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".
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"
)a data frame or matrix of input data. It must have row names, either Entrez Gene ID or Symbol
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)
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
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
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"
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
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 of terms in the ontology is used to restrict terms in consideration. By default, it sets to 'NULL' to consider all distances
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
the number of random permutations. For each permutation, gene-score associations will be permutated so that permutation of gene-term associations is realised
logical to indicate whether to fast calculate expected results from permutated data. By default, it sets to true
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
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
logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display
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): \(wget -r -l2 -A "*.RData" -np -nH --cut-dirs=0 "http://dnet.r-forge.r-project.org/RData"\)
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
# 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, 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])
# }
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