plot_weights_scatter: Scatterplots of weights
In MOFA2: Multi-Omics Factor Analysis v2

Description Usage Arguments Details Value Examples

Scatterplot of the weights values for two factors

plot_weights_scatter(
  object,
  factors,
  view = 1,
  color_by = NULL,
  shape_by = NULL,
  dot_size = 1,
  name_color = "",
  name_shape = "",
  show_missing = TRUE,
  abs = FALSE,
  scale = TRUE,
  legend = TRUE
)

`object`	a trained `MOFA` object.
`factors`	a vector of length two with the factors to plot. Factors can be specified either as a characters using the factor names, or as numeric with the index of the factors
`view`	character vector with the voiew name, or numeric vector with the index of the view to use. Default is the first view.
`color_by`	specifies groups or values used to color the features. This can be either a character giving the same of a column in the feature metadata slot a vector specifying the value for each feature. a dataframe with two columns: "feature" and "color"
`shape_by`	specifies groups or values used to shape the features. This can be either a character giving the same of a column in the feature metadata slot a vector specifying the value for each feature. a dataframe with two columns: "feature" and "shape"
`dot_size`	numeric indicating dot size.
`name_color`	name for color legend (usually only used if color_by is not a character itself)
`name_shape`	name for shape legend (usually only used if shape_by is not a character itself)
`show_missing`	logical indicating whether to include dots for which `shape_by` or `color_by` is missing
`abs`	logical indicating whether to take the absolute value of the weights.
`scale`	logical indicating whether to scale all weights from -1 to 1 (or from 0 to 1 if `abs=TRUE`).
`legend`	logical indicating whether to add a legend to the plot (default is TRUE).

One of the first steps for the annotation of factors is to visualise and group/color them using known covariates such as phenotypic or clinical data. This method generates a single scatterplot for the combination of two latent factors.

Returns a ggplot2 object

# Using an existing trained model on simulated data
file <- system.file("extdata", "model.hdf5", package = "MOFA2")
model <- load_model(file)
plot_weights_scatter(model, factors = 1:2)