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

## ----setup--------------------------------------------------------------------
library(dar)
# suppressPackageStartupMessages(library(plotly))
data("metaHIV_phy")

metaHIV_phy

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

## -----------------------------------------------------------------------------
## QC 
phy_qc(rec)

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

rec

## -----------------------------------------------------------------------------
## DA steps definition
rec <- rec |> 
  step_deseq() |> 
  step_maaslin(min_prevalence = 0) 

rec

## -----------------------------------------------------------------------------
## Execute in parallel
da_results <- prep(rec, parallel = TRUE)
da_results

## -----------------------------------------------------------------------------
## Default DA taxa results
results <- 
  bake(da_results) |> 
  cool()

results

## ----fig.height=5-------------------------------------------------------------
## Intersection plot 
intersection_plt(da_results, ordered_by = "degree", font_size = 1)

## -----------------------------------------------------------------------------
## Exclusion plot 
exclusion_plt(da_results)

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

## ----fig.height=6-------------------------------------------------------------
## Mutual plot
mutual_plt(
  da_results, 
  count_cutoff = length(steps_ids(da_results, type = "da"))
)

## -----------------------------------------------------------------------------
## Define consensus strategy
da_results <- bake(da_results)
da_results

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

f_results

## ----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") 

## -----------------------------------------------------------------------------
devtools::session_info()