xDefineHIC: Function to extract promoter capture HiC-gene pairs given a...

Description Usage Arguments Value Note See Also Examples

View source: R/xDefineHIC.r

Description

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

Usage

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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",
guid = NULL
)

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

guid

a valid (5-character) Global Unique IDentifier for an OSF project. See xRDataLoader for details

Value

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

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

"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.

Note

Pre-built HiC datasets are described below according to the data sources.
1. Promoter Capture HiC datasets in 17 primary blood cell types. Sourced from Cell 2016, 167(5):1369-1384.e19

2. Promoter Capture HiC datasets (involving active promoters and enhancers) in 9 primary blood cell types. Sourced from Cell 2016, 167(5):1369-1384.e19

See Also

xRDataLoader, xAggregate

Examples

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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)

## End(Not run)

Pi documentation built on Nov. 29, 2021, 3 p.m.