plot_weights_scatter {MOFA2} | R Documentation

## Scatterplots of weights

### Description

Scatterplot of the weights values for two factors

### Usage

    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
    )

### Arguments

`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).

### Details

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.

### Value

Returns a `ggplot2` object

### Examples

    # 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)

[Package MOFA2 version 1.0.1 Index]