NET_run: Single gene network approaches.

In ubioinformat/TraRe: Transcriptional Rewiring

Single gene network approaches.

Description

NET_run() generate a single GRN. NET_compute_graph_all_LASSO1se() defines the statistics of drivers and targets to be the Lasso method, choosing 1 standard error from the minimum RSS. NET_compute_graph_all_LASSOmin() uses Lasso method, choosing the minimum RSS point. NET_compute_graph_all_LM() uses a linear model and NET_compute_graph_all_VBSR() uses a Variational Bayes Spike Regression.

Usage

NET_run(
  lognorm_est_counts,
  target_filtered_idx,
  regulator_filtered_idx,
  nassay = 1,
  regulator = "regulator",
  graph_mode = c("VBSR", "LASSOmin", "LASSO1se", "LM"),
  FDR = 0.05,
  NrCores = 1
)

NET_compute_graph_all_LASSO1se(
  lognorm_est_counts,
  regulator_filtered_idx,
  target_filtered_idx,
  alpha = 1 - 1e-06,
  NrCores = 1
)

NET_compute_graph_all_LASSOmin(
  lognorm_est_counts,
  regulator_filtered_idx,
  target_filtered_idx,
  alpha = 1 - 1e-06,
  NrCores = 1
)

NET_compute_graph_all_LM(
  lognorm_est_counts,
  regulator_filtered_idx,
  target_filtered_idx,
  NrCores = 1
)

NET_compute_graph_all_VBSR(
  lognorm_est_counts,
  regulator_filtered_idx,
  target_filtered_idx,
  NrCores = 1
)

Arguments

lognorm_est_counts	Matrix of log-normalized estimated counts of the gene expression data (Nr Genes x Nr samples)
target_filtered_idx	Index array of the target genes on the lognorm_est_counts matrix if SummarizedExperiment object is not provided.
regulator_filtered_idx	Index array of the regulatory genes on the lognorm_est_counts matrix if SummarizedExperiment object is not provided.
nassay	if SummarizedExperiment object is passed as input to lognorm_est_counts, name of the assay containing the desired matrix. Default: 1 (first item in assay's list).
regulator	if SummarizedExperiment object is passed as input to lognorm_est_counts, name of the rowData() variable to build target_filtered_idx and regulator_filtered_idx. This variable must be one for driver genes and zero for target genes. Default: 'regulator'
graph_mode	Chosen method(s) to generate the edges in the bipartite graph. The available options are 'VBSR', 'LASSOmin', 'LASSO1se' and 'LM'. By default, all methods are chosen.
FDR	The False Discovery Rate correction used for the enrichment analysis. By default, 0.05.
NrCores	Nr of computer cores for the parallel parts of the method. Note that the parallelization is NOT initialized in any of the functions. By default, 3.
alpha	feature selection parameter in case of a LASSO model to be chosen.

Value

List containing the GRN graphs.

Examples

   ## Assume we have run the rewiring method and we have discovered a rewired module.
   ## After we have selected the drivers and targets from that modules, we can build
   ## a single GRN to study it separately.


   ## For this example, we are going to join 60 drivers and
   ## 200 targets genes from the example folder.

   drivers <- readRDS(paste0(system.file('extdata',package='TraRe'),'/tfs_linker_example.rds'))
   targets <- readRDS(paste0(system.file('extdata',package='TraRe'),'/targets_linker_example.rds'))

   lognorm_est_counts <- as.matrix(rbind(drivers,targets))

   ## We create the index for drivers and targets in the log-normalized gene expression matrix.

   R<-60
   T<-200

   regulator_filtered_idx <- seq_len(R)
   target_filtered_idx <- R+c(seq_len(T))

   ## We recommend VBSR (rest of parameters are set by default)
   graph <- NET_run(lognorm_est_counts,target_filtered_idx=target_filtered_idx,
                     regulator_filtered_idx=regulator_filtered_idx,graph_mode='VBSR')

ubioinformat/TraRe documentation built on March 10, 2024, 1:11 a.m.