DA_DESeq2: DA_DESeq2

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

DA_DESeq2

Description

Fast run for DESeq2 differential abundance detection method.

Usage

DA_DESeq2(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  design = NULL,
  contrast = NULL,
  alpha = 0.05,
  norm = c("ratio", "poscounts", "iterate"),
  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 or formula to specify the model matrix.
contrast	character vector with exactly three elements: the name of a factor in the design formula, the name of the numerator level for the fold change, and the name of the denominator level for the fold change.
alpha	the significance cutoff used for optimizing the independent filtering (by default 0.05). If the adjusted p-value cutoff (FDR) will be a value other than 0.05, alpha should be set to that value.
norm	name of the normalization method to use in the differential abundance analysis. Choose between the native DESeq2 normalization methods, such as ratio, poscounts, or iterate. Alternatively (only for advanced users), if norm is equal to "TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", or "none" from norm_edgeR, "CSS" from norm_CSS, or "TSS" from norm_TSS, the normalization factors are automatically transformed into size factors. If custom factors are supplied, make sure they are compatible with DESeq2 size 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

phyloseq_to_deseq2 for phyloseq to DESeq2 object conversion, DESeq and results for the differential abundance method.

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 poscounts size factors
ps_NF <- norm_DESeq2(object = ps, method = "poscounts")
# The phyloseq object now contains the size factors:
sizeFacts <- phyloseq::sample_data(ps_NF)[, "NF.poscounts"]
head(sizeFacts)
# Differential abundance
DA_DESeq2(object = ps_NF, pseudo_count = FALSE, design = ~ group, contrast =
    c("group", "B", "A"), norm = "poscounts")

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