set_DESeq2 {benchdamic} | R Documentation

## set_DESeq2

### Description

Set the parameters for DESeq2 differential abundance detection method.

### Usage

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

### Arguments

`pseudo_count` | add 1 to all counts if TRUE (default `pseudo_count = FALSE`).
`design` | (Required). A `formula` which specifies the design of the experiment, taking the form `formula(~ x + y + z)`. That is, a formula with right-hand side only. By default, the functions in this package and DESeq2 will use the last variable in the formula (e.g. `z`) for presenting results (fold changes, etc.) and plotting. When considering your specification of experimental design, you will want to re-order the levels so that the `NULL` set is first. For example, the following line of code would ensure that Enterotype 1 is used as the reference sample class in tests by setting it to the first of the factor levels using the `relevel` function: `sample_data(entill)$Enterotype <- relevel(sample_data(entill)$Enterotype, "1")`
`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 used to compute the normalization factors to use in the differential abundance analysis. If `norm` is equal to "TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "CSSmedian", "CSSdefault", "TSS" the scaling factors are automatically transformed into normalization 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 (even of other packages!)
    setNorm_DESeq2 <- set_DESeq2(design = ~ group, contrast =
        c("group", "B", "A"), norm = c("TMM", "poscounts"))
    # Set many possible combinations of parameters for edgeR
    all_DESeq2 <- set_DESeq2(pseudo_count = c(TRUE, FALSE), design = ~ group,
        contrast = c("group", "B", "A"), weights_logical = c(TRUE,FALSE))

[Package benchdamic version 1.0.0 Index]