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
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