DA_MAST | R Documentation |
Fast run for MAST differential abundance detection method.
DA_MAST(
object,
assay_name = "counts",
pseudo_count = FALSE,
rescale = c("median", "default"),
design,
coefficient = NULL,
verbose = TRUE
)
object |
a phyloseq or TreeSummarizedExperiment object. |
assay_name |
the name of the assay to extract from the
TreeSummarizedExperiment object (default |
pseudo_count |
add 1 to all counts if TRUE (default
|
rescale |
Rescale count data, per million if 'default', or per median library size if 'median' ('median' is suggested for metagenomics data). |
design |
The model for the count distribution. Can be the variable name, or a character similar to "~ 1 + group", or a formula, or a 'model.matrix' object. |
coefficient |
The coefficient of interest as a single word formed by the variable name and the non reference level. (e.g.: 'ConditionDisease' if the reference level for the variable 'Condition' is 'control'). |
verbose |
an optional logical value. If |
A list object containing the matrix of p-values 'pValMat', the matrix of summary statistics for each tag 'statInfo', and a suggested 'name' of the final object considering the parameters passed to the function.
zlm
for the Truncated Gaussian Hurdle model
estimation.
set.seed(1)
# Create a very simple phyloseq object
counts <- matrix(rnbinom(n = 600, size = 3, prob = 0.5),
nrow = 100, ncol = 6)
metadata <- data.frame("Sample" = c("S1", "S2", "S3", "S4", "S5", "S6"),
"group" = as.factor(c("A", "A", "A", "B", "B", "B")))
ps <- phyloseq::phyloseq(phyloseq::otu_table(counts, taxa_are_rows = TRUE),
phyloseq::sample_data(metadata))
# Differential abundance
DA_MAST(object = ps, pseudo_count = FALSE, rescale = "median",
design = ~ group, coefficient = "groupB")
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