gs_scoresheat {GeneTonic} | R Documentation

## Plots a matrix of geneset scores

### Description

Plots a matrix of geneset Z scores, across all samples

### Usage

    gs_scoresheat(
      mat,
      n_gs = nrow(mat),
      gs_ids = NULL,
      clustering_distance_rows = "euclidean",
      clustering_distance_cols = "euclidean",
      cluster_rows = TRUE,
      cluster_cols = TRUE
    )

### Arguments

`mat` | A matrix, e.g. returned by the `gs_scores()` function
`n_gs` | Integer value, corresponding to the maximal number of gene sets to be displayed.
`gs_ids` | Character vector, containing a subset of `gs_id` as they are available in `res_enrich`. Lists the gene sets to be displayed.
`clustering_distance_rows` | Character, a distance measure used in clustering rows
`clustering_distance_cols` | Character, a distance measure used in clustering columns
`cluster_rows` | Logical, determining if rows should be clustered
`cluster_cols` | Logical, determining if columns should be clustered

### Value

A `ggplot` object

### See Also

`gs_scores()` computes the scores plotted by this function

### 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[1:30, ],
      anno_df
    )
    gs_scoresheat(scores_mat,
      n_gs = 30
    )

[Package GeneTonic version 1.6.4 Index]