runEgret: Run EGRET in R

In netZoo/netZooR: Unified methods for the inference and analysis of gene regulatory networks

Run EGRET in R

Description

Description: NOTE: Beta version. EGRET infers individual-specific gene regulatory networks using inidividual level data - a genotype vcf file (v) and QBiC binding predictions (q) - as well as population/reference level data - eQTLs (b), a motif-gene prior (m), PPI network (p), and gene expression (e). An annotation file g is also used to map TF names to their corresponding ensemble ids.

Usage

runEgret(b, v, q, m, e, p, g, t)

Arguments

b	: Data frame of eQTL data, each row containing an eQTL which exist within motif regions adjacent to the eGene, with columns TF, gene, variant position,variant chromosome, eQTL beta value.
v	: Data frame of VCF file containing SNPs of the individual in question
q	: Data frame of QBiC predictions of the effect of eQTL variants on TF binding. Each row represents an eQTL variant with a predicted negative (disruptive) effect on the binding of the TF corresponding to the motif in which the eQTL variant resides. Colums are: eQTL variant as chr[chrNum]_position, TF, adjacent eGene, QBiC binding effect size and QBiC binding effect (should be negative)
m	: Motif prior data frame. Each row represents an edge in the bipartite motif prior, with columns TF, gene and edge weight. The edge weight should be 1 or 0 based on the presence/absence of the TF motif in the promoter region of the gene.
e	: Gene expression data frame in which each row represents a gene and each column represents the expression of that gene in a sample. The first column should contain gene IDs.
p	: PPI network data frame. Each row represents an edgem with columns TF, TF and interaction weight.
g	: Data frame mapping gene names to gene ids, with columns containing the gene ID the corresponding gene name.
t	: A string containing a name for the EGRET run. Output files will be labelled with this tag. Outputs:

Details

Inputs:

Value

EGRET : Predicted genotye-specific gene regulatory network saved as tag_egret.RData

BASELINE : A Baseline (PANDA) genotype-agnostic gene regulatory network saved as tag_panda.RData

Examples

# Run EGRET algorithm
toy_qbic_path <- system.file("extdata", "toy_qbic.txt", package = "netZooR", 
mustWork = TRUE)
toy_genotype_path <- system.file("extdata", "toy_genotype.vcf", 
package = "netZooR", mustWork = TRUE)
toy_motif_path <- system.file("extdata", "toy_motif_prior.txt", 
package = "netZooR", mustWork = TRUE)
toy_expr_path <- system.file("extdata", "toy_expr.txt", 
package = "netZooR", mustWork = TRUE)
toy_ppi_path <- system.file("extdata", "toy_ppi_prior.txt", 
package = "netZooR", mustWork = TRUE)
toy_eqtl_path <- system.file("extdata", "toy_eQTL.txt", 
package = "netZooR", mustWork = TRUE)
toy_map_path <- system.file("extdata", "toy_map.txt", 
package = "netZooR", mustWork = TRUE)
qbic <- read.table(file = toy_qbic_path, header = FALSE)
vcf <- read.table(toy_genotype_path, header = FALSE, sep = "\t", 
stringsAsFactors = FALSE, 
colClasses = c("character", "numeric", "character", "character", "character", 
"character", "character", "character", "character", "character"))
motif <- read.table(toy_motif_path, sep = "\t", header = FALSE)
expr <- read.table(toy_expr_path, header = FALSE, sep = "\t", row.names = 1)
ppi <- read.table(toy_ppi_path, header = FALSE, sep = "\t")
qtl <- read.table(toy_eqtl_path, header = FALSE)
nameGeneMap <- read.table(toy_map_path, header = FALSE)
tag <- "my_toy_egret_run"

runEgret(qtl,vcf,qbic,motif,expr,ppi,nameGeneMap,tag)

file.remove("my_toy_egret_run_egret.RData")
file.remove("my_toy_egret_run_panda.RData")
file.remove("priors_my_toy_egret_run.txt")

netZoo/netZooR documentation built on Oct. 16, 2024, 10:23 p.m.