Differential abundance analysis

differential_abundance(
  MAE,
  tax_level,
  input_da_condition = c(),
  input_da_condition_covariate = NULL,
  min_num_filter = 5,
  input_da_padj_cutoff = 0.05,
  method = "DESeq2"
)

Arguments

MAE: A multi-assay experiment object
tax_level: The taxon level used for organisms
input_da_condition: Which condition is the target condition
input_da_condition_covariate: Covariates added to linear function
min_num_filter: Minimum number reads mapped to this microbe
input_da_padj_cutoff: adjusted pValue cutoff
method: choose between DESeq2 and limma

Value

A output dataframe

Examples

data_dir <- system.file("extdata/MAE.rds", package = "animalcules")
toy_data <- readRDS(data_dir)
differential_abundance(toy_data,
  tax_level = "phylum",
  input_da_condition = c("DISEASE"),
  min_num_filter = 2,
  input_da_padj_cutoff = 0.5,
  method = "DESeq2"
)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> -- replacing outliers and refitting for 4 genes
#> -- DESeq argument 'minReplicatesForReplace' = 7 
#> -- original counts are preserved in counts(dds)
#> estimating dispersions
#> fitting model and testing
#>      [,1]                                     
#> [1,] "No differentially abundant items found!"