DA_limma: DA_limma

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

DA_limma

Description

Fast run for limma voom differential abundance detection method.

Usage

DA_limma(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  design = NULL,
  coef = 2,
  norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none"),
  weights,
  verbose = TRUE
)

Arguments

object	a phyloseq or TreeSummarizedExperiment object.
assay_name	the name of the assay to extract from the TreeSummarizedExperiment object (default assayName = "counts"). Not used if the input object is a phyloseq.
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 to use in the differential abundance analysis. Choose between the native edgeR normalization methods, such as TMM, TMMwsp, RLE, upperquartile, posupperquartile, or none. Alternatively (only for advanced users), if norm is equal to "ratio", "poscounts", or "iterate" from norm_DESeq2, "CSS" from norm_CSS, or "TSS" from norm_TSS, the scaling factors are automatically transformed into normalization factors. If custom factors are supplied, make sure they are compatible with edgeR normalization 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 normalization factors
ps_NF <- norm_edgeR(object = ps, method = "TMM")
# The phyloseq object now contains the normalization factors:
normFacts <- phyloseq::sample_data(ps_NF)[, "NF.TMM"]
head(normFacts)
# Differential abundance
DA_limma(object = ps_NF, pseudo_count = FALSE, design = ~ group, coef = 2,
    norm = "TMM")

mcalgaro93/benchdamic documentation built on Nov. 28, 2022, 6:55 p.m.