Infer gene regulatory network from expression data

Infer gene regulatory network from expression data

Usage

exp2grn(
  exp,
  regulators = NULL,
  eps = 0,
  estimator_aracne = "spearman",
  estimator_clr = "pearson",
  remove_zero = TRUE,
  nsplit = 10,
  ...
)

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`.

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.

estimator_aracne

Entropy estimator to be used in ARACNE inference. One of "mi.empirical", "mi.mm", "mi.shrink", "mi.sg", "pearson", "spearman", or "kendall". Default: "spearman".

estimator_clr

Entropy estimator to be used in CLR inference. One of "mi.empirical", "mi.mm", "mi.shrink", "mi.sg", "pearson", "spearman", or "kendall". Default: "pearson".

remove_zero

Logical indicating whether to remove edges whose weight is exactly zero. Zero values indicate edges that were removed by ARACNE. Default: TRUE.

nsplit

Number of groups in which the edge list will be split. Default: 10.

...

Additional arguments passed to `GENIE3::GENIE3()`.

Value

A filtered edge list with regulators in the first column and targets in the second column.

Details

This function infers GRNs with ARACNE, GENIE3 and CLR, ranks correlation weights for each GRN and calculates the average rank for each edge. Then, the resulting GRN is filtered to keep the top n edges that lead to the optimal scale-free topology fit.

Examples

data(filt.se)
tfs <- sample(rownames(filt.se), size=50, replace=FALSE)
# Test with small number of trees for demonstration purpose
grn <- exp2grn(filt.se, regulators = tfs, nTrees=2, nsplit=2)
#> The top number of edges that best fits the scale-free topology is 254