To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("Pi")

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Pi

DOI: 10.18129/B9.bioc.Pi    

This is the development version of Pi; for the stable release version, see Pi.

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

Bioconductor version: Development (3.6)

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>

Citation (from within R, enter citation("Pi")):

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("Pi")

Documentation

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 1.5.0
In Bioconductor since BioC 3.4 (R-3.3) (1 year)
License GPL-3
Depends XGR, igraph, dnet, ggplot2, graphics
Imports Matrix, MASS, 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
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
Build Report  

Package Archives

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

Source Package Pi_1.5.0.tar.gz
Windows Binary Pi_1.5.0.zip
Mac OS X 10.11 (El Capitan) Pi_1.5.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/Pi
Package Short Url http://bioconductor.org/packages/Pi/
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