data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<style>
body {
text-align: justify}
</style>

```{r, include = FALSE}
knitr::opts_chunk$set(
  message = FALSE,
  digits = 3,
  collapse = TRUE,
  comment = "#>",
  fig.align = "center", 
  fig.width = 8
)
options(digits = 3)
```

To illustrate the functionality of the `dar` package, this study will use the 
data set from (Noguera-Julian, M., et al. 2016). The authors of this study 
found that men who have sex with men (MSM) predominantly belonged to the 
Prevotella-rich enterotype whereas most non-MSM subjects were enriched in 
Bacteroides, independently of HIV-1 status. This result highlights the 
potential impact of sexual orientation on the gut microbiome and emphasizes the 
importance of controlling for such variables in microbiome research. Using the 
`dar` package, we will conduct a differential abundance analysis to further 
explore this finding and uncover potential microbial biomarkers associated with 
this specific population.

## Load dar package and data

```{r setup}
library(dar)
# suppressPackageStartupMessages(library(plotly))
data("metaHIV_phy")

metaHIV_phy
```

## Recipe initialization

To begin the analysis process with the `dar` package, the first step is to 
initialize a `Recipe` object, which is an S4 class. This recipe object serves as 
a blueprint for the data preparation steps required for the differential 
abundance analysis. The initialization of the recipe object is done through the 
function `recipe()`, which takes as inputs a `phyloseq` or `TreeSummarizedExperiment` 
(TSE) object, the name of the categorical variable of interest and the taxonomic
level at which the differential abundance analyses are to be performed. As 
previously mentioned, we will use the data set from 
(Noguera-Julian, M., et al. 2016) and the variable of interest "RiskGroup2" 
containing the categories: men who have sex with men (msm), non-MSM (hts) and 
people who inject drugs (pwid) and we will perform the analysis at the species 
level.

```{r}
## Recipe initialization
rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species")
rec
```

## Recipe QC and preprocessing steps definition

Once the recipe object has been initialized, the next step is to populate it 
with steps. Steps are the methods that will be applied to the data stored in the
recipe. There are two types of steps: preprocessing (prepro) and differential 
abundance (da) steps. Initially, we will focus on the prepro steps which are 
used to modify the data loaded into the recipe, which will then be used for the 
da steps. The ‘dar’ package includes 3 main preprocessing functionalities: 
`step_subset_taxa`, which is used for subsetting columns and values in the taxon 
table connected to the phyloseq object, `step_filter_taxa`, which is used to 
filter the OTUs, and `step_rarefaction`, which is used to resample the OTU table 
to ensure that all samples have the same library size. These functionalities 
allow for a high level of flexibility and customization in the data preparation 
process before performing the differential abundance analysis.

The `dar` package provides convenient wrappers for the `step_filter_taxa` 
function, designed to filter Operational Taxonomic Units (OTUs) based on 
specific criteria: prevalence, variance, abundance, and rarity. 

- `step_filter_by_prevalence`: Filters OTUs according to the number of samples 
in which the OTU appears.
- `step_filter_by_variance`: Filters OTUs based on the variance of the OTU's 
presence across samples.
- `step_filter_by_abundance`: Filters OTUs according to the OTU's abundance 
across samples.
- `step_filter_by_rarity`: Filters OTUs based on the rarity of the OTU across 
samples.

In addition to the preprocessing steps, the `dar` package also incorporates the 
function `phy_qc` which returns a table with a set of metrics that allow for 
informed decisions to be made about the data preprocessing that will be done. 
In our case, we decided to use the step_subset_taxa function to retain only 
those observations annotated within the realm of Bacteria and Archaea. We also 
used the `step_filter_by_prevalence` function to retain only those OTUs with at 
least 1% of the samples with values greater than 0. This approach ensured that 
we were working with a high-quality, informative subset of the data, which 
improved the overall accuracy and reliability of the differential abundance 
analysis.

```{r}
## QC 
phy_qc(rec)

## Adding prepro steps
rec <- 
  rec |>
  step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |>
  step_filter_by_prevalence()

rec
```

## Define Differential Analysis (DA) steps

Once data is preprocessed and cleaned, the next step is to add the da steps. 
The `dar` package incorporates multiple methods to analyze the data, including: 
ALDEx2, ANCOM-BC, corncob, DESeq2, Lefse, MAaslin2, MetagenomeSeq, and Wilcox. 
These methods provide a range of options for uncovering potential microbial 
biomarkers associated with the variable of interest. To ensure consistency 
across methods, we decided not to use default parameters, but to set the 
`min_prevalence` parameter to 0 for MAaslin2, and the `rm_zeros` parameter to 
0.01 for MetagenomeSeq, since it was observed that the pct_all_zeros value was 
not equal to 0 in some levels of the categorical variable in the results of 
`phy_qc()`. This approach ensured that the analysis was consistent across all 
methods and that the results were interpretable.

Note: to reduce computation time, in this example we will only use the 
metagenomeSeq and MAaslin2 methods, that are the fastest ones. However, we 
recommend using all the methods available in the package to ensure a more 
robust analysis.

```{r}
## DA steps definition
rec <- rec |> 
  step_metagenomeseq(rm_zeros = 0.01) |> 
  step_maaslin(min_prevalence = 0) 

rec
```

## Prep recipe

Once the recipe has been defined, the next step is to execute all the steps 
defined in the recipe. This is done through the function `prep()`. Internally, 
it first executes the preprocessing steps, which modify the phyloseq object 
stored in the recipe. Then, using the modified phyloseq, it executes each of 
the defined differential abundance methods. To speed up the execution time, the 
`prep()` function includes the option to run in parallel. The resulting object 
has class `PrepRecipe` and when printed in the terminal, it displays the 
number of taxa detected as significant in each of the methods and also the 
total number of taxa shared across all methods. This allows for a provisional 
overview of the results and a comparison between methods.

```{r}
## Execute in parallel
da_results <- prep(rec, parallel = TRUE)
da_results
```

## Default results extraction

At this point, we could extract the taxa shared across all methods using the 
function `bake()` to define a default consensus strategy and then `cool()` to 
extract the results. 

```{r}
## Default DA taxa results
results <- 
  bake(da_results) |> 
  cool()

results
```

However, `dar` allows for complex consensus strategies based on the obtained 
results. To that end, the user has access to different functions to graphically 
represent different types of information. This feature allows for a more 
in-depth analysis of the results and a better understanding of the underlying 
patterns in the data.

## Exploration for consensus strategie definition 

For example, `intersection_plt()` gives an overview of the overlaps between 
methods by creating an upSet plot. In our case, this function has shown that 
210 taxa are shared across all the methods used.

```{r, fig.height=5}
## Intersection plot 
intersection_plt(da_results, ordered_by = "degree", font_size = 1)
```

In addition to the `intersection_plt()` function, `dar` also has the function 
`exclusion_plt()` which provides information about the number of OTUs shared 
between methods. This function allows to identify the OTUs that are specific to 
each method and also the ones that are not shared among any method.

```{r}
## Exclusion plot 
exclusion_plt(da_results)
```

Besides to the previously mentioned functions, `dar` also includes the function 
`corr_heatmap()`, which allows for visualization of the overlap of significant 
OTUs between tested methods. This function can provide similar information to 
the previous plots, but in some cases it may be easier to interpret.
comprehensive view of the results.

```{r, fig.height=6}
## Correlation heatmap
corr_heat <- corr_heatmap(da_results, font_size = 10) 
corr_heat
```

Finally, `dar` also includes the function `mutual_plt()`, which plots the number 
of differential abundant features mutually found by a defined number of 
methods, colored by the differential abundance direction and separated by 
comparison. The resulting graph allows us to see that the features detected 
correspond mainly to the comparisons between hts vs msm and msm vs pwid. 
Additionally, the graph also allows us to observe the direction of the effect; 
whether a specific OTU is enriched or depleted for each comparison.

```{r, fig.height=6}
## Mutual plot
mutual_plt(
  da_results, 
  count_cutoff = length(steps_ids(da_results, type = "da"))
)
```

## Define a consesus strategy using bake

After visually inspecting the results from running all the differential 
analysis methods on our data, we have the necessary information to define a 
consensus strategy that fits our dataset. In our case, we will retain all the 
methods. However if one or more methods are not desired, the `bake()` function
includes the `exclude` parameter, which allows to exclude specific methods. 

Additionally, the `bake()` function allows to further refine the consensus 
strategy through its parameters, such as `count_cutoff`, which indicates the 
minimum number of methods in which an OTU must be present, and `weights`, a 
named vector with the ponderation value for each method. However, for 
simplicity, these parameters are not used in this example.

```{r}
## Define consensus strategy
da_results <- bake(da_results)
da_results
```

## Extract results

To conclude, we can extract the final results using the `cool()` function. This 
function takes a `PrepRecipe` object and the ID of the bake to be used as input 
(by default it is 1, but if you have multiple consensus strategies, you can 
change it to extract the desired results).

```{r}
## Extract results for bake id 1
f_results <- cool(da_results, bake = 1)

f_results
```

To further visualize the results, the `abundance_plt()` function can be utilized 
to visualize the differences in abundance of the differential abundant taxa.

```{r, fig.height=6}
## Ids for Bacteroide and Provotella species 
ids <- 
  f_results |>
  dplyr::filter(stringr::str_detect(taxa, "Bacteroi.*|Prevote.*")) |>
  dplyr::pull(taxa_id) 

## Abundance plot as boxplot
abundance_plt(da_results, taxa_ids = ids, type = "boxplot") 
```

## Session info

```{r}
devtools::session_info()
```
