get_aggrscores {GeneTonic} | R Documentation

## Compute aggregated scores for gene sets

### Description

Computes for each gene set in the `res_enrich` object a Z score and an aggregated score (using the log2FoldChange values, provided in the `res_de`)

### Usage

    get_aggrscores(res_enrich, res_de, annotation_obj, gtl = NULL, aggrfun = mean)

### Arguments

`res_enrich` | A `data.frame` object, storing the result of the functional enrichment analysis. See more in the main function, `GeneTonic()`, to check the formatting requirements (a minimal set of columns should be present).
`res_de` | A `DESeqResults` object.
`annotation_obj` | A `data.frame` object with the feature annotation information, with at least two columns, `gene_id` and `gene_name`.
`gtl` | A `GeneTonic`-list object, containing in its slots the arguments specified above: `dds`, `res_de`, `res_enrich`, and `annotation_obj` - the names of the list must be specified following the content they are expecting
`aggrfun` | Specifies the function to use for aggregating the scores for each term. Common values could be `mean` or `median`.

### Value

A `data.frame` with the same columns as provided in the input, with additional information on the `z_score` and the `aggr_score` for each gene set. This information is used by other functions such as `gs_volcano()` or `enrichment_map()`

### See Also

`gs_volcano()` and `enrichment_map()` make efficient use of the computed aggregated scores

### Examples

    library("macrophage")
    library("DESeq2")
    library("org.Hs.eg.db")
    library("AnnotationDbi")

    # dds object
    data("gse", package = "macrophage")
    dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
    rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
    dds_macrophage <- estimateSizeFactors(dds_macrophage)

    # annotation object
    anno_df <- data.frame(
      gene_id = rownames(dds_macrophage),
      gene_name = mapIds(org.Hs.eg.db,
        keys = rownames(dds_macrophage),
        column = "SYMBOL",
        keytype = "ENSEMBL"
      ),
      stringsAsFactors = FALSE,
      row.names = rownames(dds_macrophage)
    )

    # res object
    data(res_de_macrophage, package = "GeneTonic")
    res_de <- res_macrophage_IFNg_vs_naive

    # res_enrich object
    data(res_enrich_macrophage, package = "GeneTonic")
    res_enrich <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive)

    res_enrich <- get_aggrscores(
      res_enrich,
      res_de,
      anno_df
    )

[Package GeneTonic version 1.6.4 Index]