gs_scores {GeneTonic} | R Documentation

## Compute gene set scores

### Description

Compute gene set scores for each sample, by transforming the gene-wise change to a geneset-wise change

### Usage

    gs_scores(se, res_de, res_enrich, annotation_obj = NULL, gtl = NULL)

### Arguments

`se` | A `SummarizedExperiment` object, or an object derived from this class, such as a `DESeqTransform` object (variance stabilized transformed data, or regularized logarithm transformed), in where the transformation has been applied to make the data more homoscedastic and thus a better fit for visualization.
`res_de` | A `DESeqResults` object.
`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).
`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

### Value

A matrix with the geneset Z scores, e.g. to be plotted with `gs_scoresheat()`

### See Also

`gs_scoresheat()` plots these 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)

    vst_macrophage <- vst(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)

    scores_mat <- gs_scores(
      vst_macrophage,
      res_de,
      res_enrich[1:50, ],
      anno_df
    )

[Package GeneTonic version 1.6.4 Index]