createEnrichment {benchdamic}

R Documentation

createEnrichment

Description

Create a data.frame object with several information to perform enrichment analysis.

Usage

createEnrichment(
  object,
  priorKnowledge,
  enrichmentCol,
  namesCol = NULL,
  slot = "pValMat",
  colName = "adjP",
  type = "pvalue",
  direction = NULL,
  threshold_pvalue = 1,
  threshold_logfc = 0,
  top = NULL,
  alternative = "greater",
  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).
priorKnowledge	data.frame (with feature names as row.names) containing feature level metadata.
enrichmentCol	name of the column containing information for enrichment analysis.
namesCol	name of the column containing new names for features (default namesCol = NULL).
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).
alternative	indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. Only used in the 2 by 2 case.
verbose	Boolean to display the kind of extracted values (default verbose = FALSE).

Value

a list of objects for each method. Each list contains:

• data a data.frame object with DA directions, statistics, and feature names;

• tables a list of 2x2 contingency tables;

• tests the list of Fisher exact tests' p-values for each contingency table;

• summaries a list with the first element of each contingency table and its p-value (for graphical purposes);

See Also

addKnowledge, extractDA, and enrichmentTest.

Examples

data("ps_plaque_16S")
data("microbial_metabolism")

# Extract genera from the phyloseq tax_table slot
genera <- phyloseq::tax_table(ps_plaque_16S)[, "GENUS"]
# Genera as rownames of microbial_metabolism data.frame
rownames(microbial_metabolism) <- microbial_metabolism$Genus
# Match OTUs to their metabolism
priorInfo <- data.frame(genera,
    "Type" =  microbial_metabolism[genera, "Type"])
# Unmatched genera becomes "Unknown"
unknown_metabolism <- is.na(priorInfo$Type)
priorInfo[unknown_metabolism, "Type"] <- "Unknown"
priorInfo$Type <- factor(priorInfo$Type)
# Add a more informative names column
priorInfo[, "newNames"] <- paste0(rownames(priorInfo), priorInfo[, "GENUS"])

# Add some normalization/scaling factors to the phyloseq object
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)

# Initialize some limma based methods
my_limma <- set_limma(design = ~ 1 + HMP_BODY_SUBSITE, coef = 2,
    norm = c("TMM", "CSSmedian"))

# Perform DA analysis
Plaque_16S_DA <- runDA(method_list = my_limma, object = ps_plaque_16S)

# Enrichment analysis
enrichment <- createEnrichment(object = Plaque_16S_DA,
    priorKnowledge = priorInfo, enrichmentCol = "Type", namesCol = "GENUS",
    slot = "pValMat", colName = "adjP", type = "pvalue", direction = "logFC",
    threshold_pvalue = 0.1, threshold_logfc = 1, top = 10, verbose = TRUE)

---