Data_Prep: Discretizes biological assay data in preparation for...

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

Description

Discretizes biological assay data in preparation for bayensian network learning

Usage

Data_Prep(
  Auto_WGCNA_OUTPUT = NULL,
  Remove_ME0 = FALSE,
  Numeric_Pheno_scores = NULL,
  xCell_Signatures = NULL,
  ibreaks = 60
)

Arguments

Auto_WGCNA_OUTPUT	R object generated from Auto_WGCNA function.
Remove_ME0	a logical value. If FALSE (default), ME0 is not removed. If TRUE the eigengene for module 0 is removed prior to analysis.
Numeric_Pheno_scores	a data.frame with rows indicating sample ID and columns representing additional phenotype data to be included in BN learning. If NULL (default) no data will be included. If provided, the data.frame will be merged with MEs and discretized into three levels.
xCell_Signatures	the name of the text file generated by xCell that contains the cell signature scores. If NULL (default) the only module eigenegnes will be processed. If not NULL and if Auto_WGCNA_OUTPUT is NULL, cell signature scores will be discretized.
ibreaks	an integer that indicates the number of ibreaks used for discretization. The default value is 60.

Value

a list containing a data.frame with module eigenegnes merged with Xcell signature scores and discretized into three levels: L, M, H. If Auto_WGCNA_OUTPUT is NULL, both scaled and discretized cell signatures will be return.

Note

Please verify that the sample name formatting is consistent between both datasets. Rownames in the module eigengenes data.frame and the column names of xCell signatures scores text file are matched for merging. Only samples that are present in both will be processed!

Examples

file_dir<-system.file("extdata", "IRIS_xCell_sig.txt", 
package = "GmicR", mustWork = TRUE)
Disc_Xcell_sig<-Data_Prep(xCell_Signatures=file_dir, ibreaks = 10)
Disc_Xcell_sig$disc_data

GmicR documentation built on Nov. 8, 2020, 7:07 p.m.