set_edgeR {benchdamic} | R Documentation

## set_edgeR

### Description

Set the parameters for edgeR differential abundance detection method.

### Usage

    set_edgeR(
      pseudo_count = FALSE,
      group_name = NULL,
      design = NULL,
      robust = FALSE,
      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`).
`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 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 observation weights must be supplied (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_edgeR` method.

### See Also

`DA_edgeR`

### Examples

    # Set some basic combinations of parameters for edgeR
    base_edgeR <- set_edgeR(group_name = "group", design = ~ group, coef = 2)

    # Set a specific set of normalization for edgeR (even of other packages!)
    setNorm_edgeR <- set_edgeR(group_name = "group", design = ~ group, coef = 2,
        norm = c("TMM", "poscounts"))

    # Set many possible combinations of parameters for edgeR
    all_edgeR <- set_edgeR(pseudo_count = c(TRUE, FALSE), group_name = "group",
        design = ~ group, robust = c(TRUE, FALSE), coef = 2,
        weights_logical = c(TRUE,FALSE))

[Package benchdamic version 1.0.0 Index]