Function to conduct gene set enrichment analysis given the input data and the ontology in query

Description

dGSEA is supposed to conduct gene set enrichment analysis given the input data and the ontology in query. It returns an object of class "eTerm".

Usage

dGSEA(data, identity = c("symbol", "entrez"), check.symbol.identity = FALSE, genome = c("Hs", 
  "Mm", "Rn", "Gg", "Ce", "Dm", "Da", "At"), ontology = c("GOBP", "GOMF", "GOCC", 
      "PS", "PS2", "SF", "DO", "HPPA", "HPMI", "HPCM", "HPMA", "MP", "MsigdbH", "MsigdbC1", 
      "MsigdbC2CGP", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", 
      "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", 
      "MsigdbC5CC", "MsigdbC6", "MsigdbC7", "DGIdb", "Customised"), customised.genesets = NULL, 
      sizeRange = c(10, 20000), which_distance = NULL, weight = 1, nperm = 1000, fast = T, 
      sigTail = c("two-tails", "one-tail"), p.adjust.method = c("BH", "BY", "bonferroni", 
          "holm", "hochberg", "hommel"), verbose = T, RData.location = "https://github.com/hfang-bristol/RDataCentre/blob/master/dnet/1.0.7")

Arguments

data
a data frame or matrix of input data. It must have row names, either Entrez Gene ID or Symbol
identity
the type of gene identity (i.e. row names of input data), either "symbol" for gene symbols (by default) or "entrez" for Entrez Gene ID. The option "symbol" is preferred as it is relatively stable from one update to another; also it is possible to search against synonyms (see the next parameter)
check.symbol.identity
logical to indicate whether synonyms will be searched against when gene symbols cannot be matched. By default, it sets to FALSE since it may take a while to do such check using all possible synoyms
genome
the genome identity. It can be one of "Hs" for human, "Mm" for mouse, "Rn" for rat, "Gg" for chicken, "Ce" for c.elegans, "Dm" for fruitfly, "Da" for zebrafish, and "At" for arabidopsis
ontology
the ontology supported currently. It can be "GOBP" for Gene Ontology Biological Process, "GOMF" for Gene Ontology Molecular Function, "GOCC" for Gene Ontology Cellular Component, "PS" for phylostratific age information, "PS2" for the collapsed PS version (inferred ancestors being collapsed into one with the known taxonomy information), "SF" for domain superfamily assignments, "DO" for Disease Ontology, "HPPA" for Human Phenotype Phenotypic Abnormality, "HPMI" for Human Phenotype Mode of Inheritance, "HPCM" for Human Phenotype Clinical Modifier, "HPMA" for Human Phenotype Mortality Aging, "MP" for Mammalian Phenotype, and Drug-Gene Interaction database (DGIdb) and the molecular signatures database (Msigdb) only in human (including "MsigdbH", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7"). Note: These four ("GOBP", "GOMF", "GOCC" and "PS") are availble for all genomes/species; for "Hs" and "Mm", these six ("DO", "HPPA", "HPMI", "HPCM", "HPMA" and "MP") are also supported; all "Msigdb" are only supported in "Hs". For details on the eligibility for pairs of input genome and ontology, please refer to the online Documentations at http://supfam.org/dnet/docs.html. Also supported are the user-customised gene sets; in doing so, the option "Customised" should be used together with the input of the next parameter "customised.genesets"
customised.genesets
an input vector/matrix/list which only works when the user chooses "Customised" in the previous parameter "ontology". It contains either Entrez Gene ID or Symbol
sizeRange
the minimum and maximum size of members of each gene set in consideration. By default, it sets to a minimum of 10 but no more than 1000
which_distance
which distance of terms in the ontology is used to restrict terms in consideration. By default, it sets to 'NULL' to consider all distances
weight
type of score weight. It can be "0" for unweighted (an equivalent to Kolmogorov-Smirnov, only considering the rank), "1" for weighted by input gene score (by default), and "2" for over-weighted, and so on
nperm
the number of random permutations. For each permutation, gene-score associations will be permutated so that permutation of gene-term associations is realised
fast
logical to indicate whether to fast calculate expected results from permutated data. By default, it sets to true
sigTail
the tail used to calculate the statistical significance. It can be either "two-tails" for the significance based on two-tails or "one-tail" for the significance based on one tail
p.adjust.method
the method used to adjust p-values. It can be one of "BH", "BY", "bonferroni", "holm", "hochberg" and "hommel". The first two methods "BH" (widely used) and "BY" control the false discovery rate (FDR: the expected proportion of false discoveries amongst the rejected hypotheses); the last four methods "bonferroni", "holm", "hochberg" and "hommel" are designed to give strong control of the family-wise error rate (FWER). Notes: FDR is a less stringent condition than FWER
verbose
logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display
RData.location
the characters to tell the location of built-in RData files. By default, it remotely locates at https://github.com/hfang-bristol/RDataCentre/blob/master/dnet and http://dnet.r-forge.r-project.org/RData. Be aware of several versions and the latest one is matched to the current package version. For the user equipped with fast internet connection, this option can be just left as default. But it is always advisable to download these files locally. Especially when the user needs to run this function many times, there is no need to ask the function to remotely download every time (also it will unnecessarily increase the runtime). For examples, these files (as a whole or part of them) can be first downloaded into your current working directory, and then set this option as: RData.location=".". Surely, the location can be anywhere as long as the user provides the correct path pointing to (otherwise, the script will have to remotely download each time). Here is the UNIX command for downloading all RData files (preserving the directory structure): wget -r -l2 -A "*.RData" -np -nH --cut-dirs=0 "http://dnet.r-forge.r-project.org/RData"

Value

an object of class "eTerm", a list with following components:

  • set_info: a matrix of nSet X 4 containing gene set information, where nSet is the number of gene set in consideration, and the 4 columns are "setID" (i.e. "Term ID"), "name" (i.e. "Term Name"), "namespace" and "distance"
  • gs: a list of gene sets, each storing gene members. Always, gene sets are identified by "setID" and gene members identified by "Entrez ID"
  • data: a matrix of nGene X nSample containing input data in consideration. It is not always the same as the input data as only those mappable are retained
  • es: a matrix of nSet X nSample containing enrichment score, where nSample is the number of samples (i.e. the number of columns in input data
  • nes: a matrix of nSet X nSample containing normalised enrichment score. It is the version of enrichment score but after being normalised by gene set size
  • pvalue: a matrix of nSet X nSample containing nominal p value
  • adjp: a matrix of nSet X nSample containing adjusted p value. It is the p value but after being adjusted for multiple comparisons
  • gadjp: a matrix of nSet X nSample containing globally adjusted p value in terms of all samples
  • fdr: a matrix of nSet X nSample containing false discovery rate (FDR). It is the estimated probability that the normalised enrichment score represents a false positive finding
  • qvalue: a matrix of nSet X nSample containing q value. It is the monotunically increasing FDR
  • weight: the input type of score weight
  • call: the call that produced this result

Note

The interpretation of returned components:

  • "es": enrichment score for the gene set is the degree to which this gene set is overrepresented at the top or bottom of the ranked list of genes in each column of input data;
  • "nes": normalised enrichment score for the gene set is enrichment score that has already normalised by gene set size. It is comparable across analysed gene sets;
  • "pvalue": nominal p value is the statistical significance of the enrichment score. It is not adjusted for multiple hypothesis testing, and thus is of limited use in comparing gene sets;
  • "adjp": adjusted p value by Benjamini & Hochberg method. It is comparable across gene sets;
  • "gadjp": globally adjusted p value by Benjamini & Hochberg method. Unlike "adjp", it is adjusted in terms of all samples;
  • "fdr": false discovery rate is the estimated probability that the normalised enrichment score represents a false positive finding. Unlike "adjp" or "gadjp" (also aliased as "fdr") that is derived from a list of p values, this version of fdr is directly calculate from the statistic (i.e. normalised enrichment score);
  • "qvalue": q value is the monotunically increasing FDR so that the higher "nes", the lower "qvalue".

Examples

# load data library(Biobase) TCGA_mutations <-dRDataLoader(RData='TCGA_mutations')
'TCGA_mutations' (from package 'dnet' version 1.0.11) has been loaded into the working environment (at 2017-03-27 19:55:23)
# gene set enrichment analysis (GSEA) using KEGG pathways ## calculate the total mutations for each gene tol <- apply(exprs(TCGA_mutations), 1, sum) data <- data.frame(tol=tol) eTerm <- dGSEA(data, identity="symbol", genome="Hs", ontology="MsigdbC2KEGG")
Start at 2017-03-27 19:55:26 First, load the ontology MsigdbC2KEGG and its gene associations in the genome Hs (2017-03-27 19:55:26) ... 'org.Hs.eg' (from https://github.com/hfang-bristol/RDataCentre/blob/master/dnet/1.0.7/org.Hs.eg.RData?raw=true) has been loaded into the working environment (at 2017-03-27 19:55:29) 'org.Hs.egMsigdbC2KEGG' (from https://github.com/hfang-bristol/RDataCentre/blob/master/dnet/1.0.7/org.Hs.egMsigdbC2KEGG.RData?raw=true) has been loaded into the working environment (at 2017-03-27 19:55:35) Then, do mapping based on symbol (2017-03-27 19:55:35) ... Among 19420 symbols of input data, there are 19038 mappable via official gene symbols but 382 left unmappable Third, perform GSEA analysis based on 1000 permutations for 186 gene sets (2017-03-27 19:55:39) ... Sample 1 is being processed at (2017-03-27 19:55:39) ... 10 of 186 gene sets have been processed (2017-03-27 19:55:43) ... 20 of 186 gene sets have been processed (2017-03-27 19:55:45) ... 30 of 186 gene sets have been processed (2017-03-27 19:55:48) ... 40 of 186 gene sets have been processed (2017-03-27 19:55:50) ... 50 of 186 gene sets have been processed (2017-03-27 19:55:53) ... 60 of 186 gene sets have been processed (2017-03-27 19:55:56) ... 70 of 186 gene sets have been processed (2017-03-27 19:55:59) ... 80 of 186 gene sets have been processed (2017-03-27 19:56:02) ... 90 of 186 gene sets have been processed (2017-03-27 19:56:04) ... 100 of 186 gene sets have been processed (2017-03-27 19:56:07) ... 110 of 186 gene sets have been processed (2017-03-27 19:56:10) ... 120 of 186 gene sets have been processed (2017-03-27 19:56:13) ... 130 of 186 gene sets have been processed (2017-03-27 19:56:16) ... 140 of 186 gene sets have been processed (2017-03-27 19:56:18) ... 150 of 186 gene sets have been processed (2017-03-27 19:56:22) ... 160 of 186 gene sets have been processed (2017-03-27 19:56:24) ... 170 of 186 gene sets have been processed (2017-03-27 19:56:26) ... 180 of 186 gene sets have been processed (2017-03-27 19:56:29) ... 186 of 186 gene sets have been processed (2017-03-27 19:56:30) ... End at 2017-03-27 19:56:32 Runtime in total is: 66 secs
res <- dGSEAview(eTerm, which_sample=1, top_num=5, sortBy="adjp", decreasing=FALSE, details=TRUE) visGSEA(eTerm, which_sample=1, which_term=rownames(res)[1]) output <- dGSEAwrite(eTerm, which_content="gadjp", which_score="gadjp", filename="eTerm.txt")
A file called eTerm.txt has been successfully written!
## based on customised gene sets eTerm <- dGSEA(data, identity="symbol", genome="Hs", ontology="Customised", customised.genesets=sample(rownames(data),100))
Start at 2017-03-27 19:56:32 First, load the ontology Customised and its gene associations in the genome Hs (2017-03-27 19:56:32) ... 'org.Hs.eg' (from https://github.com/hfang-bristol/RDataCentre/blob/master/dnet/1.0.7/org.Hs.eg.RData?raw=true) has been loaded into the working environment (at 2017-03-27 19:56:36) Among 100 symbols of input data, there are 98 mappable via official gene symbols but 2 left unmappable Then, do mapping based on symbol (2017-03-27 19:56:36) ... Among 19420 symbols of input data, there are 19038 mappable via official gene symbols but 382 left unmappable Third, perform GSEA analysis based on 1000 permutations for 1 gene sets (2017-03-27 19:56:38) ... Sample 1 is being processed at (2017-03-27 19:56:38) ... 1 of 1 gene sets have been processed (2017-03-27 19:56:39) ... End at 2017-03-27 19:56:39 Runtime in total is: 7 secs
res <- dGSEAview(eTerm, which_sample=1, top_num=5, sortBy="adjp", decreasing=FALSE, details=TRUE) visGSEA(eTerm, which_sample=1, which_term=rownames(res)[1])

Source code

dGSEA.r

Source man

dGSEA.Rd dGSEA.pdf

See also

dGSEAview, dGSEAwrite, visGSEA