plot_data_heatmap: Plot heatmap of relevant features
In MOFA2: Multi-Omics Factor Analysis v2

Description Usage Arguments Details Value Examples

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

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,
  ...
)

`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`

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.

A pheatmap object

# 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)