plot_weights: Plot distribution of feature weights (weights)
In MOFA2: Multi-Omics Factor Analysis v2

Description Usage Arguments Value Examples

An important step to annotate factors is to visualise the corresponding feature weights.
This function plots all weights for a given latent factor and view, labeling the top ones.
In contrast, the function plot_top_weights displays only the top features with highest loading.

plot_weights(
  object,
  view = 1,
  factors = 1,
  nfeatures = 10,
  color_by = NULL,
  shape_by = NULL,
  abs = FALSE,
  manual = NULL,
  color_manual = NULL,
  scale = TRUE,
  dot_size = 1,
  text_size = 5,
  legend = TRUE,
  return_data = FALSE
)

`object`	a `MOFA` object.
`view`	a string with the view name, or an integer with the index of the view.
`factors`	character vector with the factor name(s), or numeric vector with the index of the factor(s).
`nfeatures`	number of top features to label.
`color_by`	specifies groups or values (either discrete or continuous) used to color the dots (features). This can be either: (default) the string "group": in this case, the plot will color the dots with respect to their predefined groups. a character giving the name of a feature that is present in the input data a character giving the same of a column in the features metadata slot a vector of the same length as the number of features specifying the value for each feature a dataframe with two columns: "feature" and "color"
`shape_by`	specifies groups or values (only discrete) used to shape the dots (features). This can be either: (default) the string "group": in this case, the plot will shape the dots with respect to their predefined groups. a character giving the name of a feature that is present in the input data a character giving the same of a column in the features metadata slot a vector of the same length as the number of features specifying the value for each feature a dataframe with two columns: "feature" and "shape"
`abs`	logical indicating whether to take the absolute value of the weights.
`manual`	A nested list of character vectors with features to be manually labelled (see the example for details).
`color_manual`	a character vector with colors, one for each element of 'manual'
`scale`	logical indicating whether to scale all weights from -1 to 1 (or from 0 to 1 if abs=TRUE).
`dot_size`	numeric indicating the dot size.
`text_size`	numeric indicating the text size.
`legend`	logical indicating whether to add legend.
`return_data`	logical indicating whether to return the data frame to plot instead of plotting

A ggplot object or a data.frame if return_data is TRUE

# Using an existing trained model on simulated data
file <- system.file("extdata", "model.hdf5", package = "MOFA2")
model <- load_model(file)

# Plot distribution of weights for Factor 1 and View 1
plot_weights(model, view = 1, factors = 1)

# Plot distribution of weights for Factors 1 to 3 and View 1
plot_weights(model, view = 1, factors = 1:3)

# Take the absolute value and highlight the top 10 features
plot_weights(model, view = 1, factors = 1, nfeatures = 10, abs = TRUE)

# Change size of dots and text
plot_weights(model, view = 1, factors = 1, text_size = 5, dot_size = 1)