# rqt: utilities for gene-level meta-analysis
## Installation
### Release version
```rqt``` is currently accepted into Bioconductor: https://github.com/Bioconductor/Contributions/issues/212
and hence requires the version of R >=3.4 and the version of Bioconductor of 3.5.
If you have these installed, then ```rqt``` can be installed from Github using biocLite:
```
source("https://bioconductor.org/biocLite.R")
biocLite("rqt")
```
### Developing version
The last version of rqt can be downloaded using devtools:
```
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)
```
