gnet {GNET2} | R Documentation

## Run GNET2

### Description

Build regulation modules by iteratively perform regulator assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations reached.

### Usage

    gnet(
      input,
      reg_names,
      init_method = "boosting",
      init_group_num = 4,
      max_depth = 3,
      cor_cutoff = 0.9,
      min_divide_size = 3,
      min_group_size = 2,
      max_iter = 5,
      heuristic = TRUE,
      max_group = 0,
      force_split = 0.5
    )

### Arguments

`input` | A SummarizedExperiment object, or a p by n matrix of expression data of p genes and n samples, for example log2 RPKM from RNA-Seq.
`reg_names` | A list of potential upstream regulators names, for example a list of known transcription factors.
`init_method` | Cluster initialization, can be "boosting" or "kmeans", default is using "boosting".
`init_group_num` | Initial number of function clusters used by the algorithm.
`max_depth` | max_depth Maximum depth of the tree.
`cor_cutoff` | Cutoff for within group Pearson correlation coefficient, if all data belong to a node have average correlation greater or equal to this, the node would not split anymore.
`min_divide_size` | Minimum number of data belong to a node allowed for further split of the node.
`min_group_size` | Minimum number of genes allowed in a group.
`max_iter` | Maxumum number of iterations allowed if not converged.
`heuristic` | If the splites of the regression tree is determined by k-means heuristicly.
`max_group` | Max number of group allowed for the first clustering step, default equals init_group_num and is set to 0.
`force_split` | Force split the largest gene group into smaller groups by kmeans. Default is 0.5(Split if it contains more than half target genes)

### Value

A list of expression data of genes, expression data of regulators, within group score, table of tree structure and final assigned group of each gene.

### Examples

    set.seed(1)
    init_group_num = 8
    init_method = 'boosting'
    exp_data <- matrix(rnorm(50*10),50,10)
    reg_names <- paste0('TF',1:5)
    rownames(exp_data) <- c(reg_names,paste0('gene',1:(nrow(exp_data)-length(reg_names))))
    colnames(exp_data) <- paste0('condition_',1:ncol(exp_data))
    se <- SummarizedExperiment::SummarizedExperiment(assays=list(counts=exp_data))
    gnet_result <- gnet(se,reg_names,init_method,init_group_num)

[Package GNET2 version 1.3.9 Index]