plot_weights: Plot distribution of feature weights (weights)

In bioFAM/MOFA2: Multi-Omics Factor Analysis v2

Plot distribution of feature weights (weights)

Description

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.

Usage

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
)

Arguments

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

Value

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

Examples

# 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)

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