snpEnrichment (version 1.7.0)

snpEnrichment-package: ~ Overview: SNPs enrichment analysis ~

Description

Implements classes and methods for large-scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).

Details

Package:

snpEnrichment

Title:

SNPs enrichment analysis

Author:

Mickael Canouil

Contributor:

Loic Yengo

Maintainer:

Mickael Canouil

License:

GPL (>= 2)

Depends:

R (>= 3.0.0), methods

Suggests:

grid, ggplot2

Imports:

parallel, snpStats

URL:

https://github.com/mcanouil/snpEnrichment

Encoding:

UTF-8

See Also

Overview : snpEnrichment-package Classes : Enrichment, Chromosome, EnrichSNP Methods : plot, reSample, getEnrichSNP, excludeSNP, compareEnrichment, enrichment, is.enrichment, chromosome, is.chromosome Functions : initFiles, writeLD, readEnrichment

Examples

###################
### 1. Prepare data
## Not run: snpInfoDir <- system.file("extdata/snpInfo",
#                           package = "snpEnrichment")
# signalFile <- system.file("extdata/Signal/toySignal.txt",
#                           package = "snpEnrichment")
# 
# initFiles(pattern = "Chrom", snpInfoDir, signalFile, mc.cores = 1)
# 
# writeLD(pattern = "Chrom", snpInfoDir, signalFile,
#         ldDir = NULL, ldThresh = 0.8, depth = 1000,
#         mc.cores = 1)## End(Not run)


################
### 2. Read data
## Not run: snpListDir <- system.file("extdata/List",
#                           package = "snpEnrichment")
# data(transcript)
# transcriptFile <- transcript
# 
# toyData <- readEnrichment(pattern = "Chrom", signalFile,
#                          transcriptFile, snpListDir,
#                          snpInfoDir, distThresh = 1000,
#                          sigThresh = 0.05, LD = TRUE,
#                          ldDir = NULL, mc.cores = 1)
# toyData## End(Not run)


######################
### 3. Compute results
## Not run: reSample(object = toyData,
#          nSample = 10,
#          empiricPvalue = TRUE,
#          MAFpool = c(0.05, 0.10, 0.2, 0.3, 0.4, 0.5),
#          mc.cores = 1,
#          onlyGenome = TRUE)## End(Not run)


#######################
### 4. Further analysis: Exclude SNP from original list.
## Not run: excludeFile <- c(
#     "rs4376885", "rs17690928", "rs6460708", "rs13061537", "rs11769827",
#     "rs12717054", "rs2907627", "rs1380109", "rs7024214", "rs7711972",
#     "rs9658282", "rs11750720", "rs1793268", "rs774568", "rs6921786",
#     "rs1699031", "rs6994771", "rs16926670", "rs465612", "rs3012084",
#     "rs354850", "rs12803455", "rs13384873", "rs4364668", "rs8181047",
#     "rs2179993", "rs12049335", "rs6079926", "rs2175144", "rs11564427",
#     "rs7786389", "rs7005565", "rs17423335", "rs12474102", "rs191314",
#     "rs10513168", "rs1711437", "rs1992620", "rs283115", "rs10754563",
#     "rs10851727", "rs2173191", "rs7661353", "rs1342113", "rs7042073",
#     "rs1567445", "rs10120375", "rs550060", "rs3761218", "rs4512977"
# )
# # OR
# excludeFile <- system.file("extdata/Exclude/toyExclude.txt",
#                            package = "snpEnrichment")
# 
# toyData_exclude <- excludeSNP(toyData, excludeFile, mc.cores = 1)
# 
# # Warning: compareEnrichment is in development!!
# compareResults <- compareEnrichment(object.x = toyData,
#                                     object.y = toyData_exclude,
#                                     pattern = "Chrom",
#                                     nSample = 10,
#                                     empiricPvalue = TRUE,
#                                     mc.cores = 1,
#                                     onlyGenome = TRUE)## End(Not run)


####################
### 5. Watch results
## Not run: show(toyData)
# print(toyData)
# head(getEnrichSNP(toyData, type = "xSNP"))
# 
# show(toyData_exclude)
# print(toyData_exclude)
# head(getEnrichSNP(toyData_exclude, type = "eSNP"))## End(Not run)