This is the documentation for the R implementation of GENIE3.

The GENIE3 method is described in:

Huynh-Thu V. A., Irrthum A., Wehenkel L., and Geurts P. (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS ONE, 5(9):e12776.

Format of expression data

Format of steady-state expression data

The GENIE3() function takes as input argument a gene expression matrix exprMatr. Each row of that matrix must correspond to a gene and each column must correspond to a sample. The gene names must be specified in rownames(exprMatr). The sample names can be specified in colnames(exprMatr), but this is not mandatory. For example, the following command lines generate a fake expression matrix (for the purpose of this tutorial only):

exprMatr <- matrix(sample(1:10, 100, replace=TRUE), nrow=20)
rownames(exprMatr) <- paste("Gene", 1:20, sep="")
colnames(exprMatr) <- paste("Sample", 1:5, sep="")
##       Sample1 Sample2 Sample3 Sample4 Sample5
## Gene1       1       9       4       6       6
## Gene2       3       9       8       1       7
## Gene3      10       3       4       1       6
## Gene4       6       7       2       3       2
## Gene5       9       8       6       9       9
## Gene6       2       6       4       6       9

This matrix contains the expression data of 20 genes from 5 samples. Note that the expression data do not need to be normalised in any way.

How to run GENIE3

Run GENIE3 with the default parameters

The following command runs GENIE3 on the expression data exprMatr with the default parameters:

set.seed(123) # For reproducibility of results
weightMat <- GENIE3(exprMatr)
## [1] 20 20
##             Gene1      Gene10     Gene11     Gene12     Gene13
## Gene1  0.00000000 0.220109960 0.01720369 0.08969210 0.08109922
## Gene10 0.12548122 0.000000000 0.01812731 0.06995542 0.08174247
## Gene11 0.02279682 0.015639739 0.00000000 0.01279999 0.02081317
## Gene12 0.03809578 0.009497857 0.03223657 0.00000000 0.04902087
## Gene13 0.06665334 0.066135226 0.01768800 0.02022538 0.00000000

The algorithm outputs a matrix containing the weights of the putative regulatory links, with higher weights corresponding to more likely regulatory links. weightMat[i,j] is the weight of the link directed from the \(i\)-th gene to \(j\)-th gene.

Restrict the candidate regulators to a subset of genes

By default, all the genes in exprMatr are used as candidate regulators. The list of candidate regulators can however be restricted to a subset of genes. This can be useful when you know which genes are transcription factors.

# Genes that are used as candidate regulators
regulators <- c(2, 4, 7)
# Or alternatively:
regulators <- c("Gene2", "Gene4", "Gene7")
weightMat <- GENIE3(exprMatr, regulators=regulators)

Here, only Gene2, Gene4 and Gene7 (respectively corresponding to rows 2, 4 and 7 in exprMatr) were used as candidate regulators. In the resulting weightMat, the links that are directed from genes that are not candidate regulators have a weight equal to 0.

Change the tree-based method and its settings

GENIE3 is based on regression trees. These trees can be learned using either the Random Forest method 1 Breiman L. (2001) Random forests. Machine learning, 45(1):5-32. or the Extra-Trees method 2 Geurts P., Ernst D. and Wehenkel L. (2006) Extremely randomized trees. Machine learning, 36(1):3-42.. The tree-based method can be specified using the tree.method parameter (tree.method="RF" for Random Forests, which is the default choice, or tree.method="ET" for Extra-Trees).

Each tree-based method has two parameters: K and ntrees. K is the number of candidate regulators that are randomly selected at each tree node for the best split determination. Let \(p\) be the number of candidate regulators. K must be either:

  • "sqrt", which sets \(K=\sqrt{p}\). This is the default value.
  • "all", which sets \(K=p\).
  • Or any integer between \(1\) and \(p\).

The parameter ntrees specifies the number of trees that are grown per ensemble. It can be set to any strictly positive integer (the default value is 1000).

An example is shown below:

# Use Extra-Trees (ET) method
# 7 randomly chosen candidate regulators at each node of a tree
# 5 trees per ensemble
weightMat <- GENIE3(exprMatr, treeMethod="ET", K=7, nTrees=50)

Parallel GENIE3

To decrease the computing times, GENIE3 can be run on multiple cores. The parameter ncores specifies the number of cores you want to use. For example:

set.seed(123) # For reproducibility of results
weightMat <- GENIE3(exprMatr, nCores=4, verbose=TRUE)

Note that seet.seed allows to get the same results across different runs, but only within nCores==1 or nCores>1. e.g. A run with set.seed(123) and nCores=1 and another with the same seed but nCores>1 may provide different results.

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