addKnowledge {benchdamic} | R Documentation

## addKnowledge

### Description

Add a priori knowledge for each feature tested by a method.

### Usage

    addKnowledge(method, priorKnowledge, enrichmentCol, namesCol = NULL)

### Arguments

`method` | Output of differential abundance detection method in which DA information is extracted by the `getDA` function.
`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`).

### Value

A `data.frame` with a new column containing information for enrichment analysis.

### See Also

`createEnrichment`.

### 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")

[Package benchdamic version 1.0.0 Index]