DA_NOISeq: DA_NOISeq

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

DA_NOISeq

Description

Fast run for NOISeqBIO differential abundance detection method. It computes differential expression between two experimental conditions.

Usage

DA_NOISeq(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  contrast = NULL,
  norm = c("rpkm", "uqua", "tmm", "n"),
  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).
contrast	character vector with exactly, three elements: a string indicating the name of factor whose levels are the conditions to be compared, the name of the level of interest, and the name of the other level.
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.
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', a matrix of summary statistics for each tag 'statInfo', and a suggested 'name' of the final object considering the parameters passed to the function. NOISeq does not produce p-values but an estimated probability of differential expression for each feature. Note that these probabilities are not equivalent to p-values. The higher the probability, the more likely that the difference in abundance is due to the change in the experimental condition and not to chance... Hence, 'pValMat' matrix is filled with 1 - prob values which can be interpreted as 1 - FDR. Where FDR can be considered as an adjusted p-value (see NOISeq vignette).

See Also

noiseqbio for analysis of differential expression/abundance between two experimental conditions from read count data.

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))
# Differential abundance
DA_NOISeq(object = ps, pseudo_count = FALSE, contrast = c("group", "B", "A"),
    norm = "tmm", verbose = FALSE)

mcalgaro93/benchdamic documentation built on March 10, 2024, 10:40 p.m.