Description Usage Arguments Value Note See Also Examples

View source: R/xEnricherSNPs.r

`xEnricherSNPs`

is supposed to conduct enrichment analysis given
the input data and the ontology in query. It returns an object of class
"eTerm". Enrichment analysis is based on either Fisher's exact test or
Hypergeometric test. The test can respect the hierarchy of the
ontology. Now it supports enrichment analysis for SNPs using GWAS
Catalog traits mapped to Experimental Factor Ontology. If required,
additional SNPs that are in linkage disequilibrium (LD) with input SNPs
are also be used for test.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
xEnricherSNPs(data, background = NULL, ontology = c("EF", "EF_disease",
"EF_phenotype", "EF_bp"), include.LD = NA, LD.r2 = 0.8,
size.range = c(10, 2000), min.overlap = 3, which.distance = NULL,
test = c("hypergeo", "fisher", "binomial"), p.tail = c("one-tail",
"two-tails"), 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 = T, verbose =
T,
silent = FALSE, RData.location =
"http://galahad.well.ox.ac.uk/bigdata")
``` |

`data` |
an input vector. It contains a list of SNPs of interest |

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

`ontology` |
the ontology supported currently. Now it is only "EF" for Experimental Factor Ontology (used to annotate GWAS Catalog SNPs). However, there are several subparts of this ontology to choose: 'EF_disease' for the subpart under the term 'disease' (EFO:0000408), 'EF_phenotype' for the subpart under the term 'phenotype' (EFO:0000651), 'EF_bp' for the subpart under the term 'biological process' (GO:0008150) |

`include.LD` |
additional SNPs in LD with Lead SNPs are also included. By default, it is 'NA' to disable this option. Otherwise, LD SNPs will be included based on one or more of 26 populations and 5 super populations from 1000 Genomics Project data (phase 3). The population can be one of 5 super populations ("AFR", "AMR", "EAS", "EUR", "SAS"), or one of 26 populations ("ACB", "ASW", "BEB", "CDX", "CEU", "CHB", "CHS", "CLM", "ESN", "FIN", "GBR", "GIH", "GWD", "IBS", "ITU", "JPT", "KHV", "LWK", "MSL", "MXL", "PEL", "PJL", "PUR", "STU", "TSI", "YRI"). Explanations for population code can be found at http://www.1000genomes.org/faq/which-populations-are-part-your-study |

`LD.r2` |
the LD r2 value. By default, it is 0.8, meaning that SNPs in LD (r2>=0.8) with input SNPs will be considered as LD SNPs. It can be any value from 0.8 to 1 |

`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 test statistic 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 > fisher's exact test > binomial test. In other words, in terms of the calculated p-value, hypergeometric test < fisher's exact test < binomial test |

`p.tail` |
the tail used to calculate p-values. It can be either "two-tails" for the significance based on two-tails (ie both over- and under-overrepresentation) or "one-tail" (by default) for the significance based on one tail (ie only over-representation) |

`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 true |

`verbose` |
logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display |

`silent` |
logical to indicate whether the messages will be silent completely. By default, it sets to false. If true, verbose will be forced to be false |

`RData.location` |
the characters to tell the location of built-in
RData files. See |

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"`g`

: an igraph object to represent DAG`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"`fc`

: a vector containing fold changes`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`or`

: a vector containing odds ratio`CIl`

: a vector containing lower bound confidence interval for the odds ratio`CIu`

: a vector containing upper bound confidence interval for the odds ratio`cross`

: a matrix of nTerm X nTerm, with an on-diagnal cell for the overlapped-members observed in an individaul term, and off-diagnal cell for the overlapped-members shared betwene two terms`call`

: the call that produced this result

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".

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ```
## Not run:
# Load the library
library(XGR)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev/"
# SNP-based enrichment analysis using GWAS Catalog traits (mapped to EF)
# a) provide the input SNPs of interest (eg 'EFO:0002690' for 'systemic lupus erythematosus')
## load GWAS SNPs annotated by EF (an object of class "dgCMatrix" storing a spare matrix)
anno <- xRDataLoader(RData='GWAS2EF', RData.location=RData.location)
ind <- which(colnames(anno)=='EFO:0002690')
data <- rownames(anno)[anno[,ind]!=0]
data
# optionally, provide the test background (if not provided, all annotatable SNPs)
#background <- rownames(anno)
# b) perform enrichment analysis
eTerm <- xEnricherSNPs(data=data, ontology="EF",
path.mode=c("all_paths"), RData.location=RData.location)
# b') optionally, enrichment analysis for input SNPs plus their LD SNPs
## LD based on European population (EUR) with r2>=0.8
#eTerm <- xEnricherSNPs(data=data, include.LD="EUR", LD.r2=0.8, RData.location=RData.location)
# c) view enrichment results for the top significant terms
xEnrichViewer(eTerm)
# d) save enrichment results to the file called 'EF_enrichments.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="EF_enrichments.txt", sep="\t",
row.names=FALSE)
# e) barplot of significant enrichment results
bp <- xEnrichBarplot(eTerm, top_num="auto", displayBy="adjp")
print(bp)
# f) visualise the top 10 significant terms in the ontology hierarchy
# color-code terms according to the adjust p-values (taking the form of 10-based negative logarithm)
xEnrichDAGplot(eTerm, top_num=10, displayBy="adjp",
node.info=c("full_term_name"))
# color-code terms according to the z-scores
xEnrichDAGplot(eTerm, top_num=10, displayBy="zscore",
node.info=c("full_term_name"))
## End(Not run)
``` |

XGR documentation built on Jan. 31, 2018, 3 p.m.

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