Visualize MOFA multicellular model — plot_MOFA

Plots a heatmap with factor scores annotated by sample's information and factor's statistics

Usage

plot_MOFA_hmap(
  model,
  group = FALSE,
  metadata,
  sample_id_column = "sample",
  sample_anns,
  assoc_list = NULL,
  col_rows = NULL
)

Arguments

model: A MOFA2 model.
group: Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided.
metadata: A data frame containing the annotations of the samples included in the MOFA model.
sample_id_column: A string character that refers to the column in metadata where the sample identifier is located.
sample_anns: A vector containing strings that refer to the columns in metadata to be used to annotate samples
assoc_list: A named list collecting results of MOFAcellulaR::get_associations()
col_rows: A named list of lists containing at the first index, all sample_anns used, and at the second index, all levels of an annotation within the first index. As required by ComplexHeatmap::rowAnnotation col parameter.

Value

A dataframe in a tidy format containing the manifold and the scatter plot

Details

This function summarizes a MOFA2 model by plotting and clustering the factor scores across samples. Additionally, it allows you to annotate each sample with categorical or continous variables. Finally, for each factor, the amount of explained variance captured for each cell type is shown. If provided, summary of the association statistics and sample variables can be provided

Examples

inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR")
model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5"))
#> Warning: Factor(s) 1 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.
metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds"))
metadata$var <- stats::rnorm(nrow(metadata))

categorical_assoc <- get_associations(model = model,
                                      metadata = metadata,
                                      sample_id_column = "sample",
                                      test_variable = "patient_group",
                                      test_type = "categorical",
                                      group = FALSE)

continuous_assoc <- get_associations(model = model,
                                     metadata = metadata,
                                     sample_id_column = "sample",
                                     test_variable = "var",
                                     test_type = "continous",
                                     group = FALSE)


assoc_list = list("categorical" = categorical_assoc, "continous" = continuous_assoc)

plot_MOFA_hmap(model = model,
               group = FALSE,
               metadata = metadata,
               sample_id_column = "sample",
               sample_anns = c("patient_group", "batch", "var"),
               assoc_list = assoc_list)