grn_infer {BioNERO} | R Documentation |
The available algorithms are Context Likelihood of Relatedness (CLR), ARACNE, or GENIE3.
grn_infer( exp, regulators = NULL, method = c("clr", "aracne", "genie3"), estimator_clr = "pearson", estimator_aracne = "spearman", eps = 0.1, remove_zero = TRUE, ... )
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()'. |
A gene regulatory network represented as an edge list.
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)