plot_factor {MOFA2} | R Documentation

## Beeswarm plot of factor values

### Description

Beeswarm plot of the latent factor values.

### Usage

    plot_factor(
      object,
      factors = 1,
      groups = "all",
      group_by = "group",
      color_by = "group",
      shape_by = NULL,
      add_dots = TRUE,
      dot_size = 2,
      dot_alpha = 1,
      add_violin = FALSE,
      violin_alpha = 0.5,
      color_violin = TRUE,
      add_boxplot = FALSE,
      boxplot_alpha = 0.5,
      color_boxplot = TRUE,
      show_missing = TRUE,
      scale = FALSE,
      dodge = FALSE,
      color_name = "",
      shape_name = "",
      stroke = NULL,
      legend = TRUE,
      rasterize = FALSE
    )

### Arguments

`object` | a trained `MOFA` object.
`factors` | character vector with the factor names, or numeric vector with the indices of the factors to use, or "all" to plot all factors.
`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.
`group_by` | specifies grouping of samples: (default) the string "group": in this case, the plot will color samples 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 name of a column in the sample metadata slot a vector of the same length as the number of samples specifying the value for each sample.
`color_by` | specifies color of samples. 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 name of a column in the sample metadata slot a vector of the same length as the number of samples specifying the value for each sample.
`shape_by` | specifies shape of samples. 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 name of a column in the sample metadata slot a vector of the same length as the number of samples specifying the value for each sample.
`add_dots` | logical indicating whether to add dots.
`dot_size` | numeric indicating dot size.
`dot_alpha` | numeric indicating dot transparency.
`add_violin` | logical indicating whether to add violin plots
`violin_alpha` | numeric indicating violin plot transparency.
`color_violin` | logical indicating whether to color violin plots.
`add_boxplot` | logical indicating whether to add box plots
`boxplot_alpha` | numeric indicating boxplot transparency.
`color_boxplot` | logical indicating whether to color box plots.
`show_missing` | logical indicating whether to remove samples for which `shape_by` or `color_by` is missing.
`scale` | logical indicating whether to scale factor values.
`dodge` | logical indicating whether to dodge the dots (default is FALSE).
`color_name` | name for color legend (usually only used if color_by is not a character itself).
`shape_name` | name for shape legend (usually only used if shape_by is not a character itself).
`stroke` | numeric indicating the stroke size (the black border around the dots).
`legend` | logical indicating whether to add a legend to the plot (default is TRUE).
`rasterize` | logical indicating whether to rasterize the plot (default is FALSE).

### Details

One of the main steps for the annotation of factors is to visualise and color them using known covariates or phenotypic data.
This function generates a Beeswarm plot of the sample values in a given latent factor.
Similar functions are `plot_factors` for doing scatter plots.

### Value

Returns a `ggplot2`

### Examples

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

    # Plot Factors 1 and 2 and colour by "group"
    plot_factor(model, factors = c(1,2), color_by="group")

    # Plot Factor 3 and colour by the value of a specific feature
    plot_factor(model, factors = 3, color_by="feature_981_view_1")

    # Add violin plots
    plot_factor(model, factors = c(1,2), color_by="group", add_violin = TRUE)

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

[Package MOFA2 version 1.0.1 Index]