DA_limma {benchdamic}

R Documentation

DA_limma

Description

Fast run for limma voom differential abundance detection method.

Usage

DA_limma(
  object,
  pseudo_count = FALSE,
  design = NULL,
  coef = 2,
  norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none",
    "ratio", "poscounts", "iterate", "TSS", "CSSmedian", "CSSdefault"),
  weights,
  verbose = TRUE
)

Arguments

object	phyloseq object.
pseudo_count	add 1 to all counts if TRUE (default pseudo_count = FALSE).
design	character name of the metadata columns, formula, or design matrix with rows corresponding to samples and columns to coefficients to be estimated.
coef	integer or character index vector indicating which coefficients of the linear model are to be tested equal to zero.
norm	name of the normalization method used to compute the scaling factors to use in the differential abundance analysis. If norm is equal to "ratio", "poscounts", or "iterate" the normalization factors are automatically transformed into scaling factors.
weights	an optional numeric matrix giving observational weights.
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

voom for the mean-variance relationship estimation, lmFit for the linear model framework.

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))
# Calculate the TMM scaling factors
ps_NF <- norm_edgeR(object = ps, method = "TMM")
# The phyloseq object now contains the scaling factors:
scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.TMM"]
head(scaleFacts)
# Differential abundance
DA_limma(object = ps_NF, pseudo_count = FALSE, design = ~ group, coef = 2,
    norm = "TMM")

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