network_sift: Sifting network

In inferCSN: Inferring Cell-Specific Gene Regulatory Network

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
)

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

## Not run: 
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
)

## End(Not run)

inferCSN documentation built on Sept. 11, 2024, 9:32 p.m.