viewEnrichMap-GSCA-method: Plot the enrichment map for GSEA or GSOA result

Description Usage Arguments Details Value References Examples

Description

This is a generic function. When implemented as the S4 method for objects of class GSCA, this function will plot an enrichment map for GSEA or Hypergeometric test results.

Usage

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## S4 method for signature 'GSCA'
viewEnrichMap(object, resultName = "GSEA.results", gscs,
  ntop = NULL, allSig = TRUE, gsNameType = "id", specificGeneset = NULL,
  cutoff = NULL, options = list(), seriesObjs = NULL)

Arguments

object

A GSCA object.

resultName

A single character value specifying draw an enrichment map based on which result. Could only be 'HyperGeo.results' or 'GSEA.results'.

gscs

A character vector specifying the names of gene set collections of which the top significant gene sets will be plotted.

ntop

A single integer or numeric value specifying how many gene sets of top significance will be plotted.

allSig

A single logical value. If 'TRUE', all significant gene sets (GSEA adjusted p-value < 'pValueCutoff' of slot 'para') will be used; otherwise, only top 'ntop' gene sets will be used.

gsNameType

A single character value specifying the type of the gene set names that will be displayed as the names of nodes in the enrichment map. The type of the gene set names should be one of the following: "id", "term" or "none".

specificGeneset

A named list of specific gene sets. Specifically, this term needs to be a subset of all analyzed gene sets which can be roughly gotten by getTopGeneSets(object, resultName, gscs, ntop = 20000, allSig = FALSE).

cutoff

A numeric value between 0 and 1. This parameter is setted as a cutoff of edge weight in the enrichment map for better visualization. When the edge weight, namely the Jaccard coefficient between two gene sets, is less than this cutoff, this edge would not be showed in the enrichment map. The order of the list must match the order of results gotten by aboved function getTopGeneSets.

options

A list of options to modify the enrichmentmap. Details are not showed here due to too many options. Users are highly recommended to modify the enrichment map in a shiny report by report.

seriesObjs

A list of GSCA object. Internally used in the shiny report for visualizing the enrichment map of time series data. No need to explicitly set it!

Details

The idea of this function is similar to the PLoS one paper by Merico et al.

An enrichment map is a network to help better visualize and interpret the GSEA or Hypergeometric test results. In an enrichment map, the nodes represent gene sets and the edges denote the Jaccard similarity coefficient between two gene sets. Node colors are scaled according to the adjusted p-values (the darker the more significant). For GSEA, nodes are colored by the sign of the enrichment scores (red:+, blue: -). The size of nodes illustrates the size of gene sets, while the width of edges denotes the Jaccard coefficient.

A useful application of this function is that user can define specificGeneset to plot an enrichment map with their interested gene sets instead of the top significant ones or all the significant ones. Especially when the number of the significant gene sets are huge, which would make the enrichment map a mess as well as of no information. To this end, user can set the parameter specificGeneset.

Value

An object of igraph with all attributes about the enrichement map.

References

Merico D, Isserlin R, Stueker O, Emili A, Bader GD (2010) Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation. PLoS ONE5(11): e13984. https://doi.org/10.1371/journal.pone.0013984

Examples

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## load a GSCA object(see the examples of 'analyze' GSCA for details)
library(igraph)
data(d7_gsca)

## Example1: view an enrichment map for top 30 significant 'GO_MF' gene sets of GSEA results
## Not run: 
viewEnrichMap(d7_gsca, resultName = "GSEA.results", gscs=c("GO_MF"),
              allSig = FALSE, ntop = 30, gsNameType = "term")

## End(Not run)

## Example2: view an enrichment map for top 15 significant 'GO_MF'
## and 'PW_KEGG' gene sets of GSEA results
## Not run: 
viewEnrichMap(d7_gsca, resultName = "GSEA.results", gscs=c("GO_MF", "PW_KEGG"),
              allSig = FALSE, ntop = 15, gsNameType = "term")

## End(Not run)

## Example3: view an enrichment map for top 15 significant 'GO_MF'
## and 'PW_KEGG' gene sets of GSEA results, edge Jaccard coefficient less than 0.05
## would not be showed in the enrichment map.
## Not run: 
viewEnrichMap(d7_gsca, resultName = "GSEA.results", gscs=c("GO_MF", "PW_KEGG"),
              allSig = FALSE, ntop = 15, gsNameType = "term", cutoff = 0.05)

## End(Not run)

## Example4: view an enrichment map with specificGenesets in 'GO_MF' gene sets of GSEA results
## As told previously, specificGeneset needs to be a subset of all analyzed gene sets
## which can be roughly gotten by:
tmp <- getTopGeneSets(d7_gsca, resultName = "GSEA.results", gscs=c("GO_MF"),
                      ntop = 20000, allSig = FALSE)
## In that case, we can define specificGeneset as below:
GO_MF_geneset <- tmp$GO_MF[c(4,2,6,9,12)]
## the name of specificGenesets also needs to match with the names of tmp
specificGeneset <- list("GO_MF"=GO_MF_geneset)
## Not run: 
viewEnrichMap(d7_gsca, resultName = "GSEA.results", gscs=c("GO_MF"),
              allSig = FALSE, gsNameType = "term",
              ntop = NULL, specificGeneset = specificGeneset)

## End(Not run)

## Example5: view an enrichment map with specificGenesets in 'GO_MF'
## and 'PW_KEGG' gene sets of GSEA results
tmp <- getTopGeneSets(d7_gsca, resultName = "GSEA.results", gscs=c("GO_MF", "PW_KEGG"),
                      ntop = 20000, allSig = FALSE)
GO_MF_geneset <- tmp$GO_MF[c(6,3,5,9,12)]
PW_KEGG_geneset <- tmp$PW_KEGG[c(7,2,5,1,9)]
specificGeneset <- list("GO_MF"=GO_MF_geneset, "PW_KEGG"=PW_KEGG_geneset)
## Not run: 
viewEnrichMap(d7_gsca, resultName = "GSEA.results", gscs=c("GO_MF", "PW_KEGG"),
              allSig = FALSE, gsNameType = "term",
              ntop = NULL, specificGeneset = specificGeneset)

## End(Not run)

CityUHK-CompBio/HTSanalyzeR2 documentation built on Aug. 28, 2018, 1:19 a.m.