plot_factor {MOFA2} | R Documentation |
Beeswarm plot of the latent factor values.
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 )
object |
a trained |
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:
|
color_by |
specifies color of samples. This can be either:
|
shape_by |
specifies shape of samples. This can be either:
|
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 |
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). |
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.
Returns a ggplot2
# 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)