RGBM (version 1.0-4)

RGBM:

Description

This function performs the proposed regularized gradient boosting machines for reverse engineering GRN. It allows the user to provide prior information in the form of a mechanistic network g_M and after generation of an initially inferred GRN using the core GBM model undergoes a pruning step. Here we detect and remove isolated nodes using the select_ideal_k function along with identification of the optimal set of transcription factors for each target gene. We then re-iterate through the GBM followed by the refinement step to generate the final re-constructed GRN.

Usage

RGBM(E = matrix(rnorm(100), 10, 10), K = matrix(0, nrow(E), ncol(E)), 
     g_M = matrix(1, 10, 10), tfs = paste0("G", c(1:10)), 
     targets = paste0("G", c(1:10)), lf = 1, M = 5000, nu = 0.001, s_f = 0.3,
     no_iterations = 2, mink = 0, experimentid = 1, outputpath= "DEFAULT",
     sample_type = "Exp1_", 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 p genes and Ntfs represents the number of transcription factors and Ntargets represents the number of target 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.

g_M

Initial mechanistic network in the form of an adajcency matrix (Ntf-by-Ntargets). Here each column is a binary vector where only those elements are 1 when the corresponding transcription factor has a connection with that target gene. Colnames of g_M should be same as names of targets and Rownames of g_M should be same as names of Tfs. By default it's a matrix of ones of size Ntfs x Ntargets.

tfs

List of names of transcription factors

targets

List of names of target genes

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.

no_iterations

Number of times initial GRN to be constructed and then averaged to generate smooth edge weights for the initial GRN as shown in first_GBM_step

mink

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

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 intermediate Adjacency_Matrix and Images folder will be created. By default it's a temp directory (e.g. /tmp/Rtmp...)

sample_type

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

real

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

Value

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

See Also

select_ideal_k, first_GBM_step

Examples

# 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.
A = RGBM()
stopCluster(cl)

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