gnet | R Documentation |

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.

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, nthread = 4 )

`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) |

`nthread` |
Number of threads to run GBDT based clustering |

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.

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)

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