grn_infer {BioNERO} | R Documentation

## Infer gene regulatory network with one of three algorithms

### Description

The available algorithms are Context Likelihood of Relatedness (CLR), ARACNE, or GENIE3.

### Usage

    grn_infer(
      exp,
      regulators = NULL,
      method = c("clr", "aracne", "genie3"),
      estimator_clr = "pearson",
      estimator_aracne = "spearman",
      eps = 0.1,
      remove_zero = TRUE,
      ...
    )

### Arguments

`exp` | A gene expression data frame with genes in row names and samples in column names or a 'SummarizedExperiment' object.
`regulators` | A character vector of regulators (e.g., transcription factors or miRNAs). All regulators must be included in 'exp'.
`method` | GRN inference algorithm to be used. One of "clr", "aracne", or "genie3".
`estimator_clr` | Entropy estimator to be used. One of "mi.empirical", "mi.mm", "mi.shrink", "mi.sg", "pearson", "spearman", or "kendall". Default: "pearson".
`estimator_aracne` | Entropy estimator to be used. One of "mi.empirical", "mi.mm", "mi.shrink", "mi.sg", "pearson", "spearman", or "kendall". Default: "spearman".
`eps` | Numeric value indicating the threshold used when removing an edge: for each triplet of nodes (i,j,k), the weakest edge, say (ij), is removed if its weight is below min(ik),(jk) - eps. Default: 0.1.
`remove_zero` | Logical indicating whether to remove edges whose weight is exactly zero. Default: TRUE
`...` | Additional arguments passed to 'GENIE3::GENIE3()'.

### Value

A gene regulatory network represented as an edge list.

### Examples

    data(filt.se)
    tfs <- sample(rownames(filt.se), size=20, replace=FALSE)
    clr <- grn_infer(filt.se, method = "clr", regulators=tfs)
    aracne <- grn_infer(filt.se, method = "aracne", regulators=tfs)
    # only 2 trees for demonstration purposes
    genie3 <- grn_infer(filt.se, method = "genie3", regulators=tfs, nTrees=2)

[Package BioNERO version 1.0.4 Index]