\HeaderA{giaever}{Yeast Gene-Knockout Fitness Data}{giaever}
\keyword{datasets}{giaever}
\begin{Description}\relax
This data set contains fitness deficiency scores from gene knockout experiments involving yeast grown under a variety of altered environments (e.g. acid, heat, sorbitol, etc.)
\end{Description}
\begin{Usage}
\begin{verbatim}data(giaever)\end{verbatim}
\end{Usage}
\begin{Format}\relax
A matrix whose rows are the 5922 genes knocked out and whose columns are the 32 experimental conditions.
\end{Format}
\begin{Source}\relax
\url{http://gobi.lbl.gov/YeastFitnessData}
\end{Source}
\begin{References}\relax
Giaever, G. et al. 2002 ``Functional profiling of the Saccharomyces cerevisiae genome.'' Nature \bold{418}, 387--391.
\end{References}
\begin{Examples}
\begin{ExampleCode}
data(giaever)

## Find the 3000 most variable genes, according to sd/mean:
varMeas <- function(vec, na.rm=TRUE) 
{
    if(na.rm)
      vec <- vec[!is.na(vec)]
    if(length(vec) == 0)
      measure <- NA
    else
      measure <- sd(vec)/mean(vec)
    return(measure)
}

variability <- apply(giaever, 1, varMeas)

rks <- rank(variability)

giaever3000 <- giaever[rks>length(rownames(giaever))-3000,]
\end{ExampleCode}
\end{Examples}

