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

In PI: Networking Genetic Evidence to Prioritise Drug Targets at the Gene, Pathway and Network Level

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

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:

• term_info: a matrix of nTerm X 4 containing snp/gene set information, where nTerm is the number of terms, and the 4 columns are "id" (i.e. "Term ID"), "name" (i.e. "Term Name"), "namespace" and "distance"

• annotation: a list of terms containing annotations, each term storing its annotations. Always, terms are identified by "id"

• data: a vector containing input data in consideration. It is not always the same as the input data as only those mappable are retained

• background: a vector containing the background data. It is not always the same as the input data as only those mappable are retained

• overlap: a list of overlapped snp/gene sets, each storing snps overlapped between a snp/gene set and the given input data (i.e. the snps of interest). Always, gene sets are identified by "id"

• zscore: a vector containing z-scores

• pvalue: a vector containing p-values

• adjp: a vector containing adjusted p-values. It is the p value but after being adjusted for multiple comparisons

• call: the call that produced this result

Note

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

• "none": does not consider the ontology hierarchy at all.

• "lea": computers the significance of a term in terms of the significance of its children at the maximum depth (e.g. 2). Precisely, once snps are already annotated to any children terms with a more signficance than itself, then all these snps are eliminated from the use for the recalculation of the signifance at that term. The final p-values takes the maximum of the original p-value and the recalculated p-value.

• "elim": computers the significance of a term in terms of the significance of its all children. Precisely, once snps are already annotated to a signficantly enriched term under the cutoff of e.g. pvalue<1e-2, all these snps are eliminated from the ancestors of that term).

• "pc": requires the significance of a term not only using the whole snps as background but also using snps annotated to all its direct parents/ancestors as background. The final p-value takes the maximum of both p-values in these two calculations.

• "Notes": the order of the number of significant terms is: "none" > "lea" > "elim" > "pc".

See Also

xRDataLoader, xEnricher

Examples

## 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.