Create monocle object based on expression matrix — create_monocle

Use the expression matrix and the output of the automatic_stability_assessment function to create a Monocle object which has the stable numver of clusters.

create_monocle_object(
  normalized_expression_matrix,
  count_matrix = NULL,
  clustassess_object,
  metadata,
  stable_feature_type,
  stable_feature_set_size,
  stable_clustering_method,
  stable_n_clusters = NULL,
  gene_names = NULL,
  cell_names = NULL,
  use_all_genes = FALSE,
  is_object_from_shinyapp = FALSE
)

Arguments

normalized_expression_matrix: The normalized expression matrix having genes on rows and cells on columns.
count_matrix: The count matrix having genes on rows and cells on columns. If NULL, the normalized_expression_matrix will be used.
clustassess_object: The output of the automatic_stability_assessment.
metadata: The metadata dataframe having the cell names as rownames. If NULL, a dataframe with a single column named identical_ident will be created.
stable_feature_type: The feature type which leads to stable clusters.
stable_feature_set_size: The feature size which leads to stable clusters.
stable_clustering_method: The clustering method which leads to stable clusters.
stable_n_clusters: The number of clusters that are stable. If NULL, all the clusters will be provided. Defaults to NULL.
gene_names: The gene names that will be used in the Monocle object. If NULL, the rownames of the normalized_expression_matrix will be used. Defaults to NULL.
cell_names: The cell names that will be used in the Monocle object. If NULL, the colnames of the normalized_expression_matrix will be used. Defaults to NULL.
use_all_genes: A boolean value indicating if the expression matrix should be truncated to the genes used in the stability assessment. Defaults to FALSE.
is_object_from_shinyapp: A boolean value indicating if the ClustAssess object is extracted from a ShinyApp directory, or is a direct output of the automatic_stability_assessment function. Defaults to FALSE.

Value

A Monocle object of the expression matrix, having the stable number of clusters identified by ClustAssess.

Examples

set.seed(2024)
# create an already-transposed artificial expression matrix
expr_matrix <- matrix(
    c(runif(20 * 10), runif(30 * 10, min = 3, max = 4)),
    nrow = 10, byrow = FALSE
)
colnames(expr_matrix) <- as.character(seq_len(ncol(expr_matrix)))
rownames(expr_matrix) <- paste("feature", seq_len(nrow(expr_matrix)))

autom_object <- automatic_stability_assessment(
    expression_matrix = expr_matrix,
    n_repetitions = 3,
    n_neigh_sequence = c(5),
    resolution_sequence = c(0.1, 0.5),
    features_sets = list(
        "set1" = rownames(expr_matrix)
    ),
    steps = list(
        "set1" = c(5, 7)
    ),
    umap_arguments = list(
        # the following parameters have been modified
        # from the default values to ensure that the function
        # will run under 5 seconds
        n_neighbors = 3,
        approx_pow = TRUE,
        n_epochs = 0,
        init = "random",
        min_dist = 0.3
    ),
    n_top_configs = 1,
    algorithms_clustering_assessment = 1,
    save_temp = FALSE,
    verbose = FALSE
)


# uncomment to create the monocle object
# mon_obj <- create_monocle_object(
# normalized_expression_matrix = expr_matrix,
# clustassess_object = autom_object,
# metadata = NULL,
# stable_feature_type = "set1",
# stable_feature_set_size = "5",
# stable_clustering_method = "Louvain"
# )