NET_run {TraRe} | R Documentation

## 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')

[Package TraRe version 1.2.0 Index]