Sifting network

network_sift(
  network_table,
  matrix = NULL,
  meta_data = NULL,
  pseudotime_column = NULL,
  method = c("entropy", "max"),
  entropy_method = c("Shannon", "Renyi"),
  effective_entropy = FALSE,
  shuffles = 100,
  entropy_nboot = 300,
  lag_value = 1,
  entropy_p_value = 0.05,
  cores = 1,
  verbose = TRUE
)

Arguments

network_table: The weight data table of network.
matrix: The expression matrix.
meta_data: The meta data for cells or samples.
pseudotime_column: The column of pseudotime.
method: The method used for filter edges. Could be choose entropy or max.
entropy_method: If setting method to entropy, could be choose Shannon or Renyi to compute entropy.
effective_entropy: Default is FALSE. Logical value, using effective entropy to filter weights or not.
shuffles: Default is 100. The number of shuffles used to calculate the effective transfer entropy.
entropy_nboot: Default is 300. The number of bootstrap replications for each direction of the estimated transfer entropy.
lag_value: Default is 1. Markov order of gene expression values, i.e. the number of lagged values affecting the current value of gene expression values.
entropy_p_value: P value used to filter edges by entropy.
cores: The number of cores to use for parallelization with foreach, default is 1.
verbose: Logical value, default is TRUE, whether to print progress messages.

Value

Sifted network table

Examples

if (FALSE) { # \dontrun{
data("example_matrix")
data("example_meta_data")
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
network_table_sifted <- network_sift(network_table)
network_table_sifted_entropy <- network_sift(
  network_table,
  matrix = example_matrix,
  meta_data = example_meta_data,
  pseudotime_column = "pseudotime",
  lag_value = 2,
  shuffles = 0,
  entropy_nboot = 0
)

plot_network_heatmap(
  example_ground_truth[, 1:3],
  heatmap_title = "Ground truth",
  show_names = TRUE,
  rect_color = "gray70"
)
plot_network_heatmap(
  network_table,
  heatmap_title = "Raw",
  show_names = TRUE,
  rect_color = "gray70"
)
plot_network_heatmap(
  network_table_sifted,
  heatmap_title = "Filtered",
  show_names = TRUE,
  rect_color = "gray70"
)
plot_network_heatmap(
  network_table_sifted_entropy,
  heatmap_title = "Filtered by entropy",
  show_names = TRUE,
  rect_color = "gray70"
)

calculate_auc(
  network_table,
  example_ground_truth,
  plot = TRUE
)
calculate_auc(
  network_table_sifted,
  example_ground_truth,
  plot = TRUE
)
calculate_auc(
  network_table_sifted_entropy,
  example_ground_truth,
  plot = TRUE
)
} # }