DA_edgeR: DA_edgeR

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

DA_edgeR

Description

Fast run for edgeR differential abundance detection method.

Usage

DA_edgeR(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  group_name = NULL,
  design = NULL,
  robust = FALSE,
  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).
group_name	character giving the name of the column containing information about experimental group/condition for each sample/library.
design	character or formula to specify the model matrix.
robust	logical, should the estimation of prior.df be robustified against outliers?
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 dispersion estimates dispEsts, 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

DGEList for the edgeR DEG object creation, estimateDisp and estimateGLMRobustDisp for dispersion estimation, and glmQLFit and glmQLFTest for the quasi-likelihood negative binomial model fit.

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_edgeR(object = ps_NF, pseudo_count = FALSE, group_name = "group",
         design = ~ group, coef = 2, robust = FALSE, norm = "TMM")

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