Co-expressed gene set enrichment analysis

Description

To discovery smaller scale, but highly correlated cellular events that may be of great biological relevance, co-expressed gene set enrichment analysis, cogena, clusters gene expression profiles (coExp) and then make enrichment analysis for each clusters (clEnrich) based on hyper-geometric test. The heatmapCluster and heatmapPEI can visualise the results. See vignette for the detailed workflow.

Source

https://github.com/zhilongjia/cogena

Examples

## A quick start

# Loading the examplar dataseat
data(Psoriasis)

# Clustering the gene expression profiling
clMethods <- c("hierarchical","kmeans","diana","fanny","som","model","sota","pam","clara","agnes")
genecl_result <- coExp(DEexprs, nClust=5:6, clMethods=clMethods, 
    metric="correlation", method="complete", ncore=2, verbose=TRUE)

# Gene set used
annofile <- system.file("extdata", "c2.cp.kegg.v5.0.symbols.gmt.xz", package="cogena")

# Enrichment analysis for clusters
clen_res <- clEnrich(genecl_result, annofile=annofile, sampleLabel=sampleLabel)

summary(clen_res)


# Visualisation
heatmapCluster(clen_res, "hierarchical", "6")
heatmapPEI(clen_res, "hierarchical", "6", printGS=FALSE)

# Obtain genes in a certain cluster
head(geneInCluster(clen_res, "hierarchical", "6", "2"))

## The end

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(cogena)
Loading required package: cluster
Loading required package: ggplot2
Loading required package: kohonen
Loading required package: class
Loading required package: MASS
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/cogena/cogena_package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: cogena_package
> ### Title: Co-expressed gene set enrichment analysis
> ### Aliases: cogean_package cogena cogena_package cogena_package-package
> ### Keywords: package
> 
> ### ** Examples
> 
> 
> ## A quick start
> 
> # Loading the examplar dataseat
> data(Psoriasis)
> 
> # Clustering the gene expression profiling
> clMethods <- c("hierarchical","kmeans","diana","fanny","som","model","sota","pam","clara","agnes")
> genecl_result <- coExp(DEexprs, nClust=5:6, clMethods=clMethods, 
+     metric="correlation", method="complete", ncore=2, verbose=TRUE)
[1] "Dist caculation done"
[1] "# The clMethod, hierarchical starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, hierarchical done"
[1] "# The clMethod, kmeans starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, kmeans done"
[1] "# The clMethod, diana starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, diana done"
[1] "# The clMethod, fanny starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, fanny done"
[1] "# The clMethod, som starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, som done"
[1] "# The clMethod, model starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, model done"
[1] "# The clMethod, sota starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, sota done"
[1] "# The clMethod, pam starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, pam done"
[1] "# The clMethod, clara starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, clara done"
[1] "# The clMethod, agnes starts #"
[1] "getDoParWorkers: 2"
[1] "The clMethod, agnes done"
> 
> # Gene set used
> annofile <- system.file("extdata", "c2.cp.kegg.v5.0.symbols.gmt.xz", package="cogena")
> 
> # Enrichment analysis for clusters
> clen_res <- clEnrich(genecl_result, annofile=annofile, sampleLabel=sampleLabel)
> 
> summary(clen_res)

Clustering Methods:
 hierarchical kmeans diana fanny som model sota pam clara agnes 

The Number of Clusters:
 5 6 

Metric of Distance Matrix:
 correlation 

Agglomeration method for hierarchical clustering (hclust and agnes):
 complete 

Gene set:
 c2.cp.kegg.v5.0.symbols.gmt.xz 

> 
> 
> # Visualisation
> heatmapCluster(clen_res, "hierarchical", "6")
The number of genes in each cluster:
upDownGene
  1   2 
468 238 
cluster_size
  1   2   3   4   5   6 
329 191 122  17  12  35 
> heatmapPEI(clen_res, "hierarchical", "6", printGS=FALSE)
> 
> # Obtain genes in a certain cluster
> head(geneInCluster(clen_res, "hierarchical", "6", "2"))
[1] "BTC"     "KRT77"   "C5ORF46" "CLDN1"   "PTPN21"  "PLLP"   
> 
> ## The end
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>