generatecliques {TraRe} | R Documentation

## Cliques generation and plot displaying.

### Description

By the way the LINKER method works, some highly-correlated driver genes (TFs) may be dropped from the resultant model, as the role they play at the GRN inference process is very similar. Due to this, we propose a method based on cliques (Fully Connected Networks) to recover those dropped drivers.

### Usage

    generatecliques(
      dataset = NULL,
      nassay = 1,
      method = "pearson",
      correlationth = 0.6,
      sparsecorrmatrix = TRUE,
      numcliques = "All",
      mandatorygenes = c(),
      selection = 1
    )

    preparedata(dataset, method)

    generategraph(correlationth, sparsecorrmatrix, pdoutput)

    selectmethod(selection, ggoutput, pdoutput)

    plotcliques(ml, pdobject, sortparameter_ix_numcliques, smobject, numcliques)

### Arguments

`dataset` | input expression file with genes as rows and samples as columns.
`nassay` | if SummarizedExperiment object is passed as input to dataset, name of the assay containing the desired matrix. Default: 0
`method` | method to use in the correlation matrix generation (see stats:cor). Default: 'pearson'
`correlationth` | threshold to consider edge exists. Default: 0.6
`sparsecorrmatrix` | boolean variable specifying whether to set to 0 values below threshold or not. Default: TRUE
`numcliques` | number of cliques to be generated. Default: 'All'
`mandatorygenes` | array of gene names which is mandatory to include in the returned cliques. Default: c() (none)
`selection` | integer selecting method. The available options are: 1 - Maximize Genes/Clique, 2 - Maximize Median Correlation Value/Clique, 3- Maximize Avg Variance Correlation Value/Clique or 4 - Maximize Sum(option two, option three).
`pdoutput` | output from preparedata()
`ggoutput` | output from generategraph()
`ml` | cliques from smobject
`pdobject` | output from preparedata()
`sortparameter_ix_numcliques` | parameter for sorting cliques.
`smobject` | output from selectmethod()

### Details

First, `preparedata()` prepares the correlation matrix and the hash table for future uses. Then, `generategraph()` generates an igraph object from genes correlation (threshold dependant) adjacency matrix. After that, `selectmethod()` chooses method in order to remove duplicities generated from the igraph::max_cliques method. Finally, `generatecliques()` generates all the cliques, containing fully connected networks of genes per clique.

### Value

List containing the plot generated and a list with all the generated cliques.

### Examples

       ## Suppose we want to recover the drivers LINKER may have dropped out.
       ## This method allows to group the highly correlated (above `correlationth`)
       ## driver genes so after the GRN generation, we know that if a particular
       ## driver gene is taking part of a relationship inside that GRN, all the
       ## genes inside this group may be also taking part of the same relationship
       ## due to the high correlation. Note that the large this threshold is, the
       ## surer we are about this affirmation.

       ## For this example, we will only work with the driver genes of a example dataset.

       dataset <- readRDS(paste0(system.file('extdata',package='TraRe'),
                          '/tfs_linker_example_eg.rds'))

       ## Lets select the generated dataset, as the rest of parameters are set by default.

       clioutput <- generatecliques(dataset = dataset)

[Package TraRe version 1.2.0 Index]