runEgret | R Documentation |
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.
runEgret(b, v, q, m, e, p, g, t)
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: |
Inputs:
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
# 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)
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