createConcordance {benchdamic} | R Documentation

## createConcordance

### Description

Compute the between and within method concordances comparing the lists of extracted statistics from the outputs of the differential abundance detection methods.

### Usage

    createConcordance(object, slot = "pValMat", colName = "rawP", type = "pvalue")

### 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"`).

### Value

A long format `data.frame` object with several columns:

- `comparison` which indicates the comparison number;

- `n_features` which indicates the total number of taxa in the comparison dataset;

- `method1` which contains the first method name;

- `method2` which contains the first method name;

- `rank`;

- `concordance` which is defined as the cardinality of the intersection of the top rank elements of each list, divided by rank, i.e. , (L_{1:rank} \bigcap M_{1:rank})/(rank), where L and M represent the lists of the extracted statistics of method1 and method2 respectively (averaged values between subset1 and subset2).

### See Also

`extractStatistics` and `areaCAT`.

### Examples

    data(ps_plaque_16S)

    # Balanced design for independent samples
    my_splits <- createSplits(
        object = ps_plaque_16S, varName =
        "HMP_BODY_SUBSITE", balanced = TRUE, N = 10 # N = 100 suggested
    )

    # Initialize some limma based methods
    my_limma <- set_limma(design = ~ HMP_BODY_SUBSITE, coef = 2,
        norm = c("TMM", "CSSmedian"))

    # Set the normalization methods according to the DA methods
    my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"),
        method = c("TMM", "median"))

    # Run methods on split datasets
    results <- runSplits(split_list = my_splits, method_list = my_limma,
        normalization_list = my_norm, object = ps_plaque_16S)

    # Concordance for p-values
    concordance_pvalues <- createConcordance(
        object = results, slot = "pValMat", colName = "rawP", type = "pvalue"
    )

    # Concordance for log fold changes
    concordance_logfc <- createConcordance(
        object = results, slot = "statInfo", colName = "logFC", type = "logfc"
    )

    # Concordance for log fold changes in the first method and p-values in the
    # other
    concordance_logfc_pvalues <- createConcordance(
        object = results, slot = c("statInfo", "pValMat"),
        colName = c("logFC", "rawP"), type = c("logfc", "pvalue")
    )

[Package benchdamic version 1.0.0 Index]