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# dar <a href="https://microbialgenomics-irsicaixaorg.github.io/dar/"><img src="man/figures/logo.png" align="right" height="138" /></a>
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## Introduction
Differential abundance testing in microbiome data challenges both
parametric and non-parametric statistical methods, due to its sparsity,
high variability and compositional nature. Microbiome-specific
statistical methods often assume classical distribution models or take
into account compositional specifics. These produce results that range
within the specificity vs sensitivity space in such a way that type I
and type II error are difficult to ascertain in real microbiome data
when a single method is used. Recently, a consensus approach based on
multiple differential abundance (DA) methods was recently suggested in
order to increase robustness.
With dar, you can use dplyr-like pipeable sequences of DA methods and
then apply different consensus strategies. In this way we can obtain
more reliable results in a fast, consistent and reproducible way.
## Installation
You can install the development version of dar from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("MicrobialGenomics-IrsicaixaOrg/dar")
```
## Usage
``` r
library(dar)
#> Registered S3 methods overwritten by 'vegan':
#> method from
#> reorder.hclust seriation
#> rev.hclust dendextend
data("metaHIV_phy")
## Define recipe
rec <-
recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") %>%
step_subset_taxa(expr = 'Kingdom %in% c("Bacteria", "Archaea")') %>%
step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.03 * length(x))") %>%
step_metagenomeseq(rm_zeros = 0.01) %>%
step_maaslin()
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 451 taxa and 156 samples
#> ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
#> ℹ taxonomic level Species
#>
#> Preporcessing steps:
#>
#> ◉ step_subset_taxa() id = subset_taxa__Suncake
#> ◉ step_filter_taxa() id = filter_taxa__Hot_water_crust_pastry
#>
#> DA steps:
#>
#> ◉ step_metagenomeseq() id = metagenomeseq__Crocetta_of_Caltanissetta
#> ◉ step_maaslin() id = maaslin__Tortita_negra
## Prep recipe
da_results <- prep(rec, parallel = TRUE)
da_results
#> ── DAR Results ─────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 278 taxa and 156 samples
#> ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
#> ℹ taxonomic level Species
#>
#> Results:
#>
#> ✔ metagenomeseq__Crocetta_of_Caltanissetta diff_taxa = 236
#> ✔ maaslin__Tortita_negra diff_taxa = 146
#>
#> ℹ 124 taxa are present in all tested methods
## Consensus strategy
n_methods <- 2
da_results <- bake(da_results, count_cutoff = n_methods)
da_results
#> ── DAR Results ─────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 278 taxa and 156 samples
#> ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
#> ℹ taxonomic level Species
#>
#> Results:
#>
#> ✔ metagenomeseq__Crocetta_of_Caltanissetta diff_taxa = 236
#> ✔ maaslin__Tortita_negra diff_taxa = 146
#>
#> ℹ 124 taxa are present in all tested methods
#>
#> Bakes:
#>
#> ◉ 1 -> count_cutoff: 2, weights: NULL, exclude: NULL, id: bake__Kürtőskalács
## Results
cool(da_results)
#> ℹ Bake for count_cutoff = 2
#> # A tibble: 124 × 2
#> taxa_id taxa
#> <chr> <chr>
#> 1 Otu_63 Bacteroides_plebeius
#> 2 Otu_216 Clostridium_sp_CAG_632
#> 3 Otu_441 Brachyspira_sp_CAG_700
#> 4 Otu_108 Prevotella_sp_CAG_520
#> 5 Otu_257 Butyrivibrio_sp_CAG_318
#> 6 Otu_104 Prevotella_sp_CAG_1092
#> 7 Otu_69 Bacteroides_sp_CAG_530
#> 8 Otu_102 Prevotella_sp_AM42_24
#> 9 Otu_159 Lactobacillus_ruminis
#> 10 Otu_117 Alistipes_inops
#> # ℹ 114 more rows
```
## Contributing
- If you think you have encountered a bug, please [submit an
issue](https://github.com/MicrobialGenomics-IrsicaixaOrg/dar/issues).
- Either way, learn how to create and share a
[reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html)
(a minimal, reproducible example), to clearly communicate about your
code.
- Working on your first Pull Request? You can learn how from this *free*
series [How to Contribute to an Open Source Project on
GitHub](https://kcd.im/pull-request)
## Code of Conduct
Please note that the dar project is released with a [Contributor Code of
Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.
