View source: R/proposed_steps.R

regularized_GBM_step | R Documentation |

This function undertakes all the proposed steps for regularizing the list of transcription factors for individual target gene followed by re-iterating through the core GBM model and the refinement step to produce the final reverse engineered GRN.

regularized_GBM_step(E, A_prev, K, tfs, targets, Ntfs, Ntargets, lf, M, nu, s_f, experimentid, outputpath, sample_type, mink=0,real=0)

`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. |

`A_prev` |
An intermediate inferred GRN obtained from |

`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. |

`Ntfs` |
Total number of transcription factors used in the experiment. |

`Ntargets` |
Total number of target genes used in the experiment |

`lf` |
Loss Function: 1 -> Least Squares and 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. |

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

`experimentid` |
The id of the experiment being conducted. It takes natural numbers like 1,2,3 etc. By default it's 1. |

`outputpath` |
Location where the Adjacency_Matrix and Images folder will be created. |

`sample_type` |
String arguement representing a label for the experiment i.e. in case of DREAM3 challenge sample_type="DREAM3". |

`mink` |
User specified threshold i.e. the minimum number of Tfs to be considered while optimizing the L-curve criterion. By default it's 0. |

`real` |
Numeric value 0 or 1 corresponding to simulated or real experiment respectively. |

Returns the final inferred GRN in form of Ntfs-by-Ntargets matrix

Raghvendra Mall <rmall@hbku.edu.qa>

`first_GBM_step`

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.