CONQUER
Gerard Bouland, Joline Beulens, Joey Nap, Arno van der Slik, Arnaud Zaldumbide, Leen ’t Hart and Roderick Slieker 26 oktober, 2021
- 0.1 Change log
- 0.2 Installation
- 0.3 Overview
- 0.4 Note on
precalculatedargument - 0.5 Note on
pcutoffargument - 0.6 Note on multianalyze
- 0.7 Citation
- 0.8 Example without multianalyze
- 0.9 Example with multianalyze
- 0.10 Figure examples
0.1 Change log
v.1.1.3
- Added support for different P-value cut-offs in multi-analyze (see below)
v.1.1.2
-
Allow users to use precalculated SNPs in GTEx instead of calculating them all.
-
Added overlap tab of QTL to show overlap between types of QTLs
0.2 Installation
0.2.1 Install the depencies
depp <- c("BioCircos","cluster","ggplot2","enrichR","htmlwidgets",
"rio","shiny","shinycssloaders","stringr","viridis",
"DT","coloc","curl","dplyr","grDevices","jsonlite","plotly",
"shinyjs","reshape2","shinythemes","stats","purrr","readr","UpSetR")
BioDepp <- c("IRanges","BiocGenerics","clusterProfiler","GenomicRanges")
# Check present packages
depp.new<-depp[!(depp%in%installed.packages())]
if (length(depp.new)) {
install.packages(depp.new)
}
# Bioconductor
BioDepp.new<-BioDepp[!(BioDepp%in%installed.packages())]
if (length(BioDepp.new)) {
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(BioDepp, type="source")
}
# load packages
sapply(depp, library, character.only = TRUE)
sapply(BioDepp, library, character.only = TRUE)0.2.2 Install CONQUER
Install the the development version from GitLab:
install.packages("devtools")
# Install CoNQUER.db
install.packages("https://github.com/roderickslieker/CONQUER.db/releases/download/v0.1.2/conquer.db_0.1.2.tar.gz", type="source", repos=NULL)
devtools::install_github("roderickslieker/GTEx.Data")
#Install other two dependencies
devtools::install_github("roderickslieker/CONQUER.d3")
devtools::install_github("roderickslieker/CONQUER")0.3 Overview
With the use of two functions, SNPs are summarised and visualised,
namely: summarise() and visualise().
- The
summarise()function is used to collect all data related to SNPs. - The
visualise()function initiates a RStudio Shiny-based dashboard that visualises all relevant plots.
Note: We use the LD data from the API of NIH. You will need to register on the site to obtain a token. Please see:
https://ldlink.nci.nih.gov/?tab=apiaccess
The token is send by email and can be provided as character string.
0.4 Note on precalculated argument
To also allow faster pre-process of SNPs, we also allow users to only
take the precalculated eQTLs from GTEx. Pros: much faster to summarize.
Cons: you will miss interesting QTLs from your results because GTEx only
includes genes with TSSs 1Mb from the SNP. So, when precalculated is
FALSE the GTEx API will be used to test the lead SNP against genes in
cis and trans.
0.5 Note on pcutoff argument
Given that the numbers of individuals per tissue vary in GTEx one may be interested to analyse the data with a more liberal P-value to adjust for the strong correlation between the number of eQTLs and the number of individuals in that dataset. As such the package allows to analyze with three different settings:
-
stringent: only allow eQTLs that match the pValue threshold as defined by GTEx for SNP -
liberal: P<0.001 and P-value at max 2x higher than GTEx’s threshold. -
veryliberal: All eQTLs that are nominally significant P<0.05.
0.6 Note on multianalyze
Multianalyze works best if the number of SNPs is large (for example >50 SNPs). Lower numbers may result in spurious results. Instead one should focus on the single SNPs.
0.7 Citation
CONQUER: an interactive toolbox to understand functional consequences of GWAS hits.
Gerard A Bouland, Joline W J Beulens, Joey Nap, Arno R van der Slik, Arnaud Zaldumbide, Leen M ’t Hart, Roderick C Slieker
NAR Genomics and Bioinformatics, Volume 2, Issue 4, December 2020, lqaa085, https://doi.org/10.1093/nargab/lqaa085
0.8 Example without multianalyze
DIR <- "somedirectory"
library(CONQUER)
summarize(variants = c("rs878521","rs10830963"),
directory=DIR,
precalculated = TRUE,
multiAnalyze=FALSE,
token="sometoken",
tissues=NULL)0.9 Example with multianalyze
The available tissues can be viewed with the following command:
tissues <- conquer.db::gtexTissuesV8The summary files from the example below can also be obtained from https://github.com/roderickslieker/CONQUER.test/tree/master/Test
library(CONQUER)
snps <- c("rs11642430","rs11820019","rs11842871","rs13426680","rs1377807","rs1783541",
"rs1801212","rs1801645","rs2268078","rs2581787","rs34855406","rs3802177",
"rs3810291","rs4148856","rs5213","rs6011155","rs601945","rs75423501",
"rs8010382","rs8046545")
CONQUER::summarize(variants = snps,
directory=DIR,
multiAnalyze=TRUE,
precalculated = TRUE,
token=NULL,
tissues=c("Pancreas","Muscle_Skeletal","Liver"))
visualize(directory = "somedirectory", SNPs = snps)0.10 Figure examples
0.10.1 Modules
0.10.2 Enrichment
0.10.3 Pathways shared by tissues
0.10.4 LD
0.10.5 Chromosomal interactions
0.10.6 Chromatin states
0.10.7 eQTLs
0.10.8 Gene expression








