extractStatistics {benchdamic} | R Documentation

## extractStatistics

### Description

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

### Usage

    extractStatistics(
      object,
      slot = "pValMat",
      colName = "rawP",
      type = "pvalue",
      direction = 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`).
`verbose` | Boolean to display the kind of extracted values (default `verbose = FALSE`).

### Value

A vector or a `data.frame` for each method. If `direction = NULL`, the `colname` column values, transformed according to `type` (not tranformed if `type = "pvalue"`, `-abs(value)` if `type = "logfc"`), of the `slot` are reported , otherwise the `direction` column of the `statInfo` matrix is added to the output.

### See Also

`getStatistics`

### 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)
    ### Extract statistics for concordance analysis:
    # Only p-values
    extracted_pvalues <- extractStatistics(
        object = Plaque_16S_DA, slot =
            "pValMat", colName = "rawP", type = "pvalue"
    )
    # Only transformed log fold changes -abs(logFC)
    extracted_abslfc <- extractStatistics(
        object = Plaque_16S_DA, slot =
            "statInfo", colName = "logFC", type = "logfc"
    )
    # Only transformed log fold changes for a method and p-values for the other
    extracted_abslfc_pvalues <- extractStatistics(
        object = Plaque_16S_DA,
        slot = c("statInfo", "pValMat"), colName = c("logFC", "rawP"), type =
            c("logfc", "pvalue")
    )
    ### Extract statistics for enrichment analysis:
    # p-values and log fold changes
    extracted_pvalues_and_lfc <- extractStatistics(
        object = Plaque_16S_DA,
        slot = "pValMat", colName = "rawP", type = "pvalue", direction = "logFC"
    )
    # transformed log fold changes and untouched log fold changes
    extracted_abslfc_and_lfc <- extractStatistics(
        object = Plaque_16S_DA,
        slot = "statInfo", colName = "logFC", type = "logfc", direction =
            "logFC"
    )
    # Only transformed log fold changes for a method and p-values for the other
    extracted_mix <- extractStatistics(
        object = Plaque_16S_DA,
        slot = c("statInfo", "pValMat"), colName = c("logFC", "rawP"), type =
            c("logfc", "pvalue"), direction = "logFC"
    )

[Package benchdamic version 1.0.0 Index]