LINKER_run {TraRe} | R Documentation

## GRN inference via selected model.

### Description

Gene Regulatory Network inference via model selection. Consists of two phases, `LINKER_runPhase1()` and `LINKER_runPhase2()`. Help them for more information.

### Usage

    LINKER_run(
      lognorm_est_counts,
      target_filtered_idx,
      regulator_filtered_idx,
      nassay = 1,
      regulator = "regulator",
      link_mode = c("VBSR", "LASSOmin", "LASSO1se", "LM"),
      graph_mode = c("VBSR", "LASSOmin", "LASSO1se", "LM"),
      module_rep = "MEAN",
      NrModules = 100,
      corrClustNrIter = 100,
      Nr_bootstraps = 10,
      FDR = 0.05,
      NrCores = 1
    )

### Arguments

`lognorm_est_counts` | Matrix of log-normalized estimated counts of the gene expression data (Nr Genes x Nr samples) or SummarizedExperiment object.
`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'
`link_mode` | Chosen method(s) to link module eigengenes to regulators. The available options are 'VBSR', 'LASSOmin', 'LASSO1se' and 'LM'. By default, all methods are chosen.
`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.
`module_rep` | Method selected for use. Default set to MEAN.
`NrModules` | Number of modules that are a priori to be found (note that the final number of modules discovered may differ from this value). By default, 100 modules.
`corrClustNrIter` | output from preparedata(). By default, 100.
`Nr_bootstraps` | Number of bootstrap of Phase I. By default, 10.
`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, 2.

### Value

List containing the GRN raw results, GRN modules and GRN graphs.

### Examples

       ## 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 to use the default values of the function.
       ## For the sake of time, we will select faster (and worse) ones.

       linkeroutput <- LINKER_run(lognorm_est_counts,target_filtered_idx=target_filtered_idx,
                                  regulator_filtered_idx=regulator_filtered_idx,
                                  link_mode='LASSOmin',graph_mode='LM',NrModules=5,Nr_bootstraps=1,
                                  NrCores=2,corrClustNrIter=10)

[Package TraRe version 1.2.0 Index]