%\VignetteIndexEntry{ASSET Vignette}
%\VignettePackage{ASSET}
%\VigetteDepends{ASSET}

\documentclass[a4paper]{article}


\begin{document}

\title{ASSET(Association analysis for SubSETs) Package}
\maketitle

\section*{Introduction}
The ASSET package consists of two main functions: (1) h.traits and (2) h.types. The function h.traits
is suitable for conducting meta-analysis of possibly different traits when summary level data are available from individual
studies. The function allows for correlation among different studies/traits, which, for example, may arise due to shared subjects across studies.
This function can also be used to conduct "meta-analysis" across multiple correlated traits on the same individuals by appropriately specifying the correlation
matrix for the multivariate trait. Input arguments to this function are vectors/matrices of the estimated log-odds ratios, standard errors and
number of cases and controls for each SNP and study. 
The function h.types is suitable for analysis of case-control studies when cases consist of distinct disease subtypes. This
function assumes individual level data are available. The main input argument for h.types is a data frame containing the SNP variables,
response variable and covariates for all subjects.


<<start>>=
library(ASSET)
@

\section*{Examples of h.traits}

Get the path to the data.
<<data file>>=
datafile <- system.file("sampleData", "vdata.rda", package="ASSET")
@


Load the data frames. There are 4 data frames, data1 - data4 for the 4 independent studies.
Each study has the SNPs SNP1-SNP3 genotyped, and information on each subject's age and
case-control status.
Each SNP is coded as the number of copies of the minor allele or NA for missing genotypes.
<<load data>>=
load(datafile)
data1[1:5, ]

SNPs    <- paste("SNP", 1:3, sep="")
nSNP    <- length(SNPs)
studies <- paste("STUDY", 1:4, sep="") 
nStudy  <- length(studies)
@

Let us determine the number of non-missing cases and controls for each SNP and study.
<<ncase ncontrol>>=
case    <- matrix(data=NA, nrow=nSNP, ncol=nStudy) 
control <- matrix(data=NA, nrow=nSNP, ncol=nStudy) 

for (i in 1:nStudy) {
  data <- eval(parse(text=paste("data", i, sep="")))
  caseVec <- data[, "CC"] == 1
  controlVec <- !caseVec
  for (j in 1:nSNP) {
    temp <- !is.na(data[, SNPs[j]])
    case[j, i] <- sum(caseVec & temp, na.rm=TRUE)
    control[j, i] <- sum(controlVec & temp, na.rm=TRUE)
  }
}
case
control
@

Run a logistic regression for each SNP and study
<<log reg>>=
beta  <- matrix(data=NA, nrow=nSNP, ncol=nStudy) 
sigma <- matrix(data=NA, nrow=nSNP, ncol=nStudy) 
for (i in 1:nStudy) {
  data <- eval(parse(text=paste("data", i, sep="")))
  for (j in 1:nSNP) {
    data[, "SNP"] <- data[, SNPs[j]]
    fit <- glm(CC ~ AGE + SNP, data=data, family=binomial())
    coef <- summary(fit)$coefficients
    beta[j, i] <- coef["SNP", 1]
    sigma[j, i] <- coef["SNP", 2]
  }
}
beta
sigma

@

Call the h.traits function. Since the studies are independent, we do not need to specify the
cor option.
<<h.traits>>=
res <- h.traits(SNPs, studies, beta, sigma, case, control, meta=TRUE)
@

Compute a summary table. Notice that in the Subset.2sided results, the first 2 SNPs have missing
values for OR.2, CI.low.2, and CI.high.2 since the estimated betas were all positive for these SNPs.
<<summary table>>=
h.summary(res)
@

Intead of searching over all possible subsets, let us define our own subset function
to determine which nsubsets to search over. We will only consider subsets where the first m
traits are in the subset (m = 1, 2, ...). The DLM p-value will also be computed using only
these subsets.
<<subset function>>=
sub.def <- function(logicalVec) {
  sum <- sum(logicalVec)  
  ret <- all(logicalVec[1:sum])
  ret
}
@

Call the h.traits function with the zmax.args pval.args options defined
<<h.traits 2>>=
res <- h.traits(SNPs, studies, beta, sigma, case, control, meta=TRUE, 
         zmax.args=list(sub.def=sub.def), pval.args=list(sub.def=sub.def))
@

<<summary table 2>>=
h.summary(res)
@


\section*{Session Information}
<<sessionInfo>>=
sessionInfo()
@ 

\end{document}