加权共表达分析总结

综述

经典的WGCNA分析
用于疾病两组比较的MEGENA
用于两组处理比较的DGCA
整合工具CEMiTool
CEMiTool的一段代码如下:

## ----style, echo=FALSE, results="asis", message=FALSE--------------------
knitr::opts_chunk$set(tidy = FALSE,
                      warning = FALSE,
                      message = FALSE,
                      cache=TRUE)

## ------------------------------------------------------------------------
BiocManager::install("CEMiTool")
library("CEMiTool")
# load expression data
data(expr0)
head(expr0[,1:4])

## ---- results='hide'-----------------------------------------------------
cem <- cemitool(expr0)

## ------------------------------------------------------------------------
print(cem)
# or, more conveniently, just call `cem`
cem

## ------------------------------------------------------------------------
# inspect modules
nmodules(cem)
head(module_genes(cem))

## ---- eval=FALSE---------------------------------------------------------
#  generate_report(cem, output_format=c("pdf_document", "html_document"))

## ------------------------------------------------------------------------
write_files(cem, directory="./Tables", force=TRUE)

## ------------------------------------------------------------------------
# load your sample annotation data
data(sample_annot)
head(sample_annot)

## ---- results='hide'-----------------------------------------------------
# run cemitool with sample annotation
cem <- cemitool(expr0,sample_annot)

## ------------------------------------------------------------------------
sample_annotation(cem, 
                  sample_name_column="SampleName", 
                  class_column="Class") <- sample_annot

## ------------------------------------------------------------------------
# generate heatmap of gene set enrichment analysis
cem <- mod_gsea(cem)
cem <- plot_gsea(cem)
show_plot(cem, "gsea")

## ------------------------------------------------------------------------
# plot gene expression within each module
cem <- plot_profile(cem)
plots <- show_plot(cem, "profile")
plots[1]

## ------------------------------------------------------------------------
# read GMT file
gmt_fname <- system.file("extdata", "pathways.gmt", package = "CEMiTool")
gmt_in <- read_gmt(gmt_fname)

## ------------------------------------------------------------------------
# perform over representation analysis
cem <- mod_ora(cem, gmt_in)

## ------------------------------------------------------------------------
# plot ora results
cem <- plot_ora(cem)
plots <- show_plot(cem, "ora")
plots[1]












## ------------------------------------------------------------------------
# read interactions
int_fname <- system.file("extdata", "interactions.tsv", package = "CEMiTool")
int_df <- read.delim(int_fname)
head(int_df)

## ------------------------------------------------------------------------
# plot interactions
library(ggplot2)
interactions_data(cem) <- int_df # add interactions
cem <- plot_interactions(cem) # generate plot
plots <- show_plot(cem, "interaction") # view the plot for the first module
plots[3]

## ---- eval=FALSE---------------------------------------------------------
#  # run cemitool
#  library(ggplot2)
#  cem <- cemitool(expr0, sample_annot, gmt_in, interactions=int_df,
#                  filter=TRUE, plot=TRUE, verbose=TRUE)
#  # create report as html document
#  generate_report(cem, directory="./Report")
#  
#  # write analysis results into files
#  write_files(cem, directory="./Tables")
#  
#  # save all plots
#  save_plots(cem, "all", directory="./Plots")

## ---- eval=FALSE---------------------------------------------------------
#  diagnostic_report(cem, directory="./Diagnostics")