Create Starlng Shiny app from a ClustAssess object — starlng_write_app

Runs the entire Starlng pipeline on a normalized gene expression matrix togheter with the output of the ClustAssess automatic pipeline and generates a folder with all necessary files to run a Shiny app.

Usage

starlng_write_app_clustassess(
  folder_path,
  expression_matrix,
  clustassess_object,
  metadata_df,
  stable_feature_type,
  stable_feature_set_size,
  stable_clustering_method,
  stable_n_clusters = NULL,
  use_all_genes = TRUE,
  app_title_name = "",
  learn_graph_parameters = list(nodes_per_log10_cells = 30, learn_graph_controls =
    list(eps = 1e-05, maxiter = 100)),
  gene_filtering_function = function(info_gene_df) {
     rownames(info_gene_df %>%
    dplyr::filter(.data$morans_I > 0.1, .data$q_value < 0.05))
 },
  clustering_parameters = list(n_neighbours = seq(from = 5, to = 50, by = 5), graph_type
    = "snn", prune_value = -1, resolutions = list(RBConfigurationVertexPartition =
    seq(from = 0.1, to = 2, by = 0.1), RBERVertexPartition = NULL,
    ModularityVertexPartition = NULL), number_iterations = 5, number_repetitions = 100),
  ecc_threshold = 0.9,
  freq_threshold = 30,
  enrichment_organism = "hsapiens",
  save_entire_monocle = TRUE,
  discrete_colours = list(),
  continuous_colours = list(),
  max_n_colors = 40,
  verbose = FALSE,
  compression_level = 7,
  chunk_size = 100,
  nthreads = 1
)

Arguments

folder_path: Check starlng_write_app_monocle() documentation.
expression_matrix: A normalized gene by cell expression matrix. The matrix should have the rownames and the colnames defined.
clustassess_object: The output of the automatic pipeline of the ClustAssess package.
metadata_df: A data frame with cell metadata specific to the experiment. If NULL, the function will create a one-group metadata with the name "one_level".
stable_feature_type: The feature type that is chosen from the ClustAssess analysis.
stable_feature_set_size: The size of the feature set that is chosen from the ClustAssess analysis.
stable_clustering_method: The clustering method that is chosen from the ClustAssess analysis.
stable_n_clusters: The number of clusters that is chosen from the ClustAssess analysis. If NULL, the function will use all the clusters that are obtained. Defaults to NULL.
use_all_genes: If TRUE, uses all the genes in the expression matrix. If FALSE, uses only the stable feature set as determined by ClustAssess. Defaults to TRUE.
app_title_name: Check starlng_write_app_monocle() documentation.
learn_graph_parameters: Check starlng_write_app_monocle() documentation.
gene_filtering_function: Check starlng_write_app_monocle() documentation.
clustering_parameters: Check starlng_write_app_monocle() documentation.
ecc_threshold: Check starlng_write_app_monocle() documentation.
freq_threshold: Check starlng_write_app_monocle() documentation.
enrichment_organism: Check starlng_write_app_monocle() documentation.`
save_entire_monocle: Check starlng_write_app_monocle() documentation.
discrete_colours: Check starlng_write_app_monocle() documentation.
continuous_colours: Check starlng_write_app_monocle() documentation.
max_n_colors: Check starlng_write_app_monocle() documentation.
verbose: Check starlng_write_app_monocle() documentation.
compression_level: Check starlng_write_app_monocle() documentation.
chunk_size: Check starlng_write_app_monocle() documentation.
nthreads: Check starlng_write_app_monocle() documentation.

Note

For more details about the other parameters and the content of the shiny folder, consult the documentation of the starlng_write_app_monocle() function.