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R/xDefineEQTL.r
In XGR: Exploring Genomic Relations for Enhanced Interpretation Through Enrichment, Similarity, Network and Annotation Analysis
Defines functions
xDefineEQTL
Documented in
xDefineEQTL
#' Function to extract eQTL-gene pairs given a list of SNPs or a customised eQTL mapping data
#'
#' \code{xDefineEQTL} is supposed to extract eQTL-gene pairs given a list of SNPs or a customised eQTL mapping data.
#'
#' @param 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'
#' @param 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'
#' @param 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
#' @param verbose logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
#' @param RData.location the characters to tell the location of built-in RData files. See \code{\link{xRDataLoader}} for details
#' @param guid a valid (5-character) Global Unique IDentifier for an OSF project. See \code{\link{xRDataLoader}} for details
#' @return
#' a data frame with following columns:
#' \itemize{
#' \item{\code{SNP}: eQTLs}
#' \item{\code{Gene}: eQTL-containing genes}
#' \item{\code{Sig}: the eQTL mapping significant level}
#' \item{\code{Context}: the context in which eQTL data was generated}
#' }
#' @note Pre-built eQTL datasets are described below according to the data sources.\cr
#' 1. Context-specific eQTLs in monocytes: resting and activating states. Sourced from Science 2014, 343(6175):1246949
#' \itemize{
#' \item{\code{JKscience_TS2A}: cis-eQTLs in either state (based on 228 individuals with expression data available for all experimental conditions).}
#' \item{\code{JKscience_TS2A_CD14}: cis-eQTLs only in the resting/CD14+ state (based on 228 individuals).}
#' \item{\code{JKscience_TS2A_LPS2}: cis-eQTLs only in the activating state induced by 2-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_TS2A_LPS24}: cis-eQTLs only in the activating state induced by 24-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_TS2A_IFN}: cis-eQTLs only in the activating state induced by 24-hour interferon-gamma (based on 228 individuals).}
#' \item{\code{JKscience_TS2B}: cis-eQTLs in either state (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_CD14}: cis-eQTLs only in the resting/CD14+ state (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_LPS2}: cis-eQTLs only in the activating state induced by 2-hour LPS (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_LPS24}: cis-eQTLs only in the activating state induced by 24-hour LPS (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_IFN}: cis-eQTLs only in the activating state induced by 24-hour interferon-gamma (based on 432 individuals).}
#' \item{\code{JKscience_TS3A}: trans-eQTLs in either state.}
#' \item{\code{JKscience_CD14}: cis and trans-eQTLs in the resting/CD14+ state (based on 228 individuals).}
#' \item{\code{JKscience_LPS2}: cis and trans-eQTLs in the activating state induced by 2-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_LPS24}: cis and trans-eQTLs in the activating state induced by 24-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_IFN}: cis and trans-eQTLs in the activating state induced by 24-hour interferon-gamma (based on 228 individuals).}
#' }
#' 2. eQTLs in B cells. Sourced from Nature Genetics 2012, 44(5):502-510
#' \itemize{
#' \item{\code{JKng_bcell}: cis- and trans-eQTLs.}
#' \item{\code{JKng_bcell_cis}: cis-eQTLs only.}
#' \item{\code{JKng_bcell_trans}: trans-eQTLs only.}
#' }
#' 3. eQTLs in monocytes. Sourced from Nature Genetics 2012, 44(5):502-510
#' \itemize{
#' \item{\code{JKng_mono}: cis- and trans-eQTLs.}
#' \item{\code{JKng_mono_cis}: cis-eQTLs only.}
#' \item{\code{JKng_mono_trans}: trans-eQTLs only.}
#' }
#' 4. eQTLs in neutrophils. Sourced from Nature Communications 2015, 7(6):7545
#' \itemize{
#' \item{\code{JKnc_neutro}: cis- and trans-eQTLs.}
#' \item{\code{JKnc_neutro_cis}: cis-eQTLs only.}
#' \item{\code{JKnc_neutro_trans}: trans-eQTLs only.}
#' }
#' 5. eQTLs in NK cells. Unpublished (restricted access)
#' \itemize{
#' \item{\code{JK_nk}: cis- and trans-eQTLs.}
#' \item{\code{JK_nk_cis}: cis-eQTLs only.}
#' \item{\code{JK_nk_trans}: trans-eQTLs only.}
#' }
#' 6. Tissue-specific eQTLs from GTEx (version 4; including 13 tissues). Sourced from Science 2015, 348(6235):648-60
#' \itemize{
#' \item{\code{GTEx_V4_Adipose_Subcutaneous}: cis-eQTLs in tissue 'Adipose Subcutaneous'.}
#' \item{\code{GTEx_V4_Artery_Aorta}: cis-eQTLs in tissue 'Artery Aorta'.}
#' \item{\code{GTEx_V4_Artery_Tibial}: cis-eQTLs in tissue 'Artery Tibial'.}
#' \item{\code{GTEx_V4_Esophagus_Mucosa}: cis-eQTLs in tissue 'Esophagus Mucosa'.}
#' \item{\code{GTEx_V4_Esophagus_Muscularis}: cis-eQTLs in tissue 'Esophagus Muscularis'.}
#' \item{\code{GTEx_V4_Heart_Left_Ventricle}: cis-eQTLs in tissue 'Heart Left Ventricle'.}
#' \item{\code{GTEx_V4_Lung}: cis-eQTLs in tissue 'Lung'.}
#' \item{\code{GTEx_V4_Muscle_Skeletal}: cis-eQTLs in tissue 'Muscle Skeletal'.}
#' \item{\code{GTEx_V4_Nerve_Tibial}: cis-eQTLs in tissue 'Nerve Tibial'.}
#' \item{\code{GTEx_V4_Skin_Sun_Exposed_Lower_leg}: cis-eQTLs in tissue 'Skin Sun Exposed Lower leg'.}
#' \item{\code{GTEx_V4_Stomach}: cis-eQTLs in tissue 'Stomach'.}
#' \item{\code{GTEx_V4_Thyroid}: cis-eQTLs in tissue 'Thyroid'.}
#' \item{\code{GTEx_V4_Whole_Blood}: cis-eQTLs in tissue 'Whole Blood'.}
#' }
#' 7. eQTLs in CD4 T cells. Sourced from PLoS Genetics 2017, 13(3):e1006643
#' \itemize{
#' \item{\code{JKpg_CD4}: cis- and trans-eQTLs.}
#' \item{\code{JKpg_CD4_cis}: cis-eQTLs only.}
#' \item{\code{JKpg_CD4_trans}: trans-eQTLs only.}
#' }
#' 8. eQTLs in CD8 T cells. Sourced from PLoS Genetics 2017, 13(3):e1006643
#' \itemize{
#' \item{\code{JKpg_CD8}: cis- and trans-eQTLs.}
#' \item{\code{JKpg_CD8_cis}: cis-eQTLs only.}
#' \item{\code{JKpg_CD8_trans}: trans-eQTLs only.}
#' }
#' 9. eQTLs in blood. Sourced from Nature Genetics 2013, 45(10):1238-1243
#' \itemize{
#' \item{\code{WESTRAng_blood}: cis- and trans-eQTLs.}
#' \item{\code{WESTRAng_blood_cis}: cis-eQTLs only.}
#' \item{\code{WESTRAng_blood_trans}: trans-eQTLs only.}
#' }
#' 10. Tissue-specific eQTLs from GTEx (version 6p; including 44 tissues). Sourced from http://www.biorxiv.org/content/early/2016/09/09/074450
#' \itemize{
#' \item{\code{GTEx_V6p_Adipose_Subcutaneous}: cis-eQTLs in tissue "Adipose Subcutaneous".}
#' \item{\code{GTEx_V6p_Adipose_Visceral_Omentum}: cis-eQTLs in tissue "Adipose Visceral (Omentum)".}
#' \item{\code{GTEx_V6p_Adrenal_Gland}: cis-eQTLs in tissue "Adrenal Gland".}
#' \item{\code{GTEx_V6p_Artery_Aorta}: cis-eQTLs in tissue "Artery Aorta".}
#' \item{\code{GTEx_V6p_Artery_Coronary}: cis-eQTLs in tissue "Artery Coronary".}
#' \item{\code{GTEx_V6p_Artery_Tibial}: cis-eQTLs in tissue "Artery Tibial".}
#' \item{\code{GTEx_V6p_Brain_Anterior_cingulate_cortex_BA24}: cis-eQTLs in tissue "Brain Anterior cingulate cortex (BA24)".}
#' \item{\code{GTEx_V6p_Brain_Caudate_basal_ganglia}: cis-eQTLs in tissue "Brain Caudate (basal ganglia)".}
#' \item{\code{GTEx_V6p_Brain_Cerebellar_Hemisphere}: cis-eQTLs in tissue "Brain Cerebellar Hemisphere".}
#' \item{\code{GTEx_V6p_Brain_Cerebellum}: cis-eQTLs in tissue "Brain Cerebellum".}
#' \item{\code{GTEx_V6p_Brain_Cortex}: cis-eQTLs in tissue "Brain Cortex".}
#' \item{\code{GTEx_V6p_Brain_Frontal_Cortex_BA9}: cis-eQTLs in tissue "Brain Frontal Cortex (BA9)".}
#' \item{\code{GTEx_V6p_Brain_Hippocampus}: cis-eQTLs in tissue "Brain Hippocampus".}
#' \item{\code{GTEx_V6p_Brain_Hypothalamus}: cis-eQTLs in tissue "Brain Hypothalamus".}
#' \item{\code{GTEx_V6p_Brain_Nucleus_accumbens_basal_ganglia}: cis-eQTLs in tissue "Brain Nucleus accumbens (basal ganglia)".}
#' \item{\code{GTEx_V6p_Brain_Putamen_basal_ganglia}: cis-eQTLs in tissue "Brain Putamen (basal ganglia)".}
#' \item{\code{GTEx_V6p_Breast_Mammary_Tissue}: cis-eQTLs in tissue "Breast Mammary Tissue".}
#' \item{\code{GTEx_V6p_Cells_EBVtransformed_lymphocytes}: cis-eQTLs in tissue "Cells EBV-transformed lymphocytes".}
#' \item{\code{GTEx_V6p_Cells_Transformed_fibroblasts}: cis-eQTLs in tissue "Cells Transformed fibroblasts".}
#' \item{\code{GTEx_V6p_Colon_Sigmoid}: cis-eQTLs in tissue "Colon Sigmoid".}
#' \item{\code{GTEx_V6p_Colon_Transverse}: cis-eQTLs in tissue "Colon Transverse".}
#' \item{\code{GTEx_V6p_Esophagus_Gastroesophageal_Junction}: cis-eQTLs in tissue "Esophagus Gastroesophageal Junction".}
#' \item{\code{GTEx_V6p_Esophagus_Mucosa}: cis-eQTLs in tissue "Esophagus Mucosa".}
#' \item{\code{GTEx_V6p_Esophagus_Muscularis}: cis-eQTLs in tissue "Esophagus Muscularis".}
#' \item{\code{GTEx_V6p_Heart_Atrial_Appendage}: cis-eQTLs in tissue "Heart Atrial Appendage".}
#' \item{\code{GTEx_V6p_Heart_Left_Ventricle}: cis-eQTLs in tissue "Heart Left Ventricle".}
#' \item{\code{GTEx_V6p_Liver}: cis-eQTLs in tissue "Liver".}
#' \item{\code{GTEx_V6p_Lung}: cis-eQTLs in tissue "Lung".}
#' \item{\code{GTEx_V6p_Muscle_Skeletal}: cis-eQTLs in tissue "Muscle Skeletal".}
#' \item{\code{GTEx_V6p_Nerve_Tibial}: cis-eQTLs in tissue "Nerve Tibial".}
#' \item{\code{GTEx_V6p_Ovary}: cis-eQTLs in tissue "Ovary".}
#' \item{\code{GTEx_V6p_Pancreas}: cis-eQTLs in tissue "Pancreas".}
#' \item{\code{GTEx_V6p_Pituitary}: cis-eQTLs in tissue "Pituitary".}
#' \item{\code{GTEx_V6p_Prostate}: cis-eQTLs in tissue "Prostate".}
#' \item{\code{GTEx_V6p_Skin_Not_Sun_Exposed_Suprapubic}: cis-eQTLs in tissue "Skin Not Sun Exposed (Suprapubic)".}
#' \item{\code{GTEx_V6p_Skin_Sun_Exposed_Lower_leg}: cis-eQTLs in tissue "Skin Sun Exposed (Lower leg)".}
#' \item{\code{GTEx_V6p_Small_Intestine_Terminal_Ileum}: cis-eQTLs in tissue "Small Intestine Terminal Ileum".}
#' \item{\code{GTEx_V6p_Spleen}: cis-eQTLs in tissue "Spleen".}
#' \item{\code{GTEx_V6p_Stomach}: cis-eQTLs in tissue "Stomach".}
#' \item{\code{GTEx_V6p_Testis}: cis-eQTLs in tissue "Testis".}
#' \item{\code{GTEx_V6p_Thyroid}: cis-eQTLs in tissue "Thyroid".}
#' \item{\code{GTEx_V6p_Uterus}: cis-eQTLs in tissue "Uterus".}
#' \item{\code{GTEx_V6p_Vagina}: cis-eQTLs in tissue "Vagina".}
#' \item{\code{GTEx_V6p_Whole_Blood}: cis-eQTLs in tissue "Whole Blood".}
#' }
#' 11. eQTLs in eQTLGen. Sourced from bioRxiv, 2018, doi:10.1101/447367
#' \itemize{
#' \item{\code{eQTLGen}: cis- and trans-eQTLs.}
#' \item{\code{eQTLGen_cis}: cis-eQTLs only.}
#' \item{\code{eQTLGen_trans}: trans-eQTLs only.}
#' }
#' 12. Single-cell-RNA-identified celltype-specific cis-eQTLs (including 9 cell types). Sourced from Nature Genetics 2018, 50(4):493-497
#' \itemize{
#' \item{\code{scRNAseq_eQTL_Bcell}: cis-eQTLs in B cells.}
#' \item{\code{scRNAseq_eQTL_CD4}: cis-eQTLs in CD4+ T cells.}
#' \item{\code{scRNAseq_eQTL_CD8}: cis-eQTLs in CD8+ T cells.}
#' \item{\code{scRNAseq_eQTL_DC}: cis-eQTLs in dendritic cells.}
#' \item{\code{scRNAseq_eQTL_cMono}: cis-eQTLs in classical monocytes.}
#' \item{\code{scRNAseq_eQTL_ncMono}: cis-eQTLs in nonclassical monocytes.}
#' \item{\code{scRNAseq_eQTL_Mono}: cis-eQTLs in monocytes.}
#' \item{\code{scRNAseq_eQTL_NK}: cis-eQTLs in NK cells.}
#' \item{\code{scRNAseq_eQTL_PBMC}: cis-eQTLs in PBMC.}
#' }
#' 13. Japanese celltype-specific cis-eQTLs (including 6 cell types). Sourced from Nature Genetics 2017, 49(7):1120-1125
#' \itemize{
#' \item{\code{jpRNAseq_eQTL_Bcell}: cis-eQTLs in B cells.}
#' \item{\code{jpRNAseq_eQTL_CD4}: cis-eQTLs in CD4+ T cells.}
#' \item{\code{jpRNAseq_eQTL_CD8}: cis-eQTLs in CD8+ T cells.}
#' \item{\code{jpRNAseq_eQTL_Mono}: cis-eQTLs in monocytes.}
#' \item{\code{jpRNAseq_eQTL_NK}: cis-eQTLs in NK cells.}
#' \item{\code{jpRNAseq_eQTL_PBMC}: cis-eQTLs in PBMC.}
#' }
#' 14. Pi eQTL
#' \itemize{
#' \item{\code{Pi_eQTL_CD14}: cis and trans-eQTLs in the resting/CD14+ state.}
#' \item{\code{Pi_eQTL_LPS2}: cis and trans-eQTLs in the activating state induced by 2-hour LPS.}
#' \item{\code{Pi_eQTL_LPS24}: cis and trans-eQTLs in the activating state induced by 24-hour LPS.}
#' \item{\code{Pi_eQTL_IFN}: cis and trans-eQTLs in the activating state induced by 24-hour interferon-gamma.}
#' \item{\code{Pi_eQTL_Bcell}: cis and trans-eQTLs in B cells.}
#' \item{\code{Pi_eQTL_Blood}: cis and trans-eQTLs in the blood.}
#' \item{\code{Pi_eQTL_CD4}: cis and trans-eQTLs in the CD4 cells.}
#' \item{\code{Pi_eQTL_CD8}: cis and trans-eQTLs in the CD8 cells.}
#' \item{\code{Pi_eQTL_Monocyte}: cis and trans-eQTLs in the monocytes.}
#' \item{\code{Pi_eQTL_Neutrophil}: cis and trans-eQTLs in the neutrophils.}
#' \item{\code{Pi_eQTL_NK}: cis and trans-eQTLs in the NK cells.}
#' \item{\code{Pi_eQTL_shared_CD14}: cis and trans-eQTLs in the resting/CD14+ state (based on 228 individuals).}
#' \item{\code{Pi_eQTL_shared_LPS2}: cis and trans-eQTLs in the activating state induced by 2-hour LPS (based on 228 individuals).}
#' \item{\code{Pi_eQTL_shared_LPS24}: cis and trans-eQTLs in the activating state induced by 24-hour LPS (based on 228 individuals).}
#' \item{\code{Pi_eQTL_shared_IFN}: cis and trans-eQTLs in the activating state induced by 24-hour interferon-gamma (based on 228 individuals).}
#' }
#' 15. Osteoblast cis-eQTLs. Sourced from Genome Research 2009, 19(11):1942-52
#' \itemize{
#' \item{\code{Osteoblast_eQTL}: cis-eQTLs in Osteoblast.}
#' }
#' @export
#' @seealso \code{\link{xRDataLoader}}
#' @include xDefineEQTL.r
#' @examples
#' \dontrun{
#' # Load the library
#' library(XGR)
#' }
#'
#' RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
#' \dontrun{
#' # 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)
#' }
xDefineEQTL <- function(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)
{
######################################################
# Link to targets based on eQTL
######################################################
default.include.eQTL <- c("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","eQTLdb_NK","eQTLdb_CD14","eQTLdb_LPS2","eQTLdb_LPS24","eQTLdb_IFN","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")
ind <- match(default.include.eQTL, include.eQTL)
include.eQTL <- default.include.eQTL[!is.na(ind)]
df_SGS <- NULL
if(length(include.eQTL) > 0){
###########################
# built-in eQTL
###########################
# only load once 'GTEx_V4'
if(sum(grepl("GTEx_V4_",include.eQTL,perl=TRUE)) > 0){
GTEx_V4 <- xRDataLoader(RData.customised='GTEx_V4', RData.location=RData.location, guid=guid, verbose=verbose)
}
# only load once 'GTEx_V6p'
if(sum(grepl("GTEx_V6p_",include.eQTL,perl=TRUE)) > 0){
GTEx_V6p <- xRDataLoader(RData.customised='GTEx_V6p', RData.location=RData.location, guid=guid, verbose=verbose)
}
# only load once 'scRNAseq_eQTL'
if(sum(grepl("scRNAseq_eQTL_",include.eQTL,perl=TRUE)) > 0){
scRNAseq_eQTL <- xRDataLoader(RData.customised='scRNAseq_eQTL', RData.location=RData.location, guid=guid, verbose=verbose)
}
# only load once 'jpRNAseq_eQTL'
if(sum(grepl("jpRNAseq_eQTL_",include.eQTL,perl=TRUE)) > 0){
jpRNAseq_eQTL <- xRDataLoader(RData.customised='jpRNAseq_eQTL', RData.location=RData.location, guid=guid, verbose=verbose)
}
res_list <- lapply(include.eQTL, function(x){
if(verbose){
now <- Sys.time()
message(sprintf("Processing %s ...", x), appendLF=TRUE)
}
if(sum(grep("JKscience_TS2A",x,perl=TRUE)) > 0){
# cis-eQTL
cis <- xRDataLoader(RData.customised='JKscience_TS2A', RData.location=RData.location, guid=guid, verbose=verbose)
# either
if(x=='JKscience_TS2A'){
minFDR <- apply(cis[,c(9:12)], 1, base::min, na.rm=TRUE)
df <- data.frame(SNP=cis[,1], Gene=cis[,4], Sig=minFDR, stringsAsFactors=FALSE)
}else{
# only
if(x=='JKscience_TS2A_CD14'){
j <- 9
}else if(x=='JKscience_TS2A_LPS2'){
j <- 10
}else if(x=='JKscience_TS2A_LPS24'){
j <- 11
}else if(x=='JKscience_TS2A_IFN'){
j <- 12
}
ind <- which(!is.na(cis[,j]) & cis[,j]<0.05)
df <- data.frame(SNP=cis[ind,1], Gene=cis[ind,4], Sig=cis[ind,j], stringsAsFactors=FALSE)
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKscience_TS2B",x,perl=TRUE)) > 0){
# cis-eQTL
cis <- xRDataLoader(RData.customised='JKscience_TS2B', RData.location=RData.location, guid=guid, verbose=verbose)
# either
if(x=='JKscience_TS2B'){
minFDR <- apply(cis[,c(9:12)], 1, base::min, na.rm=TRUE)
df <- data.frame(SNP=cis[,1], Gene=cis[,4], Sig=minFDR, stringsAsFactors=FALSE)
}else{
# only
if(x=='JKscience_TS2B_CD14'){
j <- 9
}else if(x=='JKscience_TS2B_LPS2'){
j <- 10
}else if(x=='JKscience_TS2B_LPS24'){
j <- 11
}else if(x=='JKscience_TS2B_IFN'){
j <- 12
}
ind <- which(!is.na(cis[,j]) & cis[,j]<0.05)
df <- data.frame(SNP=cis[ind,1], Gene=cis[ind,4], Sig=cis[ind,j], stringsAsFactors=FALSE)
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(x=='JKscience_TS3A'){
# trans-eQTL
trans <- xRDataLoader(RData.customised='JKscience_TS3A', RData.location=RData.location, guid=guid, verbose=verbose)
minFDR <- apply(trans[,c(9:12)], 1, base::min, na.rm=TRUE)
df <- data.frame(SNP=trans[,1], Gene=trans[,4], Sig=minFDR, stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(x=='JKscience_CD14' | x=='JKscience_LPS2' | x=='JKscience_LPS24' | x=='JKscience_IFN'){
# cis-eQTL
cis <- xRDataLoader(RData.customised='JKscience_TS2A', RData.location=RData.location, guid=guid, verbose=verbose)
# trans-eQTL
trans <- xRDataLoader(RData.customised='JKscience_TS3A', RData.location=RData.location, guid=guid, verbose=verbose)
## both
df <- rbind(cis, trans)
if(x=='JKscience_CD14'){
j <- 9
}else if(x=='JKscience_LPS2'){
j <- 10
}else if(x=='JKscience_LPS24'){
j <- 11
}else if(x=='JKscience_IFN'){
j <- 12
}
ind <- which(!is.na(df[,j]) & df[,j]<0.05)
df <- data.frame(SNP=df[ind,1], Gene=df[ind,4], Sig=df[ind,j], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKng_bcell",x,perl=TRUE)) > 0){
# b cells
res_ls <- xRDataLoader(RData.customised='JKng_bcell', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,5], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,5], stringsAsFactors=FALSE)
if(x=='JKng_bcell'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKng_bcell_cis'){
df <- df_cis
}else if(x=='JKng_bcell_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKng_mono",x,perl=TRUE)) > 0){
# monocytes
res_ls <- xRDataLoader(RData.customised='JKng_mono', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,5], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,5], stringsAsFactors=FALSE)
if(x=='JKng_mono'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKng_mono_cis'){
df <- df_cis
}else if(x=='JKng_mono_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKpg_CD4",x,perl=TRUE)) > 0){
# CD4
res_ls <- xRDataLoader(RData.customised='JKpg_CD4', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JKpg_CD4'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKpg_CD4_cis'){
df <- df_cis
}else if(x=='JKpg_CD4_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKpg_CD8",x,perl=TRUE)) > 0){
# CD8
res_ls <- xRDataLoader(RData.customised='JKpg_CD8', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JKpg_CD8'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKpg_CD8_cis'){
df <- df_cis
}else if(x=='JKpg_CD8_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKnc_neutro",x,perl=TRUE)) > 0){
# neutrophils
res_ls <- xRDataLoader(RData.customised='JKnc_neutro', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JKnc_neutro'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKnc_neutro_cis'){
df <- df_cis
}else if(x=='JKnc_neutro_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("WESTRAng_blood",x,perl=TRUE)) > 0){
# neutrophils
res_ls <- xRDataLoader(RData.customised='WESTRAng_blood', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='WESTRAng_blood'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='WESTRAng_blood_cis'){
df <- df_cis
}else if(x=='WESTRAng_blood_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("eQTLGen",x,perl=TRUE)) > 0){
# neutrophils
res_ls <- xRDataLoader(RData.customised='eQTLGen', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,3], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,3], stringsAsFactors=FALSE)
if(x=='eQTLGen'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='eQTLGen_cis'){
df <- df_cis
}else if(x=='eQTLGen_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JK_nk",x,perl=TRUE)) > 0){
# NK cells
res_ls <- xRDataLoader(RData.customised='JK_nk', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JK_nk'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JK_nk_cis'){
df <- df_cis
}else if(x=='JK_nk_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("GTEx_V4_",x,perl=TRUE)) > 0){
y <- gsub("GTEx_V4_","",x)
cis <- ''
eval(parse(text=paste("cis <- GTEx_V4$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("GTEx_V6p_",x,perl=TRUE)) > 0){
y <- gsub("GTEx_V6p_","",x)
cis <- ''
eval(parse(text=paste("cis <- GTEx_V6p$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("scRNAseq_eQTL_",x,perl=TRUE)) > 0){
y <- gsub("scRNAseq_eQTL_","",x)
cis <- ''
eval(parse(text=paste("cis <- scRNAseq_eQTL$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("jpRNAseq_eQTL_",x,perl=TRUE)) > 0){
y <- gsub("jpRNAseq_eQTL_","",x)
cis <- ''
eval(parse(text=paste("cis <- jpRNAseq_eQTL$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,3], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(x=='Osteoblast_eQTL'){
# cis-eQTL
cis <- xRDataLoader(RData.customised='Osteoblast_eQTL', RData.location=RData.location, guid=guid, verbose=verbose)
df <- data.frame(SNP=cis[,2], Gene=cis[,3], Sig=cis[,7], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("eQTLdb_",x,perl=TRUE)) > 0){
cis <- xRDataLoader(RData.customised=x, RData.location=RData.location, guid=guid, verbose=verbose)
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
#df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,6], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("Pi_eQTL_",x,perl=TRUE)) > 0){
# both
res_ls <- xRDataLoader(RData.customised=x, RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
df <- rbind(df_cis, df_trans)
df <- data.frame(df, Context=x, stringsAsFactors=FALSE)
}else{
df <- NULL
}
return(df)
})
## get data frame (SNP Gene FDR)
SGS <- do.call(rbind, res_list)
############################
# remove Gene if NA
# remove SNP if NA
df_SGS <- SGS[!is.na(SGS[,1]) & !is.na(SGS[,2]),]
############################
if(verbose){
now <- Sys.time()
message(sprintf("%d eGenes are built-in", length(unique(df_SGS[,2]))), appendLF=TRUE)
}
}
###########################
# customised eQTL
###########################
df_SGS_customised <- NULL
if(!is.null(eQTL.customised)){
if(is.vector(eQTL.customised)){
# assume a file
df <- utils::read.delim(file=eQTL.customised, header=TRUE, row.names=NULL, stringsAsFactors=FALSE)
}else if(is.matrix(eQTL.customised) | is.data.frame(eQTL.customised)){
df <- eQTL.customised
}
if(!is.null(df)){
colnames(df) <- c("SNP", "Gene", "Sig", "Context")
SGS_customised <- df
#SGS_customised <- cbind(df, Context=rep('Customised',nrow(df)), stringsAsFactors=FALSE)
############################
# remove Gene if NA
# remove SNP if NA
df_SGS_customised <- SGS_customised[!is.na(SGS_customised[,1]) & !is.na(SGS_customised[,2]),]
############################
if(verbose){
now <- Sys.time()
message(sprintf("%d eGenes are customised for %d contexts", length(unique(df_SGS_customised[,2])), length(unique(df_SGS_customised[,4]))), appendLF=TRUE)
}
}
}
#########################################
df_SGS <- do.call(rbind, list(df_SGS, df_SGS_customised))
#########################################
if(!is.null(df_SGS)){
############################
# remove Gene if ''
# remove SNP if ''
df_SGS <- df_SGS[df_SGS[,1]!='' & df_SGS[,2]!='',]
############################
}
###########################################
if(!is.null(data)){
## replace '_' with ':'
data <- gsub("_", ":", data, perl=TRUE)
## replace 'imm:' with 'chr'
data <- gsub("imm:", "chr", data, perl=TRUE)
data <- unique(data)
## eQTL weight for input SNPs
ind <- match(df_SGS[,1], data)
df_SGS <- data.frame(df_SGS[!is.na(ind),])
if(verbose){
now <- Sys.time()
message(sprintf("A total of %d input SNPs with %d eGenes", length(data), length(unique(df_SGS[,2]))), appendLF=TRUE)
}
}
invisible(df_SGS)
}
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Defines functions xDefineEQTL
Documented in xDefineEQTL
#' Function to extract eQTL-gene pairs given a list of SNPs or a customised eQTL mapping data
#'
#' \code{xDefineEQTL} is supposed to extract eQTL-gene pairs given a list of SNPs or a customised eQTL mapping data.
#'
#' @param 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'
#' @param 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'
#' @param 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
#' @param verbose logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
#' @param RData.location the characters to tell the location of built-in RData files. See \code{\link{xRDataLoader}} for details
#' @param guid a valid (5-character) Global Unique IDentifier for an OSF project. See \code{\link{xRDataLoader}} for details
#' @return
#' a data frame with following columns:
#' \itemize{
#' \item{\code{SNP}: eQTLs}
#' \item{\code{Gene}: eQTL-containing genes}
#' \item{\code{Sig}: the eQTL mapping significant level}
#' \item{\code{Context}: the context in which eQTL data was generated}
#' }
#' @note Pre-built eQTL datasets are described below according to the data sources.\cr
#' 1. Context-specific eQTLs in monocytes: resting and activating states. Sourced from Science 2014, 343(6175):1246949
#' \itemize{
#' \item{\code{JKscience_TS2A}: cis-eQTLs in either state (based on 228 individuals with expression data available for all experimental conditions).}
#' \item{\code{JKscience_TS2A_CD14}: cis-eQTLs only in the resting/CD14+ state (based on 228 individuals).}
#' \item{\code{JKscience_TS2A_LPS2}: cis-eQTLs only in the activating state induced by 2-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_TS2A_LPS24}: cis-eQTLs only in the activating state induced by 24-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_TS2A_IFN}: cis-eQTLs only in the activating state induced by 24-hour interferon-gamma (based on 228 individuals).}
#' \item{\code{JKscience_TS2B}: cis-eQTLs in either state (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_CD14}: cis-eQTLs only in the resting/CD14+ state (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_LPS2}: cis-eQTLs only in the activating state induced by 2-hour LPS (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_LPS24}: cis-eQTLs only in the activating state induced by 24-hour LPS (based on 432 individuals).}
#' \item{\code{JKscience_TS2B_IFN}: cis-eQTLs only in the activating state induced by 24-hour interferon-gamma (based on 432 individuals).}
#' \item{\code{JKscience_TS3A}: trans-eQTLs in either state.}
#' \item{\code{JKscience_CD14}: cis and trans-eQTLs in the resting/CD14+ state (based on 228 individuals).}
#' \item{\code{JKscience_LPS2}: cis and trans-eQTLs in the activating state induced by 2-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_LPS24}: cis and trans-eQTLs in the activating state induced by 24-hour LPS (based on 228 individuals).}
#' \item{\code{JKscience_IFN}: cis and trans-eQTLs in the activating state induced by 24-hour interferon-gamma (based on 228 individuals).}
#' }
#' 2. eQTLs in B cells. Sourced from Nature Genetics 2012, 44(5):502-510
#' \itemize{
#' \item{\code{JKng_bcell}: cis- and trans-eQTLs.}
#' \item{\code{JKng_bcell_cis}: cis-eQTLs only.}
#' \item{\code{JKng_bcell_trans}: trans-eQTLs only.}
#' }
#' 3. eQTLs in monocytes. Sourced from Nature Genetics 2012, 44(5):502-510
#' \itemize{
#' \item{\code{JKng_mono}: cis- and trans-eQTLs.}
#' \item{\code{JKng_mono_cis}: cis-eQTLs only.}
#' \item{\code{JKng_mono_trans}: trans-eQTLs only.}
#' }
#' 4. eQTLs in neutrophils. Sourced from Nature Communications 2015, 7(6):7545
#' \itemize{
#' \item{\code{JKnc_neutro}: cis- and trans-eQTLs.}
#' \item{\code{JKnc_neutro_cis}: cis-eQTLs only.}
#' \item{\code{JKnc_neutro_trans}: trans-eQTLs only.}
#' }
#' 5. eQTLs in NK cells. Unpublished (restricted access)
#' \itemize{
#' \item{\code{JK_nk}: cis- and trans-eQTLs.}
#' \item{\code{JK_nk_cis}: cis-eQTLs only.}
#' \item{\code{JK_nk_trans}: trans-eQTLs only.}
#' }
#' 6. Tissue-specific eQTLs from GTEx (version 4; including 13 tissues). Sourced from Science 2015, 348(6235):648-60
#' \itemize{
#' \item{\code{GTEx_V4_Adipose_Subcutaneous}: cis-eQTLs in tissue 'Adipose Subcutaneous'.}
#' \item{\code{GTEx_V4_Artery_Aorta}: cis-eQTLs in tissue 'Artery Aorta'.}
#' \item{\code{GTEx_V4_Artery_Tibial}: cis-eQTLs in tissue 'Artery Tibial'.}
#' \item{\code{GTEx_V4_Esophagus_Mucosa}: cis-eQTLs in tissue 'Esophagus Mucosa'.}
#' \item{\code{GTEx_V4_Esophagus_Muscularis}: cis-eQTLs in tissue 'Esophagus Muscularis'.}
#' \item{\code{GTEx_V4_Heart_Left_Ventricle}: cis-eQTLs in tissue 'Heart Left Ventricle'.}
#' \item{\code{GTEx_V4_Lung}: cis-eQTLs in tissue 'Lung'.}
#' \item{\code{GTEx_V4_Muscle_Skeletal}: cis-eQTLs in tissue 'Muscle Skeletal'.}
#' \item{\code{GTEx_V4_Nerve_Tibial}: cis-eQTLs in tissue 'Nerve Tibial'.}
#' \item{\code{GTEx_V4_Skin_Sun_Exposed_Lower_leg}: cis-eQTLs in tissue 'Skin Sun Exposed Lower leg'.}
#' \item{\code{GTEx_V4_Stomach}: cis-eQTLs in tissue 'Stomach'.}
#' \item{\code{GTEx_V4_Thyroid}: cis-eQTLs in tissue 'Thyroid'.}
#' \item{\code{GTEx_V4_Whole_Blood}: cis-eQTLs in tissue 'Whole Blood'.}
#' }
#' 7. eQTLs in CD4 T cells. Sourced from PLoS Genetics 2017, 13(3):e1006643
#' \itemize{
#' \item{\code{JKpg_CD4}: cis- and trans-eQTLs.}
#' \item{\code{JKpg_CD4_cis}: cis-eQTLs only.}
#' \item{\code{JKpg_CD4_trans}: trans-eQTLs only.}
#' }
#' 8. eQTLs in CD8 T cells. Sourced from PLoS Genetics 2017, 13(3):e1006643
#' \itemize{
#' \item{\code{JKpg_CD8}: cis- and trans-eQTLs.}
#' \item{\code{JKpg_CD8_cis}: cis-eQTLs only.}
#' \item{\code{JKpg_CD8_trans}: trans-eQTLs only.}
#' }
#' 9. eQTLs in blood. Sourced from Nature Genetics 2013, 45(10):1238-1243
#' \itemize{
#' \item{\code{WESTRAng_blood}: cis- and trans-eQTLs.}
#' \item{\code{WESTRAng_blood_cis}: cis-eQTLs only.}
#' \item{\code{WESTRAng_blood_trans}: trans-eQTLs only.}
#' }
#' 10. Tissue-specific eQTLs from GTEx (version 6p; including 44 tissues). Sourced from http://www.biorxiv.org/content/early/2016/09/09/074450
#' \itemize{
#' \item{\code{GTEx_V6p_Adipose_Subcutaneous}: cis-eQTLs in tissue "Adipose Subcutaneous".}
#' \item{\code{GTEx_V6p_Adipose_Visceral_Omentum}: cis-eQTLs in tissue "Adipose Visceral (Omentum)".}
#' \item{\code{GTEx_V6p_Adrenal_Gland}: cis-eQTLs in tissue "Adrenal Gland".}
#' \item{\code{GTEx_V6p_Artery_Aorta}: cis-eQTLs in tissue "Artery Aorta".}
#' \item{\code{GTEx_V6p_Artery_Coronary}: cis-eQTLs in tissue "Artery Coronary".}
#' \item{\code{GTEx_V6p_Artery_Tibial}: cis-eQTLs in tissue "Artery Tibial".}
#' \item{\code{GTEx_V6p_Brain_Anterior_cingulate_cortex_BA24}: cis-eQTLs in tissue "Brain Anterior cingulate cortex (BA24)".}
#' \item{\code{GTEx_V6p_Brain_Caudate_basal_ganglia}: cis-eQTLs in tissue "Brain Caudate (basal ganglia)".}
#' \item{\code{GTEx_V6p_Brain_Cerebellar_Hemisphere}: cis-eQTLs in tissue "Brain Cerebellar Hemisphere".}
#' \item{\code{GTEx_V6p_Brain_Cerebellum}: cis-eQTLs in tissue "Brain Cerebellum".}
#' \item{\code{GTEx_V6p_Brain_Cortex}: cis-eQTLs in tissue "Brain Cortex".}
#' \item{\code{GTEx_V6p_Brain_Frontal_Cortex_BA9}: cis-eQTLs in tissue "Brain Frontal Cortex (BA9)".}
#' \item{\code{GTEx_V6p_Brain_Hippocampus}: cis-eQTLs in tissue "Brain Hippocampus".}
#' \item{\code{GTEx_V6p_Brain_Hypothalamus}: cis-eQTLs in tissue "Brain Hypothalamus".}
#' \item{\code{GTEx_V6p_Brain_Nucleus_accumbens_basal_ganglia}: cis-eQTLs in tissue "Brain Nucleus accumbens (basal ganglia)".}
#' \item{\code{GTEx_V6p_Brain_Putamen_basal_ganglia}: cis-eQTLs in tissue "Brain Putamen (basal ganglia)".}
#' \item{\code{GTEx_V6p_Breast_Mammary_Tissue}: cis-eQTLs in tissue "Breast Mammary Tissue".}
#' \item{\code{GTEx_V6p_Cells_EBVtransformed_lymphocytes}: cis-eQTLs in tissue "Cells EBV-transformed lymphocytes".}
#' \item{\code{GTEx_V6p_Cells_Transformed_fibroblasts}: cis-eQTLs in tissue "Cells Transformed fibroblasts".}
#' \item{\code{GTEx_V6p_Colon_Sigmoid}: cis-eQTLs in tissue "Colon Sigmoid".}
#' \item{\code{GTEx_V6p_Colon_Transverse}: cis-eQTLs in tissue "Colon Transverse".}
#' \item{\code{GTEx_V6p_Esophagus_Gastroesophageal_Junction}: cis-eQTLs in tissue "Esophagus Gastroesophageal Junction".}
#' \item{\code{GTEx_V6p_Esophagus_Mucosa}: cis-eQTLs in tissue "Esophagus Mucosa".}
#' \item{\code{GTEx_V6p_Esophagus_Muscularis}: cis-eQTLs in tissue "Esophagus Muscularis".}
#' \item{\code{GTEx_V6p_Heart_Atrial_Appendage}: cis-eQTLs in tissue "Heart Atrial Appendage".}
#' \item{\code{GTEx_V6p_Heart_Left_Ventricle}: cis-eQTLs in tissue "Heart Left Ventricle".}
#' \item{\code{GTEx_V6p_Liver}: cis-eQTLs in tissue "Liver".}
#' \item{\code{GTEx_V6p_Lung}: cis-eQTLs in tissue "Lung".}
#' \item{\code{GTEx_V6p_Muscle_Skeletal}: cis-eQTLs in tissue "Muscle Skeletal".}
#' \item{\code{GTEx_V6p_Nerve_Tibial}: cis-eQTLs in tissue "Nerve Tibial".}
#' \item{\code{GTEx_V6p_Ovary}: cis-eQTLs in tissue "Ovary".}
#' \item{\code{GTEx_V6p_Pancreas}: cis-eQTLs in tissue "Pancreas".}
#' \item{\code{GTEx_V6p_Pituitary}: cis-eQTLs in tissue "Pituitary".}
#' \item{\code{GTEx_V6p_Prostate}: cis-eQTLs in tissue "Prostate".}
#' \item{\code{GTEx_V6p_Skin_Not_Sun_Exposed_Suprapubic}: cis-eQTLs in tissue "Skin Not Sun Exposed (Suprapubic)".}
#' \item{\code{GTEx_V6p_Skin_Sun_Exposed_Lower_leg}: cis-eQTLs in tissue "Skin Sun Exposed (Lower leg)".}
#' \item{\code{GTEx_V6p_Small_Intestine_Terminal_Ileum}: cis-eQTLs in tissue "Small Intestine Terminal Ileum".}
#' \item{\code{GTEx_V6p_Spleen}: cis-eQTLs in tissue "Spleen".}
#' \item{\code{GTEx_V6p_Stomach}: cis-eQTLs in tissue "Stomach".}
#' \item{\code{GTEx_V6p_Testis}: cis-eQTLs in tissue "Testis".}
#' \item{\code{GTEx_V6p_Thyroid}: cis-eQTLs in tissue "Thyroid".}
#' \item{\code{GTEx_V6p_Uterus}: cis-eQTLs in tissue "Uterus".}
#' \item{\code{GTEx_V6p_Vagina}: cis-eQTLs in tissue "Vagina".}
#' \item{\code{GTEx_V6p_Whole_Blood}: cis-eQTLs in tissue "Whole Blood".}
#' }
#' 11. eQTLs in eQTLGen. Sourced from bioRxiv, 2018, doi:10.1101/447367
#' \itemize{
#' \item{\code{eQTLGen}: cis- and trans-eQTLs.}
#' \item{\code{eQTLGen_cis}: cis-eQTLs only.}
#' \item{\code{eQTLGen_trans}: trans-eQTLs only.}
#' }
#' 12. Single-cell-RNA-identified celltype-specific cis-eQTLs (including 9 cell types). Sourced from Nature Genetics 2018, 50(4):493-497
#' \itemize{
#' \item{\code{scRNAseq_eQTL_Bcell}: cis-eQTLs in B cells.}
#' \item{\code{scRNAseq_eQTL_CD4}: cis-eQTLs in CD4+ T cells.}
#' \item{\code{scRNAseq_eQTL_CD8}: cis-eQTLs in CD8+ T cells.}
#' \item{\code{scRNAseq_eQTL_DC}: cis-eQTLs in dendritic cells.}
#' \item{\code{scRNAseq_eQTL_cMono}: cis-eQTLs in classical monocytes.}
#' \item{\code{scRNAseq_eQTL_ncMono}: cis-eQTLs in nonclassical monocytes.}
#' \item{\code{scRNAseq_eQTL_Mono}: cis-eQTLs in monocytes.}
#' \item{\code{scRNAseq_eQTL_NK}: cis-eQTLs in NK cells.}
#' \item{\code{scRNAseq_eQTL_PBMC}: cis-eQTLs in PBMC.}
#' }
#' 13. Japanese celltype-specific cis-eQTLs (including 6 cell types). Sourced from Nature Genetics 2017, 49(7):1120-1125
#' \itemize{
#' \item{\code{jpRNAseq_eQTL_Bcell}: cis-eQTLs in B cells.}
#' \item{\code{jpRNAseq_eQTL_CD4}: cis-eQTLs in CD4+ T cells.}
#' \item{\code{jpRNAseq_eQTL_CD8}: cis-eQTLs in CD8+ T cells.}
#' \item{\code{jpRNAseq_eQTL_Mono}: cis-eQTLs in monocytes.}
#' \item{\code{jpRNAseq_eQTL_NK}: cis-eQTLs in NK cells.}
#' \item{\code{jpRNAseq_eQTL_PBMC}: cis-eQTLs in PBMC.}
#' }
#' 14. Pi eQTL
#' \itemize{
#' \item{\code{Pi_eQTL_CD14}: cis and trans-eQTLs in the resting/CD14+ state.}
#' \item{\code{Pi_eQTL_LPS2}: cis and trans-eQTLs in the activating state induced by 2-hour LPS.}
#' \item{\code{Pi_eQTL_LPS24}: cis and trans-eQTLs in the activating state induced by 24-hour LPS.}
#' \item{\code{Pi_eQTL_IFN}: cis and trans-eQTLs in the activating state induced by 24-hour interferon-gamma.}
#' \item{\code{Pi_eQTL_Bcell}: cis and trans-eQTLs in B cells.}
#' \item{\code{Pi_eQTL_Blood}: cis and trans-eQTLs in the blood.}
#' \item{\code{Pi_eQTL_CD4}: cis and trans-eQTLs in the CD4 cells.}
#' \item{\code{Pi_eQTL_CD8}: cis and trans-eQTLs in the CD8 cells.}
#' \item{\code{Pi_eQTL_Monocyte}: cis and trans-eQTLs in the monocytes.}
#' \item{\code{Pi_eQTL_Neutrophil}: cis and trans-eQTLs in the neutrophils.}
#' \item{\code{Pi_eQTL_NK}: cis and trans-eQTLs in the NK cells.}
#' \item{\code{Pi_eQTL_shared_CD14}: cis and trans-eQTLs in the resting/CD14+ state (based on 228 individuals).}
#' \item{\code{Pi_eQTL_shared_LPS2}: cis and trans-eQTLs in the activating state induced by 2-hour LPS (based on 228 individuals).}
#' \item{\code{Pi_eQTL_shared_LPS24}: cis and trans-eQTLs in the activating state induced by 24-hour LPS (based on 228 individuals).}
#' \item{\code{Pi_eQTL_shared_IFN}: cis and trans-eQTLs in the activating state induced by 24-hour interferon-gamma (based on 228 individuals).}
#' }
#' 15. Osteoblast cis-eQTLs. Sourced from Genome Research 2009, 19(11):1942-52
#' \itemize{
#' \item{\code{Osteoblast_eQTL}: cis-eQTLs in Osteoblast.}
#' }
#' @export
#' @seealso \code{\link{xRDataLoader}}
#' @include xDefineEQTL.r
#' @examples
#' \dontrun{
#' # Load the library
#' library(XGR)
#' }
#'
#' RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
#' \dontrun{
#' # 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)
#' }
xDefineEQTL <- function(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)
{
######################################################
# Link to targets based on eQTL
######################################################
default.include.eQTL <- c("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","eQTLdb_NK","eQTLdb_CD14","eQTLdb_LPS2","eQTLdb_LPS24","eQTLdb_IFN","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")
ind <- match(default.include.eQTL, include.eQTL)
include.eQTL <- default.include.eQTL[!is.na(ind)]
df_SGS <- NULL
if(length(include.eQTL) > 0){
###########################
# built-in eQTL
###########################
# only load once 'GTEx_V4'
if(sum(grepl("GTEx_V4_",include.eQTL,perl=TRUE)) > 0){
GTEx_V4 <- xRDataLoader(RData.customised='GTEx_V4', RData.location=RData.location, guid=guid, verbose=verbose)
}
# only load once 'GTEx_V6p'
if(sum(grepl("GTEx_V6p_",include.eQTL,perl=TRUE)) > 0){
GTEx_V6p <- xRDataLoader(RData.customised='GTEx_V6p', RData.location=RData.location, guid=guid, verbose=verbose)
}
# only load once 'scRNAseq_eQTL'
if(sum(grepl("scRNAseq_eQTL_",include.eQTL,perl=TRUE)) > 0){
scRNAseq_eQTL <- xRDataLoader(RData.customised='scRNAseq_eQTL', RData.location=RData.location, guid=guid, verbose=verbose)
}
# only load once 'jpRNAseq_eQTL'
if(sum(grepl("jpRNAseq_eQTL_",include.eQTL,perl=TRUE)) > 0){
jpRNAseq_eQTL <- xRDataLoader(RData.customised='jpRNAseq_eQTL', RData.location=RData.location, guid=guid, verbose=verbose)
}
res_list <- lapply(include.eQTL, function(x){
if(verbose){
now <- Sys.time()
message(sprintf("Processing %s ...", x), appendLF=TRUE)
}
if(sum(grep("JKscience_TS2A",x,perl=TRUE)) > 0){
# cis-eQTL
cis <- xRDataLoader(RData.customised='JKscience_TS2A', RData.location=RData.location, guid=guid, verbose=verbose)
# either
if(x=='JKscience_TS2A'){
minFDR <- apply(cis[,c(9:12)], 1, base::min, na.rm=TRUE)
df <- data.frame(SNP=cis[,1], Gene=cis[,4], Sig=minFDR, stringsAsFactors=FALSE)
}else{
# only
if(x=='JKscience_TS2A_CD14'){
j <- 9
}else if(x=='JKscience_TS2A_LPS2'){
j <- 10
}else if(x=='JKscience_TS2A_LPS24'){
j <- 11
}else if(x=='JKscience_TS2A_IFN'){
j <- 12
}
ind <- which(!is.na(cis[,j]) & cis[,j]<0.05)
df <- data.frame(SNP=cis[ind,1], Gene=cis[ind,4], Sig=cis[ind,j], stringsAsFactors=FALSE)
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKscience_TS2B",x,perl=TRUE)) > 0){
# cis-eQTL
cis <- xRDataLoader(RData.customised='JKscience_TS2B', RData.location=RData.location, guid=guid, verbose=verbose)
# either
if(x=='JKscience_TS2B'){
minFDR <- apply(cis[,c(9:12)], 1, base::min, na.rm=TRUE)
df <- data.frame(SNP=cis[,1], Gene=cis[,4], Sig=minFDR, stringsAsFactors=FALSE)
}else{
# only
if(x=='JKscience_TS2B_CD14'){
j <- 9
}else if(x=='JKscience_TS2B_LPS2'){
j <- 10
}else if(x=='JKscience_TS2B_LPS24'){
j <- 11
}else if(x=='JKscience_TS2B_IFN'){
j <- 12
}
ind <- which(!is.na(cis[,j]) & cis[,j]<0.05)
df <- data.frame(SNP=cis[ind,1], Gene=cis[ind,4], Sig=cis[ind,j], stringsAsFactors=FALSE)
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(x=='JKscience_TS3A'){
# trans-eQTL
trans <- xRDataLoader(RData.customised='JKscience_TS3A', RData.location=RData.location, guid=guid, verbose=verbose)
minFDR <- apply(trans[,c(9:12)], 1, base::min, na.rm=TRUE)
df <- data.frame(SNP=trans[,1], Gene=trans[,4], Sig=minFDR, stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(x=='JKscience_CD14' | x=='JKscience_LPS2' | x=='JKscience_LPS24' | x=='JKscience_IFN'){
# cis-eQTL
cis <- xRDataLoader(RData.customised='JKscience_TS2A', RData.location=RData.location, guid=guid, verbose=verbose)
# trans-eQTL
trans <- xRDataLoader(RData.customised='JKscience_TS3A', RData.location=RData.location, guid=guid, verbose=verbose)
## both
df <- rbind(cis, trans)
if(x=='JKscience_CD14'){
j <- 9
}else if(x=='JKscience_LPS2'){
j <- 10
}else if(x=='JKscience_LPS24'){
j <- 11
}else if(x=='JKscience_IFN'){
j <- 12
}
ind <- which(!is.na(df[,j]) & df[,j]<0.05)
df <- data.frame(SNP=df[ind,1], Gene=df[ind,4], Sig=df[ind,j], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKng_bcell",x,perl=TRUE)) > 0){
# b cells
res_ls <- xRDataLoader(RData.customised='JKng_bcell', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,5], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,5], stringsAsFactors=FALSE)
if(x=='JKng_bcell'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKng_bcell_cis'){
df <- df_cis
}else if(x=='JKng_bcell_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKng_mono",x,perl=TRUE)) > 0){
# monocytes
res_ls <- xRDataLoader(RData.customised='JKng_mono', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,5], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,5], stringsAsFactors=FALSE)
if(x=='JKng_mono'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKng_mono_cis'){
df <- df_cis
}else if(x=='JKng_mono_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKpg_CD4",x,perl=TRUE)) > 0){
# CD4
res_ls <- xRDataLoader(RData.customised='JKpg_CD4', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JKpg_CD4'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKpg_CD4_cis'){
df <- df_cis
}else if(x=='JKpg_CD4_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKpg_CD8",x,perl=TRUE)) > 0){
# CD8
res_ls <- xRDataLoader(RData.customised='JKpg_CD8', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JKpg_CD8'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKpg_CD8_cis'){
df <- df_cis
}else if(x=='JKpg_CD8_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JKnc_neutro",x,perl=TRUE)) > 0){
# neutrophils
res_ls <- xRDataLoader(RData.customised='JKnc_neutro', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JKnc_neutro'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JKnc_neutro_cis'){
df <- df_cis
}else if(x=='JKnc_neutro_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("WESTRAng_blood",x,perl=TRUE)) > 0){
# neutrophils
res_ls <- xRDataLoader(RData.customised='WESTRAng_blood', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='WESTRAng_blood'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='WESTRAng_blood_cis'){
df <- df_cis
}else if(x=='WESTRAng_blood_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("eQTLGen",x,perl=TRUE)) > 0){
# neutrophils
res_ls <- xRDataLoader(RData.customised='eQTLGen', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,3], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,3], stringsAsFactors=FALSE)
if(x=='eQTLGen'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='eQTLGen_cis'){
df <- df_cis
}else if(x=='eQTLGen_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("JK_nk",x,perl=TRUE)) > 0){
# NK cells
res_ls <- xRDataLoader(RData.customised='JK_nk', RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
if(x=='JK_nk'){
## both
df <- rbind(df_cis, df_trans)
}else if(x=='JK_nk_cis'){
df <- df_cis
}else if(x=='JK_nk_trans'){
df <- df_trans
}
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("GTEx_V4_",x,perl=TRUE)) > 0){
y <- gsub("GTEx_V4_","",x)
cis <- ''
eval(parse(text=paste("cis <- GTEx_V4$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("GTEx_V6p_",x,perl=TRUE)) > 0){
y <- gsub("GTEx_V6p_","",x)
cis <- ''
eval(parse(text=paste("cis <- GTEx_V6p$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("scRNAseq_eQTL_",x,perl=TRUE)) > 0){
y <- gsub("scRNAseq_eQTL_","",x)
cis <- ''
eval(parse(text=paste("cis <- scRNAseq_eQTL$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("jpRNAseq_eQTL_",x,perl=TRUE)) > 0){
y <- gsub("jpRNAseq_eQTL_","",x)
cis <- ''
eval(parse(text=paste("cis <- jpRNAseq_eQTL$", y, sep="")))
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,3], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(x=='Osteoblast_eQTL'){
# cis-eQTL
cis <- xRDataLoader(RData.customised='Osteoblast_eQTL', RData.location=RData.location, guid=guid, verbose=verbose)
df <- data.frame(SNP=cis[,2], Gene=cis[,3], Sig=cis[,7], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("eQTLdb_",x,perl=TRUE)) > 0){
cis <- xRDataLoader(RData.customised=x, RData.location=RData.location, guid=guid, verbose=verbose)
df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,5], stringsAsFactors=FALSE)
#df <- data.frame(SNP=cis[,1], Gene=cis[,2], Sig=cis[,6], stringsAsFactors=FALSE)
df <- cbind(df, Context=rep(x,nrow(df)), stringsAsFactors=FALSE)
}else if(sum(grep("Pi_eQTL_",x,perl=TRUE)) > 0){
# both
res_ls <- xRDataLoader(RData.customised=x, RData.location=RData.location, guid=guid, verbose=verbose)
## cis
df_cis <- data.frame(SNP=res_ls$cis[,1], Gene=res_ls$cis[,2], Sig=res_ls$cis[,6], stringsAsFactors=FALSE)
## trans
df_trans <- data.frame(SNP=res_ls$trans[,1], Gene=res_ls$trans[,2], Sig=res_ls$trans[,6], stringsAsFactors=FALSE)
df <- rbind(df_cis, df_trans)
df <- data.frame(df, Context=x, stringsAsFactors=FALSE)
}else{
df <- NULL
}
return(df)
})
## get data frame (SNP Gene FDR)
SGS <- do.call(rbind, res_list)
############################
# remove Gene if NA
# remove SNP if NA
df_SGS <- SGS[!is.na(SGS[,1]) & !is.na(SGS[,2]),]
############################
if(verbose){
now <- Sys.time()
message(sprintf("%d eGenes are built-in", length(unique(df_SGS[,2]))), appendLF=TRUE)
}
}
###########################
# customised eQTL
###########################
df_SGS_customised <- NULL
if(!is.null(eQTL.customised)){
if(is.vector(eQTL.customised)){
# assume a file
df <- utils::read.delim(file=eQTL.customised, header=TRUE, row.names=NULL, stringsAsFactors=FALSE)
}else if(is.matrix(eQTL.customised) | is.data.frame(eQTL.customised)){
df <- eQTL.customised
}
if(!is.null(df)){
colnames(df) <- c("SNP", "Gene", "Sig", "Context")
SGS_customised <- df
#SGS_customised <- cbind(df, Context=rep('Customised',nrow(df)), stringsAsFactors=FALSE)
############################
# remove Gene if NA
# remove SNP if NA
df_SGS_customised <- SGS_customised[!is.na(SGS_customised[,1]) & !is.na(SGS_customised[,2]),]
############################
if(verbose){
now <- Sys.time()
message(sprintf("%d eGenes are customised for %d contexts", length(unique(df_SGS_customised[,2])), length(unique(df_SGS_customised[,4]))), appendLF=TRUE)
}
}
}
#########################################
df_SGS <- do.call(rbind, list(df_SGS, df_SGS_customised))
#########################################
if(!is.null(df_SGS)){
############################
# remove Gene if ''
# remove SNP if ''
df_SGS <- df_SGS[df_SGS[,1]!='' & df_SGS[,2]!='',]
############################
}
###########################################
if(!is.null(data)){
## replace '_' with ':'
data <- gsub("_", ":", data, perl=TRUE)
## replace 'imm:' with 'chr'
data <- gsub("imm:", "chr", data, perl=TRUE)
data <- unique(data)
## eQTL weight for input SNPs
ind <- match(df_SGS[,1], data)
df_SGS <- data.frame(df_SGS[!is.na(ind),])
if(verbose){
now <- Sys.time()
message(sprintf("A total of %d input SNPs with %d eGenes", length(data), length(unique(df_SGS[,2]))), appendLF=TRUE)
}
}
invisible(df_SGS)
}
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