plot_data_vs_cov: Scatterplots of feature values against sample covariates

In bioFAM/MOFA2: Multi-Omics Factor Analysis v2

Scatterplots of feature values against sample covariates

Description

Function to do a scatterplot of features against sample covariate values.

Usage

plot_data_vs_cov(
  object,
  covariate = 1,
  warped = TRUE,
  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 = FALSE,
  lm_per_group = FALSE,
  imputed = FALSE,
  return_data = FALSE
)

Arguments

object	a MOFA object using MEFISTO.
covariate	string with the covariate name or a samples_metadata column, or an integer with the index of the covariate
warped	logical indicating whether to show the aligned covariate (default: TRUE), only relevant if warping has been used to align multiple sample groups
factor	string with the factor name, or an integer with the index of the factor to take top features from
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 (given by highest weights). If a character vector, a set of manually-defined features.
sign	can be 'positive', 'negative' or 'all' (default) to show only features with highest 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, inferred 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
return_data	logical indicating whether to return a data frame instead of a plot

Details

One of the first steps for the annotation of factors is to visualise the weights using plot_weights or plot_top_weights and inspect the relationshio of the factor to the covariate(s) using plot_factors_vs_cov. However, one might also be interested in visualising the direct relationship between features and covariate(s), rather than looking at "abstract" weights and possibly look at the interpolated and extrapolated values by setting imputed to True.

Value

Returns a ggplot2 object or the underlying dataframe if return_data is set to TRUE.

Examples

# Using an existing trained model
file <- system.file("extdata", "MEFISTO_model.hdf5", package = "MOFA2")
model <- load_model(file)
plot_data_vs_cov(model, factor = 3, features = 2)

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