DA_dearseq | R Documentation |
Fast run for dearseq differential abundance detection method.
DA_dearseq( object, assay_name = "counts", pseudo_count = FALSE, covariates = NULL, variables2test = NULL, sample_group = NULL, test = c("permutation", "asymptotic"), preprocessed = FALSE, n_perm = 1000, 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
|
covariates |
a character vector containing the colnames of the covariates to include in the model. |
variables2test |
a character vector containing the colnames of the variable of interest. |
sample_group |
a vector of length |
test |
a character string indicating which method to use to approximate
the variance component score test, either 'permutation' or 'asymptotic'
(default |
preprocessed |
a logical flag indicating whether the expression data have
already been preprocessed (e.g. log2 transformed). Default is |
n_perm |
the number of perturbations. Default is |
verbose |
an optional logical value. If |
A list object containing the matrix of p-values 'pValMat', a matrix of summary statistics for each tag 'statInfo' which are still the p-values as this method does not produce other values, and a suggested 'name' of the final object considering the parameters passed to the function.
dear_seq
for analysis of differential
expression/abundance through a variance component test.
set.seed(1) # Create a very simple phyloseq object counts <- matrix(rnbinom(n = 60, size = 3, prob = 0.5), nrow = 10, 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_dearseq(object = ps, pseudo_count = FALSE, covariates = NULL, variables2test = "group", sample_group = NULL, test = "asymptotic", preprocessed = FALSE, verbose = TRUE)
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