extractDA {benchdamic} | R Documentation

## extractDA

### Description

Inspect the list of p-values or/and log fold changes from the output of differential abundance detection methods.

### Usage

    extractDA(
      object,
      slot = "pValMat",
      colName = "adjP",
      type = "pvalue",
      direction = NULL,
      threshold_pvalue = 1,
      threshold_logfc = 0,
      top = NULL,
      verbose = FALSE
    )

### Arguments

`object` | Output of differential abundance detection methods. `pValMat`, `statInfo` matrices, and method's `name` must be present (See vignette for detailed information).
`slot` | A character vector with 1 or number-of-methods-times repeats of the slot names where to extract values for each method (default `slot = "pValMat"`).
`colName` | A character vector with 1 or number-of-methods-times repeats of the column name of the slot where to extract values for each method (default `colName = "rawP"`).
`type` | A character vector with 1 or number-of-methods-times repeats of the value type of the column selected where to extract values for each method. Two values are possible: `"pvalue"` or `"logfc"` (default `type = "pvalue"`).
`direction` | A character vector with 1 or number-of-methods-times repeats of the `statInfo`'s column name containing information about the signs of differential abundance (usually log fold changes) for each method (default `direction = NULL`).
`threshold_pvalue` | A single or a numeric vector of thresholds for p-values. If present, features with p-values lower than `threshold_pvalue` are considered differentially abundant. Set `threshold_pvalue = 1` to not filter by p-values.
`threshold_logfc` | A single or a numeric vector of thresholds for log fold changes. If present, features with log fold change absolute values higher than `threshold_logfc` are considered differentially abundant. Set `threshold_logfc = 0` to not filter by log fold change values.
`top` | If not null, the `top` number of features, ordered by p-values or log fold change values, are considered as differentially abundant (default `top = NULL`).
`verbose` | Boolean to display the kind of extracted values (default `verbose = FALSE`).

### Value

A `data.frame` with several columns for each method:

- `stat` which contains the p-values or the absolute log fold change values;

- `direction` which is present if `direction` was supplied, it contains the information about directionality of differential abundance (usually log fold changes);

- `DA` which can contain several values according to thresholds and inputs. `"DA"` or `"non-DA"` if `direction = NULL`, `"UP Abundant"`, `"DOWN Abundant"`, or `"non-DA"` otherwise.

### See Also

`getDA`, `extractStatistics`

### Examples

    data("ps_plaque_16S")
    # Add scaling factors
    my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"),
        method = c("TMM", "median"))
    ps_plaque_16S <- runNormalizations(normalization_list = my_norm,
        object = ps_plaque_16S)
    # Perform DA analysis
    my_methods <- set_limma(design = ~ 1 + HMP_BODY_SUBSITE, coef = 2,
        norm = c("TMM", "CSSmedian"))
    Plaque_16S_DA <- runDA(method_list = my_methods, object = ps_plaque_16S)
    # Top 10 features (ordered by 'direction') are DA
    DA_1 <- extractDA(
        object = Plaque_16S_DA, slot = "pValMat", colName = "adjP",
        type = "pvalue", direction = "logFC", threshold_pvalue = 1,
        threshold_logfc = 0, top = 10
    )
    # Features with p-value < 0.05 and |logFC| > 1 are DA
    DA_2 <- extractDA(
        object = Plaque_16S_DA, slot = "pValMat", colName = "adjP",
        type = "pvalue", direction = "logFC", threshold_pvalue = 0.05,
        threshold_logfc = 1, top = NULL
    )

[Package benchdamic version 1.0.0 Index]