plot_factor: Beeswarm plot of factor values

In bioFAM/MOFA2: Multi-Omics Factor Analysis v2

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)

bioFAM/MOFA2 documentation built on June 12, 2024, 3:57 p.m.