test_edges: Statistical edges testing by permutations

In OceaneCsn/DIANE: DIANE

Statistical edges testing by permutations

Description

We designed a method to perform statistical testing on TF-target gene pairs from the Random Forest regulatory weights inference. The idea is to build a first biologically relevant network with the strongest importances given by a prior GENIE3 run, that would then be refined by statistical testing. Those tests are performed by the rfPermute package, providing empirical pvalues on observed importance values from response variable permutations.

Usage

test_edges(
  mat,
  normalized_counts,
  nGenes,
  nRegulators,
  density = 0.02,
  nTrees = 1000,
  nShuffle = 1000,
  nCores = ifelse(is.na(parallel::detectCores()), 1, max(parallel::detectCores() - 1, 1)),
  verbose = TRUE
)

Arguments

mat	matrix containing the importance values for each target and regulator (preferably computed with GENIE3 and the OOB importance metric)
normalized_counts	normalized expression data containing the genes present in mat argument, and such as used for the first network inference step.
nGenes	number of total genes in the network, union of thetarget genes, and regulators
nRegulators	number of regulators used for the network inference step
density	approximate desired density, that will be used to build a first network, which edges are the one to be statistically tested. Default is 0.02. Biological networks are known to have densities (ratio of edges over total possible edges in the graph) between 0.1 and 0.001. The number of genes and regulators are needed to compute the density.
nTrees	number of trees used for random forest importance computations
nShuffle	number of times the response variable (target gene expression) is randomized in order to estimate the null distribution of the predictive variables (regulators) importances.
nCores	Number of CPU cores to use during the procedure. Default is the detected number of cores minus one.
verbose	If set to TRUE, a feedback on the progress of the calculations is given. Default: TRUE

Value

named list containing the edges pvalues, as well as graphics intended to guide the choice of a pvalue threshold for the final network:

• links: a dataframe containing the links of the network before testing, as built from the user defined prior density. All edges are associated to their pvalue and fdr-adjusted pvalue.

• fdr_nEdges_curve : relation between the fdr threshold, and the final number of edges in the final network

Examples

## Not run: 
data("abiotic_stresses")
data("gene_annotations")
data("regulators_per_organism")

genes <- get_locus(abiotic_stresses$heat_DEGs)
regressors <- intersect(genes, 
                        regulators_per_organism$`Arabidopsis thaliana`)

data <- aggregate_splice_variants(abiotic_stresses$normalized_counts)

r <- DIANE::group_regressors(data, genes, regressors)

mat <- DIANE::network_inference(r$counts, 
                                conds = abiotic_stresses$conditions, 
                                targets = r$grouped_genes,
                                regressors = r$grouped_regressors, 
                                importance_metric = "MSEincrease_oob", 
                                verbose = TRUE) 
res <- DIANE::test_edges(mat, normalized_counts = r$counts, density = 0.02,
                        nGenes = length(r$grouped_genes), 
                        nRegulators = length(r$grouped_regressors), 
                        nTrees = 1000, verbose = TRUE)

## End(Not run)

OceaneCsn/DIANE documentation built on Jan. 10, 2024, 6:43 p.m.