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Exploring Genomic Relations for Enhanced Interpretation Through Enrichment, Similarity, Network and Annotation Analysis

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xDefineEQTL: Function to extract eQTL-gene pairs given a list of SNPs or a...

xDefineEQTL: Function to extract eQTL-gene pairs given a list of SNPs or a...
In XGR: Exploring Genomic Relations for Enhanced Interpretation Through Enrichment, Similarity, Network and Annotation Analysis

Description Usage Arguments Value Note See Also Examples

View source: R/xDefineEQTL.r

Description

xDefineEQTL is supposed to extract eQTL-gene pairs given a list of SNPs or a customised eQTL mapping data.

Usage

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xDefineEQTL(
data = NULL,
include.eQTL = c(NA, "JKscience_CD14", "JKscience_LPS2",
"JKscience_LPS24",
"JKscience_IFN", "JKscience_TS2A", "JKscience_TS2A_CD14",
"JKscience_TS2A_LPS2",
"JKscience_TS2A_LPS24", "JKscience_TS2A_IFN", "JKscience_TS2B",
"JKscience_TS2B_CD14",
"JKscience_TS2B_LPS2", "JKscience_TS2B_LPS24", "JKscience_TS2B_IFN",
"JKscience_TS3A",
"JKng_bcell", "JKng_bcell_cis", "JKng_bcell_trans", "JKng_mono",
"JKng_mono_cis",
"JKng_mono_trans", "JKpg_CD4", "JKpg_CD4_cis", "JKpg_CD4_trans",
"JKpg_CD8",
"JKpg_CD8_cis", "JKpg_CD8_trans", "JKnc_neutro", "JKnc_neutro_cis",
"JKnc_neutro_trans", "WESTRAng_blood", "WESTRAng_blood_cis",
"WESTRAng_blood_trans",
"JK_nk", "JK_nk_cis", "JK_nk_trans", "GTEx_V4_Adipose_Subcutaneous",
"GTEx_V4_Artery_Aorta", "GTEx_V4_Artery_Tibial",
"GTEx_V4_Esophagus_Mucosa",
"GTEx_V4_Esophagus_Muscularis", "GTEx_V4_Heart_Left_Ventricle",
"GTEx_V4_Lung",
"GTEx_V4_Muscle_Skeletal", "GTEx_V4_Nerve_Tibial",
"GTEx_V4_Skin_Sun_Exposed_Lower_leg", "GTEx_V4_Stomach",
"GTEx_V4_Thyroid",
"GTEx_V4_Whole_Blood", "GTEx_V6p_Adipose_Subcutaneous",
"GTEx_V6p_Adipose_Visceral_Omentum", "GTEx_V6p_Adrenal_Gland",
"GTEx_V6p_Artery_Aorta", "GTEx_V6p_Artery_Coronary",
"GTEx_V6p_Artery_Tibial",
"GTEx_V6p_Brain_Anterior_cingulate_cortex_BA24",
"GTEx_V6p_Brain_Caudate_basal_ganglia",
"GTEx_V6p_Brain_Cerebellar_Hemisphere",
"GTEx_V6p_Brain_Cerebellum", "GTEx_V6p_Brain_Cortex",
"GTEx_V6p_Brain_Frontal_Cortex_BA9", "GTEx_V6p_Brain_Hippocampus",
"GTEx_V6p_Brain_Hypothalamus",
"GTEx_V6p_Brain_Nucleus_accumbens_basal_ganglia",
"GTEx_V6p_Brain_Putamen_basal_ganglia",
"GTEx_V6p_Breast_Mammary_Tissue",
"GTEx_V6p_Cells_EBVtransformed_lymphocytes",
"GTEx_V6p_Cells_Transformed_fibroblasts",
"GTEx_V6p_Colon_Sigmoid", "GTEx_V6p_Colon_Transverse",
"GTEx_V6p_Esophagus_Gastroesophageal_Junction",
"GTEx_V6p_Esophagus_Mucosa",
"GTEx_V6p_Esophagus_Muscularis", "GTEx_V6p_Heart_Atrial_Appendage",
"GTEx_V6p_Heart_Left_Ventricle", "GTEx_V6p_Liver", "GTEx_V6p_Lung",
"GTEx_V6p_Muscle_Skeletal", "GTEx_V6p_Nerve_Tibial", "GTEx_V6p_Ovary",
"GTEx_V6p_Pancreas", "GTEx_V6p_Pituitary", "GTEx_V6p_Prostate",
"GTEx_V6p_Skin_Not_Sun_Exposed_Suprapubic",
"GTEx_V6p_Skin_Sun_Exposed_Lower_leg",
"GTEx_V6p_Small_Intestine_Terminal_Ileum", "GTEx_V6p_Spleen",
"GTEx_V6p_Stomach",
"GTEx_V6p_Testis", "GTEx_V6p_Thyroid", "GTEx_V6p_Uterus",
"GTEx_V6p_Vagina",
"GTEx_V6p_Whole_Blood", "eQTLGen", "eQTLGen_cis", "eQTLGen_trans",
"scRNAseq_eQTL_Bcell", "scRNAseq_eQTL_CD4", "scRNAseq_eQTL_CD8",
"scRNAseq_eQTL_cMono", "scRNAseq_eQTL_DC", "scRNAseq_eQTL_Mono",
"scRNAseq_eQTL_ncMono", "scRNAseq_eQTL_NK", "scRNAseq_eQTL_PBMC",
"jpRNAseq_eQTL_Bcell", "jpRNAseq_eQTL_CD4", "jpRNAseq_eQTL_CD8",
"jpRNAseq_eQTL_Mono",
"jpRNAseq_eQTL_NK", "jpRNAseq_eQTL_PBMC", "Pi_eQTL_Bcell",
"Pi_eQTL_Blood",
"Pi_eQTL_CD14", "Pi_eQTL_CD4", "Pi_eQTL_CD8", "Pi_eQTL_IFN",
"Pi_eQTL_LPS2",
"Pi_eQTL_LPS24", "Pi_eQTL_Monocyte", "Pi_eQTL_Neutrophil",
"Pi_eQTL_NK",
"Pi_eQTL_shared_CD14", "Pi_eQTL_shared_IFN", "Pi_eQTL_shared_LPS2",
"Pi_eQTL_shared_LPS24", "Osteoblast_eQTL"),
eQTL.customised = NULL,
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). Alternatively, they can be in the format of 'chrN:xxx', where N is either 1-22 or X, xxx is number; for example, 'chr16:28525386'

include.eQTL

genes modulated by eQTL (also Lead SNPs or in LD with Lead SNPs) are also included. By default, it is 'NA' to disable this option. Otherwise, those genes modulated by eQTL will be included. Pre-built eQTL datasets are detailed in the section 'Note'

eQTL.customised

a user-input matrix or data frame with 4 columns: 1st column for SNPs/eQTLs, 2nd column for Genes, 3rd for eQTL mapping significance level (p-values or FDR), and 4th for contexts (required even though only one context is input). Alternatively, it can be a file containing these 4 columns. It is designed to allow the user analysing their eQTL data. This customisation (if provided) will populate built-in eQTL data; mysql -e "use pi; SELECT rs_id_dbSNP147_GRCh37p13,gene_name,pval_nominal,Tissue FROM GTEx_V7_pair WHERE rs_id_dbSNP147_GRCh37p13!='.';" > /var/www/bigdata/eQTL.customised.txt

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

a data frame with following columns:

Note

Pre-built eQTL datasets are described below according to the data sources.
1. Context-specific eQTLs in monocytes: resting and activating states. Sourced from Science 2014, 343(6175):1246949

2. eQTLs in B cells. Sourced from Nature Genetics 2012, 44(5):502-510

3. eQTLs in monocytes. Sourced from Nature Genetics 2012, 44(5):502-510

4. eQTLs in neutrophils. Sourced from Nature Communications 2015, 7(6):7545

5. eQTLs in NK cells. Unpublished (restricted access)

6. Tissue-specific eQTLs from GTEx (version 4; including 13 tissues). Sourced from Science 2015, 348(6235):648-60

7. eQTLs in CD4 T cells. Sourced from PLoS Genetics 2017, 13(3):e1006643

8. eQTLs in CD8 T cells. Sourced from PLoS Genetics 2017, 13(3):e1006643

9. eQTLs in blood. Sourced from Nature Genetics 2013, 45(10):1238-1243

10. Tissue-specific eQTLs from GTEx (version 6p; including 44 tissues). Sourced from http://www.biorxiv.org/content/early/2016/09/09/074450

11. eQTLs in eQTLGen. Sourced from bioRxiv, 2018, doi:10.1101/447367

12. Single-cell-RNA-identified celltype-specific cis-eQTLs (including 9 cell types). Sourced from Nature Genetics 2018, 50(4):493-497

13. Japanese celltype-specific cis-eQTLs (including 6 cell types). Sourced from Nature Genetics 2017, 49(7):1120-1125

14. Pi eQTL

15. Osteoblast cis-eQTLs. Sourced from Genome Research 2009, 19(11):1942-52

See Also

xRDataLoader

Examples

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## Not run: 
# Load the library
library(XGR)

## End(Not run)

RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
## Not run: 
# a) provide the SNPs with the significance info
data(ImmunoBase)
gr <- ImmunoBase$AS$variants
data <- gr$Variant

# b) define eQTL genes
df_SGS <- xDefineEQTL(data, include.eQTL="JKscience_TS2A",
RData.location=RData.location)

## End(Not run)

XGR documentation built on Jan. 8, 2020, 5:06 p.m.

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