sponge: Compute competing endogeneous RNA interactions using Sparse...

In SPONGE: Sparse Partial Correlations On Gene Expression

Description

Compute competing endogeneous RNA interactions using Sparse Partial correlations ON Gene Expression (SPONGE)

Usage

sponge(gene_expr, mir_expr, mir_interactions = NULL,
  log.level = "ERROR", log.every.n = 1e+05, log.file = NULL,
  selected.genes = NULL, gene.combinations = NULL,
  each.miRNA = FALSE, min.cor = 0.1, parallel.chunks = 1000,
  random_seed = NULL, result_as_dt = FALSE)

Arguments

gene_expr	A gene expression matrix with samples in rows and featurs in columns. Alternatively an object of class ExpressionSet.
mir_expr	A miRNA expression matrix with samples in rows and features in columns. Alternatively an object of class ExpressionSet.
mir_interactions	A named list of genes, where for each gene we list all miRNA interaction partners that should be considered.
log.level	The log level, can be one of "info", "debug", "error"
log.every.n	write to the log after every n steps
log.file	write log to a file, particularly useful for paralleliyzation
selected.genes	Operate only on a subset of genes, particularly useful for bootstrapping
gene.combinations	A data frame of combinations of genes to be tested. Gene names are taken from the first two columns and have to match the names used for gene_expr
each.miRNA	Whether to consider individual miRNAs or pooling them.
min.cor	Consider only gene pairs with a minimum correlation specified here.
parallel.chunks	Split into this number of tasks if parallel processing is set up. The number should be high enough to guarantee equal distribution of the work load in parallel execution. However, if the number is too large, e.g. in the worst case one chunk per computation, the overhead causes more computing time than can be saved by parallel execution. Register a parallel backend that is compatible with foreach to use this feature. More information can be found in the documentation of the foreach / doParallel packages.
random_seed	A random seed to be used for reproducible results
result_as_dt	whether to return results as data table or data frame

Value

A data frame with significant gene-gene competetive endogenous RNA or 'sponge' interactions

Examples

#First, extract miRNA candidates for each of the genes
#using sponge_gene_miRNA_interaction_filter. Here we use a prepared
#dataset mir_interactions.

#Second we compute ceRNA interactions for all pairwise combinations of genes
#using all miRNAs remaining after filtering through elasticnet.
ceRNA_interactions <- sponge(
gene_expr = gene_expr,
mir_expr = mir_expr,
mir_interactions = mir_interactions)

SPONGE documentation built on Nov. 8, 2020, 5:39 p.m.