pGRN: pGRN: creates gene regulatory network based on single cell...

In pGRN: Single-Cell RNA Sequencing Pseudo-Time Based Gene Regulatory Network Inference

pGRN: creates gene regulatory network based on single cell pseudotime information

Description

Given single cell matrix and pseudotime, construct gene regulatory network (GRN)

Usage

pGRN(
  expression_matrix,
  pseudotime_list,
  method = "DTW",
  slide_window_size = 20,
  slide_step_size = 10,
  centrality_degree_mod = "out",
  components_mod = "weak",
  network_min_genes = 10,
  quantile_cutoff = 5,
  order = 1,
  cores = 1
)

Arguments

expression_matrix	expression matrix data
pseudotime_list	list of pseudotime
method	method for GRN construction: DTW, granger
slide_window_size	sliding window size
slide_step_size	sliding window step size
centrality_degree_mod	(for DTW method) mode of centrality degree for popularity calculation
components_mod	(for DTW method) mode of sub-network extraction methods (weak or strong)
network_min_genes	minimal number of gene elements required for extracted sub-networks
quantile_cutoff	an integer value (1-99) for quantile cutoff
order	(for granger method) integer specifying the order of lags to include in the auxiliary regression
cores	number of cores for parallel computing

Value

a list of tabl_graph objects

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime

# try DTW method
nets <- pGRN(expression_matrix,
             pseudotime_list, 
             method= "DTW",
             quantile_cutoff=50,
             cores=1)
plot_network(nets[[1]])

# plot the network interactively
plot_network_i(nets[[1]])

pGRN documentation built on Jan. 18, 2023, 1:06 a.m.