plotPositives {benchdamic}

R Documentation

plotPositives

Description

Plot the difference between the number of true positives (TP) and false positives (FP) for each method and for each 'top' threshold provided by the createPositives() function.

Usage

plotPositives(positives, cols = NULL)

Arguments

positives	data.frame object produced by createPositives() function.
cols	named vector of cols (default cols = NULL).

Value

a ggplot2 object.

See Also

getPositives, 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")
ps_plaque_16S <- norm_CSS(object = ps_plaque_16S, method = "median")

# Perform DA analysis
Plaque_16S_DA <- list()
Plaque_16S_DA <- within(Plaque_16S_DA, {
    # DA analysis
    da.limma <- DA_limma(
        object = ps_plaque_16S,
        design = ~ 1 + HMP_BODY_SUBSITE,
        coef = 2,
        norm = "TMM"
    )
    da.limma.css <- DA_limma(
        object = ps_plaque_16S,
        design = ~ 1 + HMP_BODY_SUBSITE,
        coef = 2,
        norm = "CSSmedian"
    )
})

# Count TPs and FPs, from the top 1 to the top 20 features.
# As direction is supplied, features are ordered by "logFC" absolute values.
positives <- createPositives(
    object = Plaque_16S_DA,
    priorKnowledge = priorInfo, enrichmentCol = "Type",
    namesCol = "newNames", slot = "pValMat", colName = "rawP",
    type = "pvalue", direction = "logFC", threshold_pvalue = 1,
    threshold_logfc = 0, top = 1:20, alternative = "greater",
    verbose = FALSE,
    TP = list(c("DOWN Abundant", "Anaerobic"), c("UP Abundant", "Aerobic")),
    FP = list(c("DOWN Abundant", "Aerobic"), c("UP Abundant", "Anaerobic"))
)
# Plot the TP-FP differences for each threshold
plotPositives(positives = positives)

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