XGR (version 1.0.7)

xGRsampling: Function to generate random samples for data genomic regions from background genomic regions

Description

xGRsampling is supposed to randomly generate samples for data genomic regions from background genomic regions. To do so, we first identify background islands, that is, non-overlapping regions. Then, we keep only parts of data genomic regions that fall into these background islands. For each kept genomic region, a randomised region of the same length is sampled from the corresponding background islands. If required, the randomised region can be restricted to be no more than (eg 10000bp) away from data genomic regions.

Usage

xGRsampling(GR.data, GR.background, num.samples = 100, gap.max = 50000,
max.distance = NULL, verbose = T,
RData.location = "http://galahad.well.ox.ac.uk/bigdata")

Arguments

GR.data

an input data GR object, containing a set of genomic regions based on which to generate a null distribution

GR.background

an input background GR object, containing a set of genomic regions to randomly sample from. It can be a GR list object or a list of GR objects

num.samples

the number of samples randomly generated

gap.max

the maximum distance of background islands to be considered away from data regions. Only background islands no far way from this distance will be considered. For example, if it is 0, meaning that only background islands that overlapp with genomic regions will be considered. By default, it is 50000

max.distance

the maximum distance away from data regions that is allowed when generating random samples. By default, it is NULl meaning no such restriction

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

a list of GR ojects, each containing an GR oject storing a sample.

See Also

xGRsampling

Examples

## Not run: 
# # Load the XGR package and specify the location of built-in data
# library(XGR)
# RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev"
# 
# # Enrichment analysis for GWAS SNPs from ImmunoBase
# # a) provide input data GR object storing GWAS SNPs
# dbSNP_GWAS <- xRDataLoader(RData.customised='dbSNP_GWAS',
# RData.location=RData.location)
# 
# # b) provide backgorund data GR object storing FANTOM5 cell-specific enhancers
# FANTOM5_Enhancer_Cell <-
# xRDataLoader(RData.customised='FANTOM5_Enhancer_Cell',
# RData.location=RData.location)
# 
# # c) generate random samples as a list of GR objects
# sGR_List <- xGRsampling(GR.data=dbSNP_GWAS,
# GR.background=FANTOM5_Enhancer_Cell, num.samples=1000,
# RData.location=RData.location)
# ## End(Not run)