GitHub - MicrobialGenomics-IrsicaixaOrg/dar: dar: runs multiple differential abundance analysis methods and through a consensus strategy returns a set of differentially abundant features. (original) (raw)

dar

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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 fromGitHub with:

install.packages("devtools")

devtools::install_github("MicrobialGenomics-IrsicaixaOrg/dar")

Usage

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
#>
#> 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

Code of Conduct

Please note that the dar project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.