Plot a heatmap of the gene signature on the data

Source: R/gs_heatmap.R

Plot a heatmap for the selected gene signature on the provided data, with the possibility to compactly display also DE only genes

gs_heatmap(
  se,
  res_de,
  res_enrich,
  annotation_obj = NULL,
  gtl = NULL,
  geneset_id = NULL,
  genelist = NULL,
  FDR = 0.05,
  de_only = FALSE,
  cluster_rows = TRUE,
  cluster_columns = FALSE,
  center_mean = TRUE,
  scale_row = FALSE,
  winsorize_threshold = NULL,
  anno_col_info = NULL,
  plot_title = 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

geneset_id

Character specifying the gene set identifier to be plotted

genelist

A vector of character strings, specifying the identifiers contained in the row names of the se input object.

FDR

Numeric value, specifying the significance level for thresholding adjusted p-values. Defaults to 0.05.

de_only

Logical, whether to include only differentially expressed genes in the plot

cluster_rows

Logical, determining if rows should be clustered, as specified by ComplexHeatmap::Heatmap()

cluster_columns

Logical, determining if columns should be clustered, as specified by ComplexHeatmap::Heatmap()

center_mean

Logical, whether to perform mean centering on the row-wise

scale_row

Logical, whether to standardize by row the expression values

winsorize_threshold

Numeric value, to be applied as value to winsorize the extreme values of the heatmap. Should be a positive number. Defaults to NULL, which corresponds to not applying any winsorization. Suggested values: enter 2 or 3 if using row-standardized values (scale_row is TRUE), or visually inspect the range of the values if using simply mean centered values.

anno_col_info

A character vector of names in colData(dds) to use for decorating the heatmap as annotation.

plot_title

Character string, to specify the title of the plot, displayed over the heatmap. If left to NULL as by default, it tries to use the information on the geneset identifier provided

...

Additional arguments passed to other methods, e.g. in the call to ComplexHeatmap::Heatmap()

Value

A plot returned by the ComplexHeatmap::Heatmap() 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)
#> using counts and average transcript lengths from tximeta
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)
#> using 'avgTxLength' from assays(dds), correcting for library size

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)
)
#> 'select()' returned 1:many mapping between keys and columns

# 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)
#> Found 500 gene sets in `topGOtableResult` object.
#> Converting for usage in GeneTonic...
res_enrich <- get_aggrscores(res_enrich, res_de, anno_df)

gs_heatmap(vst_macrophage,
  res_de,
  res_enrich,
  anno_df,
  geneset_id = res_enrich$gs_id[1],
  cluster_columns = TRUE,
  anno_col_info = "condition"
)