---
title: 'Using RQT, an R package for gene-level meta-analysis'
author: "Ilya Y. Zhbannikov"
date: "`r BiocStyle::doc_date()`"
output: BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{Tutorial for rqt package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


## Overview

Despite the recent advances of modern GWAS methods, 
it is still remains an important problem of addressing 
calculation an effect size and corresponding p-value 
for the whole gene rather than for single variant. 
We developed an R-package rqt, which offers gene-level GWAS meta-analysis. 
The package can be easily included into bioinformatics pipeline 
or used as stand-alone. 
Contact: ilya.zhbannikov@duke.edu for questions of 
usage the \texttt{rqt} or any other issues.

Below we provide several examples that show GWAS 
meta-analysis on gene-level layer.

### Methods in brief

The workflow of gene-level meta analysis consists of the following steps: 
(i) reducing the number of predictors, thereby alleviating 
correlation problem in variants (accounting for LD); 
(ii) then the regression mod-el is fitted on the reduced dataset 
to obtain corresponding regression coefficient ("effect sizes"); 
(iii) these coefficients are then to be pooled into a total index 
representing a total gene-level effect size and corresponding 
statistics is calculated. P- and q- values are then calculated 
using this statistics from asymptotic approximation or permutation 
procedure; (iv) the final step is combining gene-level p-values 
calculated from each study with Fisher's combined probability method.

## Installation of \emph{rqt} package

In order to install the \emph{rqt} package, the user must first install R (\url{http://www.r-project.org}). After that, \emph{rqt} can be installed with:

```{r, echo=TRUE, eval=FALSE}
devtools::install_github("izhbannikov/rqt", buildVignette=TRUE)
```

## Data description

### Single dataset

In \texttt{rqt} requires the following datasets: 
(i) \texttt{phenotype} (a \texttt{N} by 1) matrix (i.e. a vector); 
and (ii) \texttt{genotype} - an object of class 
\texttt{SummarizedExperiment} containing one assay:
(a \texttt{N} by \texttt{M}) matrix, where 
\texttt{N} - is the total 
number of individuals in the study and \texttt{M} is the total number 
of genetic variants. Optionally, \texttt{rqt} can accept covariates, 
in form of \texttt{N} by \texttt{K} matrix, where \texttt{K} 
is the total number of 
covariates used in the study. Phenotype can be dichotomous 
(0/1, where 1 indicates control and 0 case).

### Meta-analysis

In meta-analysis, \texttt{rqt} requires a list of \texttt{M} 
(\texttt{M} - number 
of datasets used in meta-analysis) and optionally it accepts
covariates in form described above.

## Examples

### Gene-level analysis on a single dataset

#### Dichotomous phenotype
```{r, echo=TRUE, message=FALSE}
library(rqt)

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)
res <- geneTest(obj, method="pca", out.type = "D")
print(res)
```

#### Continuous phenotype

```{r, echo=TRUE, message=FALSE}
library(rqt)

data <- data.matrix(read.table(system.file("extdata/test.cont1.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)
res <- geneTest(obj, method="pca", out.type = "C")
print(res)
```

#### Preprocessing with Partial Least Square regression (PLS)

This method is used for continous outcome, i.e. out.type = "C".

```{r, echo=TRUE, message=FALSE}

library(rqt)

data <- data.matrix(read.table(system.file("extdata/test.cont1.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)
res <- geneTest(obj, method="pls", out.type = "C")
print(res)
```

#### Preprocessing with Partial Least Square Discriminant Analysis (PLS-DA)

This method of data preprocessing is used for dichotomous outcome.

```{r, echo=TRUE, message=FALSE}

library(rqt)

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)
# Not yet supported, sorry!
#res <- geneTest(obj, method="pls", out.type = "D", scale = TRUE)
print(res)
```

#### Using additional covariates

Quite often, researchers want to supply not only genetic 
data but also specific covariates, 
representic some physiological parameters or environment 
(for example, to evaluate 
hyphoteses of gene-environment interactions). 
In such cases, the package \texttt{rqt} 
can accept additional covariates, in form of 
\texttt{N} by \texttt{K} matrix, as provided below:

```{r, echo=TRUE, message=FALSE}

library(rqt)

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)
covars <- read.table(system.file("extdata/test.cova1.dat",package="rqt"), 
    header=TRUE)
obj <- rqt(phenotype=pheno, genotype=geno.obj, covariates = covars)
res <- geneTest(obj, method="pca", out.type = "D")
print(res)
```

For continous phenotype:

```{r, echo=TRUE, message=FALSE}

library(rqt)

data <- data.matrix(read.table(system.file("extdata/test.cont1.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)
covars <- read.table(system.file("extdata/test.cova1.dat",package="rqt"), 
    header=TRUE)
obj <- rqt(phenotype=pheno, genotype=geno.obj, covariates = covars)
res <- geneTest(obj, method="pca", out.type = "C")
print(res)
```

### Meta-analysis

```{r, echo=TRUE, message=FALSE}
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)
```


## Session information
```{r, echo=TRUE}
sessionInfo()
```
