dGSEA: Function to conduct gene set enrichment analysis given the...

Description Usage Arguments Value Note See Also Examples

View source: R/dGSEA.r

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

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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:

Note

The interpretation of returned components:

See Also

dGSEAview, dGSEAwrite, visGSEA

Examples

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## Not run: 
# load data
library(Biobase)
TCGA_mutations <-dRDataLoader(RData='TCGA_mutations')

# 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")
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")

## based on customised gene sets
eTerm <- dGSEA(data, identity="symbol", genome="Hs",
ontology="Customised", customised.genesets=sample(rownames(data),100))
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])

## End(Not run)

dnet documentation built on Aug. 10, 2018, 5:04 p.m.