getDA {benchdamic} | R Documentation

## getDA

### Description

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

### Usage

    getDA(
      method,
      slot = "pValMat",
      colName = "rawP",
      type = "pvalue",
      direction = NULL,
      threshold_pvalue = 1,
      threshold_logfc = 0,
      top = NULL,
      verbose = FALSE
    )

### Arguments

`method` | Output of a differential abundance detection method. `pValMat`, `statInfo` matrices, and method's `name` must be present (See vignette for detailed information).
`slot` | The slot name where to extract values (default `slot = "pValMat"`).
`colName` | The column name of the slot where to extract values (default `colName = "rawP"`).
`type` | The value type of the column selected where to extract values. Two values are possible: `"pvalue"` or `"logfc"` (default `type = "pvalue"`).
`direction` | `statInfo`'s column name containing information about the signs of differential abundance (usually log fold changes) (default `direction = NULL`).
`threshold_pvalue` | Threshold value 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` | Threshold value 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:

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

- `direction` which is present if `method` was a `data.frame` object, 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 `method` object was a vector, `"UP Abundant"`, `"DOWN Abundant"`, or `"non-DA"` if `method` was a `data.frame`.

### See Also

`getStatistics`, `extractDA`

### Examples

    data("ps_plaque_16S")
    # Add scaling factors
    ps_plaque_16S <- norm_edgeR(object = ps_plaque_16S, method = "TMM")
    # DA analysis
    da.limma <- DA_limma(
        object = ps_plaque_16S,
        design = ~ 1 + HMP_BODY_SUBSITE,
        coef = 2,
        norm = "TMM"
    )
    # features with p-value < 0.1 as DA
    getDA(
        method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue",
        direction = NULL, threshold_pvalue = 0.1, threshold_logfc = 0,
        top = NULL
    )
    # top 10 feature with highest logFC are DA
    getDA(
        method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue",
        direction = "logFC", threshold_pvalue = 1, threshold_logfc = 0, top = 10
    )
    # features with p-value < 0.1 and |logFC| > 1 are DA
    getDA(
        method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue",
        direction = "logFC", threshold_pvalue = 0.1, threshold_logfc = 1, top =
            NULL
    )
    # top 10 features with |logFC| > 1 are DA
    getDA(
        method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue",
        direction = "logFC", threshold_pvalue = 1, threshold_logfc = 1, top = 10
    )

[Package benchdamic version 1.0.0 Index]