plot_data_heatmap: Plot heatmap of relevant features

Description Usage Arguments Details Value Examples

View source: R/plot_data.R

Description

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

Usage

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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

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# 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)

MOFA2 documentation built on Nov. 8, 2020, 7:28 p.m.