plot_data_heatmap: Plot heatmap of relevant features

In bioFAM/MOFA2: Multi-Omics Factor Analysis v2

Plot heatmap of relevant features

Description

Function to plot a heatmap of the data for relevant features, typically the ones with high weights.

Usage

plot_data_heatmap(
  object,
  factor,
  view = 1,
  groups = "all",
  features = 50,
  annotation_features = NULL,
  annotation_samples = NULL,
  transpose = FALSE,
  imputed = FALSE,
  denoise = FALSE,
  max.value = NULL,
  min.value = NULL,
  ...
)

Arguments

object	a MOFA object.
factor	a string with the factor name, or an integer with the index of the factor.
view	a string with the view name, or an integer with the index of the view. Default is the first view.
groups	groups to plot. Default is "all".
features	if an integer (default), the total number of features to plot based on the absolute value of the weights. If a character vector, a set of manually defined features.
annotation_features	annotation metadata for features (rows). Either a character vector specifying columns in the feature metadata, or a data.frame that will be passed to pheatmap as annotation_col
annotation_samples	annotation metadata for samples (columns). Either a character vector specifying columns in the sample metadata, or a data.frame that will be passed to pheatmap as annotation_row
transpose	logical indicating whether to transpose the heatmap. Default corresponds to features as rows and samples as columns.
imputed	logical indicating whether to plot the imputed data instead of the original data. Default is FALSE.
denoise	logical indicating whether to plot a denoised version of the data reconstructed using the MOFA factors.
max.value	numeric indicating the maximum value to display in the heatmap (i.e. the matrix values will be capped at max.value ).
min.value	numeric indicating the minimum value to display in the heatmap (i.e. the matrix values will be capped at min.value ). See predict. Default is FALSE.
...	further arguments that can be passed to pheatmap

Details

One of the first steps for the annotation of a given factor is to visualise the corresponding weights, using for example plot_weights or plot_top_weights.
However, one might also be interested in visualising the direct relationship between features and factors, rather than looking at "abstract" weights.
This function generates a heatmap for selected features, which should reveal the underlying pattern that is captured by the latent factor.
A similar function for doing scatterplots rather than heatmaps is plot_data_scatter.

Value

A pheatmap object

Examples

# Using an existing trained model
file <- system.file("extdata", "model.hdf5", package = "MOFA2")
model <- load_model(file)
plot_data_heatmap(model, factor = 1, show_rownames = FALSE, show_colnames = FALSE)

bioFAM/MOFA2 documentation built on Feb. 1, 2024, 6:41 a.m.