README.md

In rqt: rqt: utilities for gene-level meta-analysis

rqt: utilities for gene-level meta-analysis

Installation

Release version

rqt is currently accepted into Bioconductor: https://bioconductor.org/packages/rqt/ and hence requires the version of R >=3.5 and the version of Bioconductor of 3.6. If you have these installed, then rqt can be installed from Github using BiocManager:

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

Development version

The lastest version of rqt can be downloaded using Bioc-devel:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install(version = "devel")
BiocManager::install("rqt")

OR from GitHub:

devtools::install_github("izhbannikov/rqt@devel", buildVignette=TRUE)

Usage

Single dataset

library(rqt)
# Loading data and constructing the objects #
data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
    package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
# Analysis #
res <- geneTest(obj, method="pca", out.type = "D")
print(res)

Multiple datasets (meta analysis)

library(rqt)
data1 <- data.matrix(read.table(system.file("extdata/phengen2.dat",
                                            package="rqt"), skip=1))
pheno <- data1[,1]
geno <- data1[, 2:dim(data1)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj1 <- rqt(phenotype=pheno, genotype=geno.obj)

data2 <- data.matrix(read.table(system.file("extdata/phengen3.dat",
                                            package="rqt"), skip=1))
pheno <- data2[,1]
geno <- data2[, 2:dim(data2)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj2 <- rqt(phenotype=pheno, genotype=geno.obj)

data3 <- data.matrix(read.table(system.file("extdata/phengen.dat",
                                            package="rqt"), skip=1))
pheno <- data3[,1]
geno <- data3[, 2:dim(data3)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj3 <- rqt(phenotype=pheno, genotype=geno.obj)

res.meta <- geneTestMeta(list(obj1, obj2, obj3))
print(res.meta)

rqt documentation built on Nov. 8, 2020, 4:50 p.m.