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!"