dnet (version 1.0.0)

dGSEA: 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", "DO", "HPPA", "HPMI",
"HPON",
"MP", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CP", "MsigdbC2KEGG",
"MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR",
"MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC",
"MsigdbC6", "MsigdbC7"), sizeRange = c(10, 1000), which_distance =
NULL,
weight = 1, nperm = 100, fast = T, sigTail = c("two-tails",
"one-tail"), p.adjust.method = c("BH", "BY", "bonferroni", "holm",
"hochberg", "hommel"), verbose = T,
RData.location = "http://dnet.r-forge.r-project.org/data")

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 a

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, "DO" for Disease Ontology, "HPPA" f

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 weigth. 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 t

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 "http://dnet.r-forge.r-project.org/data". For the user equipped with fast internet connection, this option can be just left as default. But it is al

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
• call: the call that produced this result

See Also

dGSEAview, dGSEAwrite, visGSEA

Examples

load(url("http://dnet.r-forge.r-project.org/data/Datasets/Hiratani_TableS1.RData"))
data <- RT[1:1000,1:2]
eTerm <- dGSEA(data, identity="symbol", genome="Mm", ontology="MP",
which_distance=c(1,2))
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")