DA_DESeq2 {benchdamic} | R Documentation

## DA_DESeq2

### Description

Fast run for DESeq2 differential abundance detection method.

### Usage

    DA_DESeq2(
      object,
      pseudo_count = FALSE,
      design = NULL,
      contrast = NULL,
      alpha = 0.05,
      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` | (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` | 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 dispersion estimates 'dispEsts', 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

`phyloseq_to_deseq2` for phyloseq to DESeq2 object conversion, `DESeq` and `results` for the differential abundance method.

### 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 poscounts normalization factors
    ps_NF <- norm_DESeq2(object = ps, method = "poscounts")
    # The phyloseq object now contains the normalization factors:
    scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.poscounts"]
    head(scaleFacts)
    # Differential abundance
    DA_DESeq2(object = ps_NF, pseudo_count = FALSE, design = ~ group, contrast =
                  c("group", "B", "A"), norm = "poscounts")

[Package benchdamic version 1.0.0 Index]