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Plot correlation heatmap

Source: R/plot_heatmap.R
plot_corr_heatmap.Rd

The first argument can be a multiOmicDataset object (moo) or a data.frame containing counts. For a moo, choose which counts slot to use with count_type & (optionally) sub_count_type. For a data.frame, you must also set sample_metadata. All other arguments are optional.

Usage

plot_corr_heatmap(moo_counts, ...)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot

...

arguments forwarded to method plot_corr_heatmap_dat

Value

heatmap from ComplexHeatmap::Heatmap()

Methods

link to docsclass
plot_corr_heatmap_moomultiOmicDataSet
plot_corr_heatmap_datdata.frame

Method Usage

# multiOmicDataSet
plot_corr_heatmap(moo_counts,
  count_type,
  sub_count_type = NULL,
  ...)

# dataframe
plot_corr_heatmap(moo_counts,
  sample_metadata,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = "Label",
  color_values = c(
    "#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"
  ))

See also

Other plotters: plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), print_or_save_plot()

Other heatmaps: plot_expr_heatmap(), plot_expr_heatmap_dat

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

Examples

# plot correlation heatmap for a counts slot in a multiOmicDataset Object
moo <- multiOmicDataSet(
  sample_metadata = as.data.frame(nidap_sample_metadata),
  anno_dat = data.frame(),
  counts_lst = list("raw" = as.data.frame(nidap_raw_counts))
)
p <- plot_corr_heatmap(moo, count_type = "raw")

# plot correlation heatmap for a counts dataframe
plot_corr_heatmap(
  moo@counts$raw,
  sample_metadata = moo@sample_meta,
  sample_id_colname = "Sample",
  feature_id_colname = "Gene",
  group_colname = "Group",
  label_colname = "Label"
)