genomicper (version 1.6)

genomicper-package: Circular Genomic Permutations

Description

Description: Circular genomic permutation approach uses GWAS results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All SNPs in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values. The SNPs annotation and pathways annotations can be performed within the package or provided by the user.

Details

Package:

genomicper

Type:

Package

Version:

1.5

Date:

2016-07-04

License:

GPL-2

References

SNP-level Permutations: Genomicper: genome-wide association SNP-set analysis Claudia P. Cabrera*, Pau Navarro*, Jennifer E. Huffman, Alan F. Wright, Caroline Hayward,Harry Campbell, James F. Wilson, Igor Rudan, Nicholas D. Hastie, Veronique Vitart, Chris S. Haley*

Gene-level Permutations: Uncovering Networks from Genome-Wide Association Studies via Circular Genomic Permutation. G3: Genes|Genomes|Genetics 2, 1067-1075. Claudia P. Cabrera*, Pau Navarro*, Jennifer E. Huffman, Alan F. Wright, Caroline Hayward,Harry Campbell, James F. Wilson, Igor Rudan, Nicholas D. Hastie, Veronique Vitart, Chris S. Haley*

See Also

Genomicper functions: 1) read_pvals, 2) genome_order, 3) get_pathways, 4) read2_paths, 5A) snps_permutation, 5B) genes_permutation, 6) get_results, 7) plot_results

Examples

#############################################################################
#  Genomicper functions                                            ##########
# 1)  read_pvals(data_name="",snps_ann="")
# 2)  genome_order(all_data="")
# 3)  get_pathways(source="",all_paths="")
# 4)  read2_paths(ordered_alldata="",gs_locs="",sets_from="",sets_prefix="",level="")
# 5A) snps_permutation(ordered_alldata="",pers_ids="",ntraits="",nper="",saveto="",
#		threshold="",gs_locs=gs_locs,gper.env = gper.env)	
# 5B) genes_permutation(ordered_alldata="",pers_ids="",pathways="",
#		ntraits="",nper="",threshold="",saveto="",gs_locs=gs_locs,gper.env = gper.env)
# 6)  get_results(res_pattern="Permus",level="snp",from="workspace",
#		threshold=0.05, gper.env = gper.env)
# 7) plot_results(results = "", by = "", plot_all = TRUE, var = "", save_plot = TRUE, 
# 						plot_name = "", bf = FALSE, save_qq = TRUE)  
#############################################################################
############## DEMO: #######################################################

#### SNP-level  #############################################################
# SNPs annotation and Pathways provided by user
# all data stored at the WORKSPACE

#library(genomicper)
### Load files for analysis
data(demo,SNPsAnnotation)
# load pathways
data(hsa00100,hsa00120,hsa00130,hsa00140,hsa00190,hsa02010)


# Read & format GWAS pvalues
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)
# Order data according to the genome
genome_results <-genome_order(all_data=all_data)
# Results from genome_order
	ordered_alldata <- genome_results$ordered_alldata
	gs_locs <- genome_results$gs_locs
# Map SNPs to pathways 
paths_res <- read2_paths(ordered_alldata=ordered_alldata,
gs_locs=gs_locs,sets_from="workspace",sets_prefix="hsa",
level="snp",envir=.GlobalEnv)
# Results from read2_paths:		
  pers_ids <- paths_res$per_ors
  pathways<- paths_res$pathways

# Create new environment to save the permutations to:
gper.env <- new.env()
  
# Perform permutations:
snps_permutation(ordered_alldata=ordered_alldata,
pers_ids=pers_ids,ntraits=c(7:13),nper=10,saveto="workspace",
threshold=0.05,gs_locs=gs_locs,envir = gper.env)		  
# Get results						
results <- get_results(res_pattern="Permus",level="snp",
from="workspace",threshold=0.05,envir = gper.env)
# Plot results
## Not run: 
# #saves plots to working directory
# qq <- plot_results(results=results,by="set",plot_all=TRUE)
# qq <- plot_results(results=results,by="trait",
# plot_all=FALSE,var="trait1")
# # Displays interactive plot. Select a trait/set to plot and 
# # set arguments save_plot=FALSE, plot_all = FALSE
# # IMPORTANT: to EXIT interactive plot, RIGHT CLICK on the
# # plot and STOP.
# qq <- plot_results(results=results,by="set",plot_all=FALSE,
# var="hsa00100",save_plot=FALSE)
# ## End(Not run)
# -- END OF DEMO 
###############################################