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()`.
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)