DA_limma {benchdamic} | R Documentation

## DA_limma

### Description

Fast run for limma voom differential abundance detection method.

### Usage

    DA_limma(
      object,
      pseudo_count = FALSE,
      design = NULL,
      coef = 2,
      norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none",
        "ratio", "poscounts", "iterate", "TSS", "CSSmedian", "CSSdefault"),
      weights,
      verbose = TRUE
    )

### Arguments

`object` | phyloseq object.
`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` | 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 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

`voom` for the mean-variance relationship estimation, `lmFit` for the linear model framework.

### 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 TMM scaling factors
    ps_NF <- norm_edgeR(object = ps, method = "TMM")
    # The phyloseq object now contains the scaling factors:
    scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.TMM"]
    head(scaleFacts)
    # Differential abundance
    DA_limma(object = ps_NF, pseudo_count = FALSE, design = ~ group, coef = 2,
        norm = "TMM")

[Package benchdamic version 1.0.0 Index]