extractDA: extractDA

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

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", "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)
# 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
)

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