XGR (version 1.1.5)

xDefineHIC: Function to extract promoter capture HiC-gene pairs given a list of SNPs

Description

xDefineHIC is supposed to extract HiC-gene pairs given a list of SNPs.

Usage

xDefineHIC(data = NULL, entity = c("SNP", "chr:start-end",
"data.frame", "bed", "GRanges"), include.HiC = c(NA, "Monocytes",
"Macrophages_M0", "Macrophages_M1", "Macrophages_M2", "Neutrophils",
"Megakaryocytes", "Endothelial_precursors", "Erythroblasts",
"Fetal_thymus", "Naive_CD4_T_cells", "Total_CD4_T_cells",
"Activated_total_CD4_T_cells", "Nonactivated_total_CD4_T_cells",
"Naive_CD8_T_cells", "Total_CD8_T_cells", "Naive_B_cells",
"Total_B_cells", "PE.Monocytes", "PE.Macrophages_M0",
"PE.Macrophages_M1", "PE.Macrophages_M2", "PE.Neutrophils",
"PE.Megakaryocytes", "PE.Erythroblasts", "PE.Naive_CD4_T_cells",
"PE.Naive_CD8_T_cells", "Combined", "Combined_PE"),
GR.SNP = c("dbSNP_GWAS", "dbSNP_Common", "dbSNP_Single"),
verbose = TRUE,
RData.location = "http://galahad.well.ox.ac.uk/bigdata")

Arguments

data

NULL or an input vector containing SNPs. If NULL, all SNPs will be considered. If a input vector containing SNPs, SNPs should be provided as dbSNP ID (ie starting with rs) or in the format of 'chrN:xxx', where N is either 1-22 or X, xxx is number; for example, 'chr16:28525386'. Alternatively, it can be other formats/entities (see the next parameter 'entity')

entity

the data entity. By default, it is "SNP". For general use, it can also be one of "chr:start-end", "data.frame", "bed" or "GRanges"

include.HiC

genes linked to input SNPs are also included. By default, it is 'NA' to disable this option. Otherwise, those genes linked to SNPs will be included according to Promoter Capture HiC (PCHiC) datasets. Pre-built HiC datasets are detailed in the section 'Note'

GR.SNP

the genomic regions of SNPs. By default, it is 'dbSNP_GWAS', that is, SNPs from dbSNP (version 146) restricted to GWAS SNPs and their LD SNPs (hg19). It can be 'dbSNP_Common', that is, Common SNPs from dbSNP (version 146) plus GWAS SNPs and their LD SNPs (hg19). Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to dbSNP IDs. Then, tell "GR.SNP" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly

verbose

logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display

RData.location

the characters to tell the location of built-in RData files. See xRDataLoader for details

Value

If input data is NULL, a data frame with following columns:

• from: baited genomic regions (baits)

• to: preyed (other end) genomic regions of interactions (preys)

• score: CHiCAGO scores quantifying the strength of physical interactions between harbors and partners

If input data is not NULL, a list with two components: "df" and "ig". "df" is a data frame with following columns:

• from: 'from/bait' genomic regions

• to: 'to/prey' genomic regions

• score: CHiCAGO scores quantifying the strength of physical interactions between baits and preys

• from_genes: genes associated with 'from/bait' genomic regions

• to_genes: genes associated with 'to/prey' genomic regions

• SNP: input SNPs (in query)

• SNP_end: specify which end SNPs in query fall into (either 'bait/from' or 'prey/to')

• SNP_harbor: genomic regions harbors the SNPs in query

• Context: the context in which PCHiC data was generated

"ig" is an object of both classes "igraph" and "PCHiC", a direct graph with nodes for genomic regions and edges for CHiCAGO scores between them. Also added node attribute is 1) 'target' storing genes assocated and 2) 'SNP' for input SNPs (if the node harboring input SNPs). If several cell types are queried, "ig" is actually a list of "igraph"/"PCHiC" objects.

See Also

xRDataLoader

Examples

# NOT RUN {
# Load the library
library(XGR)
# }
# NOT RUN {
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
# }
# NOT RUN {
# a) provide the SNPs with the significance info
data(ImmunoBase)
data <- names(ImmunoBase$AS$variants)

# b) extract HiC-gene pairs given a list of AS SNPs
PCHiC <- xDefineHIC(data, include.HiC="Monocytes", GR.SNP="dbSNP_GWAS",
RData.location=RData.location)
head(PCHiC$df)

# c) visualise the interaction (a directed graph: bait->prey)
g <- PCHiC$ig
## a node with SNPs colored in 'skyblue' and the one without SNPs in 'pink'
## the width in an edge is proportional to the interaction strength
xPCHiCplot(g, vertex.label.cex=0.5)
xPCHiCplot(g, glayout=layout_in_circle, vertex.label.cex=0.5)
# }