GBM | R Documentation |

This function calculates a Ntfs-by-Ntargets adjacency matrix A from N-by-p expression matrix E. E is expected to be given as input. E is assumed to have p columns corresponding to all the genes, Ntfs represents the number of transcription factors and Ntargets represents the number of target genes and N rows corresponding to different experiments. Additionally, GBM function takes matrix of initial perturbations of genes K of the same size as E, and other parameters including which loss function to use (LS = 1, LAD = 2). As a result, GBM returns a squared matrix A of edge confidences of size Ntfs-by-Ntargets. A subset of known transcription factors can be defined as a subset of all p genes.

GBM(E = matrix(rnorm(100), 10, 10), K = matrix(0, nrow(E), ncol(E)), tfs = paste0("G",c(1:10)), targets = paste0("G",c(1:10)), s_s = 1, s_f = 0.3, lf = 1, M = 5000,nu = 0.001, scale = TRUE,center = TRUE, optimization.stage = 2)

`E` |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes. |

`K` |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |

`tfs` |
List of names of transcription factors |

`targets` |
List of names of target genes |

`s_s` |
Sampling rate of experiments, 0<s_s<=1. Fraction of rows of E, which will be sampled with replacement to calculate each extension in boosting model. By default it's 1. |

`s_f` |
Sampling rate of transcription factors, 0<s_f<=1. Fraction of transcription factors from E, as indicated by |

`lf` |
Loss function: 1 -> Least Squares, 2 -> Least Absolute deviation |

`M` |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |

`nu` |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |

`scale` |
Logical flag indicating if each column of E should be scaled to be unit standard deviation. By default it's TRUE. |

`center` |
Logical flag indicating if each column of E should be scaled to be zero mean. By default it's TRUE. |

`optimization.stage` |
Numerical flag indicating if re-evaluation of edge confidences should be applied after calculating initial V, optimization.stage={0,1,2}. If optimization.stage=0, no re-evaluation will be applied. If optimization.stage=1, variance-based optimization will be applied. If optimization.stage=2, variance-based and z-score based optimizations will be applied. |

`A` |
Gene Regulatory Network in form of a Ntfs-by-Ntargets adjacency matrix. |

Raghvendra Mall <rmall@hbku.edu.qa>

`GBM.train`

, `GBM.test`

, `v2l`

# load RGBM library library("RGBM") # this step is optional, it helps speed up calculations, run in parallel on 2 processors library(doParallel) cl <- makeCluster(2) # run network inference on a 100-by-100 dummy expression data. V = GBM() stopCluster(cl)

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