DA_MAST {benchdamic}R Documentation

DA_MAST

Description

Fast run for MAST differential abundance detection method.

Usage

DA_MAST(
  object,
  pseudo_count = FALSE,
  rescale = c("median", "default"),
  design,
  coefficient = NULL,
  norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none",
    "ratio", "poscounts", "iterate", "TSS", "CSSmedian", "CSSdefault"),
  verbose = TRUE
)

Arguments

object

phyloseq object.

pseudo_count

add 1 to all counts if TRUE (default pseudo_count = FALSE).

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').

norm

name of the normalization method used to compute the normalization factors to use in the differential abundance analysis. If norm is equal to "TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "CSSmedian", "CSSdefault", "TSS" the scaling factors are automatically transformed into normalization factors.

verbose

an optional logical value. If TRUE, information about the steps of the algorithm is printed. Default verbose = TRUE.

Value

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.

See Also

zlm for the Truncated Gaussian Hurdle model estimation.

Examples

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))
# No use of scaling factors
ps_NF <- norm_edgeR(object = ps, method = "none")
# The phyloseq object now contains the scaling factors:
scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.none"]
head(scaleFacts)
# Differential abundance
DA_MAST(object = ps_NF, pseudo_count = FALSE, rescale = "median",
    design = ~ group, norm = "none", coefficient = "groupB")

[Package benchdamic version 1.0.0 Index]