plot_factors {MOFA2} | R Documentation

## Scatterplots of two factor values

### Description

Scatterplot of the values of two latent factors.

### Usage

    plot_factors(
      object,
      factors = c(1, 2),
      groups = "all",
      show_missing = TRUE,
      scale = FALSE,
      color_by = NULL,
      shape_by = NULL,
      color_name = NULL,
      shape_name = NULL,
      dot_size = 1.5,
      alpha = 1,
      legend = TRUE,
      stroke = NULL,
      return_data = FALSE
    )

### 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
`groups` | character vector with the groups names, or numeric vector with the indices of the groups of samples to use, or "all" to use samples from all groups.
`show_missing` | logical indicating whether to include samples for which `shape_by` or `color_by` is missing
`scale` | logical indicating whether to scale factor values.
`color_by` | specifies groups or values used to color the samples. This can be either: (1) a character giving the name of a feature present in the training data. (2) a character giving the name of a column present in the sample metadata. (3) a vector of the name length as the number of samples specifying discrete groups or continuous numeric values.
`shape_by` | specifies groups or values used to shape the samples. This can be either: (1) a character giving the name of a feature present in the training data, (2) a character giving the name of a column present in the sample metadata. (3) a vector of the same length as the number of samples specifying discrete groups.
`color_name` | name for color legend.
`shape_name` | name for shape legend.
`dot_size` | numeric indicating dot size (default is 1.5).
`alpha` | numeric indicating dot transparency (default is 1).
`legend` | logical indicating whether to add legend.
`stroke` | numeric indicating the stroke size (the black border around the dots, default is NULL, infered automatically).
`return_data` | logical indicating whether to return the data frame to plot instead of plotting

### 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. TO-FINISH... `plot_factors` for doing Beeswarm plots for 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)

    # Scatterplot of factors 1 and 2
    plot_factors(model, factors = c(1,2))

    # Shape dots by a column in the metadata
    plot_factors(model, factors = c(1,2), shape_by="group")

    # Scale factor values from -1 to 1
    plot_factors(model, factors = c(1,2), scale = TRUE)

[Package MOFA2 version 1.0.1 Index]