| second_GBM_step {RGBM} | R Documentation |
Re-iterate through the core GBM model building with optimal set of Tfs for each target gene
Description
This function re-performs the core GBM model building (only one time) using the optimal set of transcription factors obtained from select_ideal_k followed by get_colids for individual target gene to return a regularized GRN.
Usage
second_GBM_step(E, K, df_colids, tfs, targets, Ntfs, Ntargets, lf, M, nu, s_f)
Arguments
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. |
df_colids |
A matrix made up of column vectors where each column vector represents the optimal set of active Tfs which regulate each target gene and obtained from |
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 |
Value
Returns a regularized GRN of the form Ntfs-by-Ntargets
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>