set_limma {benchdamic} | R Documentation

## set_limma

### Description

Set the parameters for limma differential abundance detection method.

### Usage

    set_limma(
      pseudo_count = FALSE,
      design = NULL,
      coef = 2,
      norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none"),
      weights_logical = FALSE,
      expand = TRUE
    )

### Arguments

`pseudo_count` | add 1 to all counts if TRUE (default `pseudo_count = FALSE`).
`design` | character name of the metadata columns, formula, or design matrix with rows corresponding to samples and columns to coefficients to be estimated.
`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 used to compute the scaling factors to use in the differential abundance analysis. If `norm` is equal to "ratio", "poscounts", or "iterate" the normalization factors are automatically transformed into scaling 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_limma` method.

### See Also

`DA_limma`

### Examples

    # Set some basic combinations of parameters for limma
    base_limma <- set_limma(design = ~ group, coef = 2)
    # Set a specific set of normalization for limma (even of other packages!)
    setNorm_limma <- set_limma(design = ~ group, coef = 2,
        norm = c("TMM", "poscounts"))
    # Set many possible combinations of parameters for limma
    all_limma <- set_limma(pseudo_count = c(TRUE, FALSE), design = ~ group,
        coef = 2, weights_logical = c(TRUE,FALSE))

[Package benchdamic version 1.0.0 Index]