xPrioritiserPathways: Function to prioritise pathways based on enrichment analysis...

Description Usage Arguments Value Note See Also Examples

View source: R/xPrioritiserPathways.r

Description

xPrioritiserPathways is supposed to prioritise pathways given prioritised genes and the ontology in query. It returns an object of class "eTerm". It is done via enrichment analysis.

Usage

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xPrioritiserPathways(pNode, priority.top = 100, background = NULL,
ontology = c("GOBP", "GOMF", "GOCC", "PS", "PS2", "SF", "DO", "HPPA",
"HPMI", "HPCM", "HPMA", "MP", "MsigdbH", "MsigdbC1", "MsigdbC2CGP",
"MsigdbC2CPall", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME",
"MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN",
"MsigdbC4CM",
"MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7",
"DGIdb"),
size.range = c(10, 2000), min.overlap = 3, which.distance = NULL,
test = c("hypergeo", "fisher", "binomial"), p.adjust.method = c("BH",
"BY", "bonferroni", "holm", "hochberg", "hommel"),
ontology.algorithm = c("none", "pc", "elim", "lea"), elim.pvalue =
0.01,
lea.depth = 2, path.mode = c("all_paths", "shortest_paths",
"all_shortest_paths"), true.path.rule = F, verbose = T,
RData.location =
"https://github.com/hfang-bristol/RDataCentre/blob/master/XGR/1.0.0")

Arguments

pNode

an object of class "pNode"

priority.top

the number of the top targets to be analysed for pathway enrichment

background

a background vector. It contains a list of Gene Symbols as the test background. If NULL, by default all annotatable are used as background

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) for drugable categories, and the molecular signatures database (Msigdb, including "MsigdbH", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CPall", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7")

size.range

the minimum and maximum size of members of each term in consideration. By default, it sets to a minimum of 10 but no more than 2000

min.overlap

the minimum number of overlaps. Only those terms with members that overlap with input data at least min.overlap (3 by default) will be processed

which.distance

which terms with the distance away from the ontology root (if any) is used to restrict terms in consideration. By default, it sets to 'NULL' to consider all distances

test

the statistic test used. It can be "fisher" for using fisher's exact test, "hypergeo" for using hypergeometric test, or "binomial" for using binomial test. Fisher's exact test is to test the independence between gene group (genes belonging to a group or not) and gene annotation (genes annotated by a term or not), and thus compare sampling to the left part of background (after sampling without replacement). Hypergeometric test is to sample at random (without replacement) from the background containing annotated and non-annotated genes, and thus compare sampling to background. Unlike hypergeometric test, binomial test is to sample at random (with replacement) from the background with the constant probability. In terms of the ease of finding the significance, they are in order: hypergeometric test > binomial test > fisher's exact test. In other words, in terms of the calculated p-value, hypergeometric test < binomial test < fisher's exact test

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

ontology.algorithm

the algorithm used to account for the hierarchy of the ontology. It can be one of "none", "pc", "elim" and "lea". For details, please see 'Note' below

elim.pvalue

the parameter only used when "ontology.algorithm" is "elim". It is used to control how to declare a signficantly enriched term (and subsequently all genes in this term are eliminated from all its ancestors)

lea.depth

the parameter only used when "ontology.algorithm" is "lea". It is used to control how many maximum depth is used to consider the children of a term (and subsequently all genes in these children term are eliminated from the use for the recalculation of the signifance at this term)

path.mode

the mode of paths induced by vertices/nodes with input annotation data. It can be "all_paths" for all possible paths to the root, "shortest_paths" for only one path to the root (for each node in query), "all_shortest_paths" for all shortest paths to the root (i.e. for each node, find all shortest paths with the equal lengths)

true.path.rule

logical to indicate whether the true-path rule should be applied to propagate annotations. By default, it sets to false

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. See xRDataLoader for details

Value

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

Note

The interpretation of the algorithms used to account for the hierarchy of the ontology is:

See Also

xRDataLoader, xEnricher

Examples

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## Not run: 
# Load the library
library(XGR)
library(igraph)
library(dnet)
library(GenomicRanges)

# a) provide the seed nodes/genes with the weight info
## load ImmunoBase
ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase')
## get genes within 500kb away from AS GWAS lead SNPs
seeds.genes <- ImmunoBase$AS$genes_variants
## seeds weighted according to distance away from lead SNPs
data <- 1- seeds.genes/500000

# b) perform priority analysis
pNode <- xPrioritiserGenes(data=data,
network="PCommonsDN_medium",restart=0.7)

# c) derive pathway-level priority
eTerm <- xPrioritiserPathways(pNode=pNode, priority.top=100,
ontology="MsigdbC2CPall")

# d) view enrichment results for the top significant terms
xEnrichViewer(eTerm)

# e) save enrichment results to the file called 'Pathways_priority.txt'
res <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp",
details=TRUE)
output <- data.frame(term=rownames(res), res)
utils::write.table(output, file="Pathways_priority.txt", sep="\t",
row.names=FALSE)

## End(Not run)

PI documentation built on May 2, 2019, 5:25 p.m.

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