regularized_GBM_step {RGBM}

Perform the regularized GBM modelling once the initial GRN is inferred

Description

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.

Usage

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

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.

A_prev

An intermediate inferred GRN obtained from first_GBM_step

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 tfs vector, which will be sampled without replacement to calculate each extesion in boosting model. By default it's 0.3.

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.

Value

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

Author(s)

Raghvendra Mall <[e-mail]>

See Also

first_GBM_step