plot_data_scatter: Scatterplots of feature values against latent factors

In MOFA2: Multi-Omics Factor Analysis v2

Description

Function to do a scatterplot of features against factor values.

Usage

plot_data_scatter(
  object,
  factor = 1,
  view = 1,
  groups = "all",
  features = 10,
  sign = "all",
  color_by = "group",
  legend = TRUE,
  alpha = 1,
  shape_by = NULL,
  stroke = NULL,
  dot_size = 2.5,
  text_size = NULL,
  add_lm = TRUE,
  lm_per_group = TRUE,
  imputed = FALSE
)

Arguments

object	a MOFA object.
factor	string with the factor name, or an integer with the index of the factor.
view	string with the view name, or an integer with the index of the view. Default is the first view.
groups	groups to plot. Default is "all".
features	if an integer (default), the total number of features to plot. If a character vector, a set of manually-defined features.
sign	can be 'positive', 'negative' or 'all' (default) to show only positive, negative or all weights, respectively.
color_by	specifies groups or values (either discrete or continuous) used to color the dots (samples). This can be either: the string "group": dots are coloured 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 same 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. a dataframe with two columns: "sample" and "color"
legend	logical indicating whether to add a legend
alpha	numeric indicating dot transparency (default is 1).
shape_by	specifies groups or values (only discrete) used to shape the dots (samples). This can be either: the string "group": dots are shaped 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 same 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. a dataframe with two columns: "sample" and "shape"
stroke	numeric indicating the stroke size (the black border around the dots, default is NULL, infered automatically).
dot_size	numeric indicating dot size (default is 5).
text_size	numeric indicating text size (default is 5).
add_lm	logical indicating whether to add a linear regression line for each plot
lm_per_group	logical indicating whether to add a linear regression line separately for each group
imputed	logical indicating whether to include imputed measurements

Details

One of the first steps for the annotation of factors is to visualise the weights using plot_weights or plot_top_weights. However, one might also be interested in visualising the direct relationship between features and factors, rather than looking at "abstract" weights.
A similar function for doing heatmaps rather than scatterplots is plot_data_heatmap.

Value

A ggplot object

Examples

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

MOFA2 documentation built on Nov. 8, 2020, 7:28 p.m.