Pi

DOI: 10.18129/B9.bioc.Pi    

This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see Pi.

Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level

Bioconductor version: 3.9

Priority index or Pi is developed as a genomic-led target prioritisation system, with the focus on leveraging human genetic data to prioritise potential drug targets at the gene, pathway and network level. The long term goal is to use such information to enhance early-stage target validation. Based on evidence of disease association from genome-wide association studies (GWAS), this prioritisation system is able to generate evidence to support identification of the specific modulated genes (seed genes) that are responsible for the genetic association signal by utilising knowledge of linkage disequilibrium (co-inherited genetic variants), distance of associated variants from the gene, evidence of independent genetic association with gene expression in disease-relevant tissues, cell types and states, and evidence of physical interactions between disease-associated genetic variants and gene promoters based on genome-wide capture HiC-generated promoter interactomes in primary blood cell types. Seed genes are scored in an integrative way, quantifying the genetic influence. Scored seed genes are subsequently used as baits to rank seed genes plus additional (non-seed) genes; this is achieved by iteratively exploring the global connectivity of a gene interaction network. Genes with the highest priority are further used to identify/prioritise pathways that are significantly enriched with highly prioritised genes. Prioritised genes are also used to identify a gene network interconnecting highly prioritised genes and a minimal number of less prioritised genes (which act as linkers bringing together highly prioritised genes).

Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight

Maintainer: Hai Fang <hfang at well.ox.ac.uk>

Installation

To install this package, start R (version "3.6") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("Pi")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("Pi")

 

HTML	R Script	Pi User Manual (R/Bioconductor package)
PDF		Reference Manual
Text		NEWS

Details

biocViews	GeneExpression, GeneTarget, Genetics, GenomeWideAssociation, GraphAndNetwork, HiC, LinkageDisequilibrium, Network, Pathways, Software
Version	1.12.0
In Bioconductor since	BioC 3.4 (R-3.3) (3 years)
License	GPL-3
Depends	XGR, igraph, dnet, ggplot2, graphics
Imports	Matrix, ggbio, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest, glmnet, Gviz, lattice, caret, plot3D, stats
LinkingTo
Suggests	foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, ExpressionAtlas, ggforce, fgsea, pathview, tidyr, dplyr
SystemRequirements
Enhances
URL	http://pi314.r-forge.r-project.org
BugReports	https://github.com/hfang-bristol/Pi/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	Pi_1.12.0.tar.gz
Windows Binary	Pi_1.12.0.zip
Mac OS X 10.11 (El Capitan)	Pi_1.12.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/Pi
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/Pi
Package Short Url	https://bioconductor.org/packages/Pi/
Package Downloads Report	Download Stats