getPositives: getPositives

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

getPositives

Description

Inspect the list of p-values or/and log fold changes from the output of a differential abundance detection method and count the True Positives (TP) and the False Positives (FP).

Usage

getPositives(method, enrichmentCol, TP, FP)

Arguments

method	Output of differential abundance detection method in which DA information is extracted by the getDA function, information related to enrichment is appropriately added through the addKnowledge function and the Fisher exact tests is performed for the contingency tables by the enrichmentTests function.
enrichmentCol	name of the column containing information for enrichment analysis.
TP	A list of length-2 vectors. The entries in the vector are the direction ("UP Abundant", "DOWN Abundant", or "non-DA") in the first position, and the level of the enrichment variable (enrichmentCol) which is expected in that direction, in the second position.
FP	A list of length-2 vectors. The entries in the vector are the direction ("UP Abundant", "DOWN Abundant", or "non-DA") in the first position, and the level of the enrichment variable (enrichmentCol) which is not expected in that direction, in the second position.

Value

A named vector containing the number of TPs and FPs.

See Also

createPositives.

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"])

# DA Analysis
# Add scaling factors
ps_plaque_16S <- norm_edgeR(object = ps_plaque_16S, method = "TMM")
# DA analysis
da.limma <- DA_limma(
    object = ps_plaque_16S,
    design = ~ 1 + HMP_BODY_SUBSITE,
    coef = 2,
    norm = "TMM"
)

DA <- getDA(
    method = da.limma, slot = "pValMat", colName = "adjP",
    type = "pvalue", direction = "logFC", threshold_pvalue = 0.05,
    threshold_logfc = 1, top = NULL
)
# Add a priori information
DA_info <- addKnowledge(
    method = DA, priorKnowledge = priorInfo,
    enrichmentCol = "Type", namesCol = "newNames"
)
# Create contingency tables and compute F tests
DA_info_enriched <- enrichmentTest(
    method = DA_info, enrichmentCol = "Type",
    alternative = "greater"
)
# Count True and False Positives
DA_TP_FP <- getPositives(
    method = DA_info_enriched, enrichmentCol = "Type",
    TP = list(c("UP Abundant", "Aerobic"), c("DOWN Abundant", "Anaerobic")),
    FP = list(c("UP Abundant", "Anaerobic"), c("DOWN Abundant", "Aerobic"))
)

mcalgaro93/benchdamic documentation built on March 10, 2024, 10:40 p.m.