set_DESeq2: set_DESeq2

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

set_DESeq2

Description

Set the parameters for DESeq2 differential abundance detection method.

Usage

set_DESeq2(
  assay_name = "counts",
  pseudo_count = FALSE,
  design = NULL,
  contrast = NULL,
  alpha = 0.05,
  norm = c("ratio", "poscounts", "iterate"),
  weights_logical = FALSE,
  expand = TRUE
)

Arguments

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_logical	logical vector, if TRUE a matrix of observational weights will be used for differential abundance analysis (default weights_logical = FALSE).
expand	logical, if TRUE create all combinations of input parameters (default expand = TRUE).

Value

A named list containing the set of parameters for DA_DESeq2 method.

See Also

DA_DESeq2

Examples

# Set some basic combinations of parameters for DESeq2
base_DESeq2 <- set_DESeq2(design = ~ group, contrast = c("group", "B", "A"))
# Set a specific set of normalization for DESeq2
setNorm_DESeq2 <- set_DESeq2(design = ~ group, contrast =
    c("group", "B", "A"), norm = c("ratio", "poscounts"))
# Set many possible combinations of parameters for DESeq2
all_DESeq2 <- set_DESeq2(pseudo_count = c(TRUE, FALSE), design = ~ group,
    contrast = c("group", "B", "A"), weights_logical = c(TRUE,FALSE))

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