run_wgcna: Run WGCNA to group genes into related modules

In jeffreyLbrabec/tinker: Tissue Network Knowledge Transfer

Description

Run WGCNA to group genes into related modules

Usage

run_wgcna(expression_data = NULL, sft_thresh_power = NULL,
  type_of_network = "signed", correlation_type = "bicor")

Arguments

expression_data	Your expression data from 'extract_expr_data'.
sft_thresh_power	The soft-thresholding power output from 'pick_soft_threshold'. This should be picked from the graph labeled Scale independence. The number closest to the "knee" of the number graph indicated by the red line.
type_of_network	WGCNA has several network types you could work under. The 'WGCNA' package recommends you use a signed network for your analysis though their are other options including unsigned and hybrid signed and unsigned.
correlation_type	Type of correlation algorithm to use. The 'WGCNA' package has several options for 'corrType' but bicor is the default.

Value

A figure with two graphs highlighting mean connectivity and scale free independence.

Examples

gse <- download_gse_data("GSE108000")
num_data <- extract_expr_data(gse)
wgcna_out <- run_wgcna(num_data, sft_thresh_power = 12, type_of_network = "signed", 
                       correlation_type = "bicor")

jeffreyLbrabec/tinker documentation built on Nov. 4, 2019, 2:37 p.m.