extractStatistics: extractStatistics

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

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", "CSS"))
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", "CSS"))
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"
)

mcalgaro93/benchdamic documentation built on Nov. 28, 2022, 6:55 p.m.