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