inferCSN (version 1.2.0)

network_sift: Sifting network

Description

Sifting network

Usage

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
)

Value

A data table of regulator-target regulatory relationships. The data table has the three columns: regulator, target, and weight.

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. Whether to use effective entropy to filter weights.

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. Default is 0.05.

cores

The number of cores to use for parallelization with foreach::foreach. Default is 1.

verbose

Whether to print progress messages. Default is TRUE.

Examples

if (FALSE) {
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_metrics(
  network_table,
  example_ground_truth,
  return_plot = TRUE
)
calculate_metrics(
  network_table_sifted,
  example_ground_truth,
  return_plot = TRUE
)
calculate_metrics(
  network_table_sifted_entropy,
  example_ground_truth,
  return_plot = TRUE
)
}