Query_Prep: Query Prep

In Rvirgenslane/GmicR: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC)

Description

Query Prep

Usage

Query_Prep(
  Auto_WGCNA_OUTPUT,
  numGenes = 500,
  Find_hubs = FALSE,
  nThreads = NULL,
  calculate_intramodularConnectivity = TRUE
)

Arguments

Auto_WGCNA_OUTPUT	R object generated by Auto_WGCNA function.
numGenes	integer indicating the number of random genes to test for hub gene detection. Default is 500.
Find_hubs	logical value. If TRUE, module hub genes will be returned. If FALSE (default), intramodularConnectivity will be returned without hub gene identification.
nThreads	non-negative integer specifying the number of parallel threads to be used by certain parts of correlation calculations. This option only has an effect on systems on which a POSIX thread library is available (which currently includes Linux and Mac OSX, but excludes Windows). If zero, the number of online processors will be used if it can be determined dynamically, otherwise correlation calculations will use 2 threads.
calculate_intramodularConnectivity	a logical value. If TRUE (default), the intramodularConnectivity will be caluculated using the intramodularConnectivity function from WGCNA. If FALSE, a table of modules and genes will be returned without intramodularConnectivity values.

Value

a data.frame detailing the gene symbols for each module. Gene intramodularConnectivity may also be returned. If detected, hub genes are annotated.

Examples

GMIC_Builder_dir<-system.file("extdata", "GMIC_Builder.Rdata", 
package = "GmicR", mustWork = TRUE)
load(GMIC_Builder_dir)
GMIC_Builder<-Query_Prep(GMIC_Builder,  Find_hubs = TRUE)
head(GMIC_Builder$Query)

Rvirgenslane/GmicR documentation built on Feb. 5, 2020, 4:08 a.m.