find_cbf_modules {fcoex} | R Documentation |
find_cbf_modules uses Symmetrical Uncertainty as a correlation measure and the FCBF algorithm to 1 - Filter the gene list by correlations to a class (Step 1) and 2 - Determine soft thresholds for coexpression to genes predominantly correlated to a class.
find_cbf_modules( fc, n_genes_selected_in_first_step = NULL, FCBF_threshold = 0.1, verbose = TRUE, is_parallel = FALSE ) ## S4 method for signature 'fcoex' find_cbf_modules( fc, n_genes_selected_in_first_step = NULL, FCBF_threshold = 0.1, verbose = TRUE, is_parallel = FALSE )
fc |
A fcoex object containing a discretized expression table |
n_genes_selected_in_first_step |
Sets the number of genes to be selected in the first part of the algorithm. If left unchanged, it defaults to NULL and the minimum_su parameter is used. Caution: it overrides the minimum_su parameter altogether. |
FCBF_threshold |
A threshold for the minimum correlation (as determined by symettrical uncertainty) between each variable and the class used for wrapped FCBF function. Defaults to 0.1. |
verbose |
Adds verbosity. Defaults to TRUE |
is_parallel |
Uses package parallel to paralleliza calculations. Defaults to FALSE. |
Returns a list with the CBF modules found or a adjacency matrix of the graph
library(SingleCellExperiment) data("mini_pbmc3k") targets <- colData(mini_pbmc3k)$clusters exprs <- as.data.frame(assay(mini_pbmc3k, "logcounts")) fc <- new_fcoex(exprs, targets) fc <- discretize(fc) fc <- find_cbf_modules(fc)