Limma_export: Prepares Limma object for visualization

In Rvirgenslane/Hotgenes: For Visualizing Differential Expression Analysis

Prepares Limma object for visualization

Description

Prepares Limma object for visualization

Usage

Limma_export(
  limmafit = NULL,
  design_data = NULL,
  Expression_dat = NULL,
  method = "global",
  adjust.method = "BH",
  p.value = 0.1
)

Arguments

limmafit	the fit generated by limma
design_data	data.frame containing Experimental design
Expression_dat	R object containing the expression data used for limma differential expression analysis.
method	character string specifying how genes and contrasts are to be combined in the multiple testing scheme. Choices are "separate", "global", "hierarchical" or "nestedF".
adjust.method	character string specifying p-value adjustment method. Possible values are "none", "BH", "fdr" (equivalent to "BH"), "BY" and "holm". See p.adjust for details.
p.value	numeric value between 0 and 1 giving the required family-wise error rate or false discovery rate.

Value

list of R objects needed for Shiny_DE_viz function

Examples

Example_Hotgenes_dir<-system.file("extdata",
"Example_Hotgenes.Rdata",
package = "Hotgenes", mustWork = TRUE)
load(Example_Hotgenes_dir)

library(limma)

exp<-Example_Hotgenes$Normalized_Expression$rld
design_m<-Example_Hotgenes$design_data

design_matrix <- model.matrix(~sh*Hrs+Bio_Rep,   
data = design_m)

aw <- arrayWeights(exp, design_matrix)

fit <- lmFit(exp, design=design_matrix, weights = aw)
fit <- eBayes(fit, robust = TRUE) 

L_out<-Limma_export(Expression_dat = exp, 
design_data = design_m, 
limmafit = fit)
summary(L_out$Output_lists)

Rvirgenslane/Hotgenes documentation built on June 15, 2024, 2:16 a.m.