\HeaderA{sagelibrary.simulate}{Simulate SAGE libraries}{sagelibrary.simulate}
\keyword{misc}{sagelibrary.simulate}
\begin{Description}\relax
Function to simulate SAGE libraries with sequencing errors.
\end{Description}
\begin{Usage}
\begin{verbatim}
sagelibrary.simulate(taglength = 4, lambda = 1000, mean.error = 0.01,
                 error.sd = 1, withintagerror.sd = 0.2,
                 ngenes = min(4^taglength, 1e+05), base.lib = NULL,
                 libseed = -1, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{taglength}] Tag length for library. 
\item[\code{lambda}] Aproximate size of library. 
\item[\code{mean.error}] Mean amount of sequencing errors. 
\item[\code{error.sd}] Standard deviation for sequencing errors. 
\item[\code{withintagerror.sd}] Standard deviation for sequencing
errors within tags. 
\item[\code{ngenes}] Number of genes to generate tags from. 
\item[\code{base.lib}] Simulate library based on tags in other lib and
create variations. 
\item[\code{libseed}] Seed for random number generator. 
\item[\code{...}] Arguments passed to em.estimate. 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
We set the number of possible transcripts and assign a random SAGE tag
to each of them out of all 4\bsl{}textasciicircum{}taglength possible SAGE tags. For each
SAGE tag a random proportion p within the library is generated from a
log-normal distribution, and the proportions are then adjusted to have
a sum of 1. The true counts of a tag are simulated by sampling from
Poisson distributions with parameters p lambda, where p is the
proportion of the tag in the library and lambda is a parameter for
setting the size of the library. The simulation of the sequencing
errors is done on each individual occurrence of a tag sequence. For
each tag sequence a mean sequencing quality value is generated from a
log-normal distribution. The individual quality values for each base
are then generated from log-normal distributions with means equal to
the simulated sequencing quality values for the tag sequences. We have
noticed that with experimentally generated data the within tag
sequence variation of sequencing quality values is usually about 1/5
of the between tag sequence variation. From each true tag sequence one
observed tag sequence is generated using the simulated quality values
of the true sequence as the multinomial probabilities, i.e. replacing
each base with either one of the 3 other bases with the probability
specified by the sequencing quality value of that base. The counts of
these generated tags are then summed to represent the observed
tags. When generating several simulated libraries for comparisons, we
use the same proportions of the genes for all libraries, replacing up
to 1/3 of the proportions by proportions with a known differential
factor.
\end{Details}
\begin{Author}\relax
Tim Beissbarth
\end{Author}
\begin{References}\relax
\url{http://tagcalling.mbgproject.org}
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{sage.library}{sage.library}},
\code{\LinkA{error.correction}{error.correction}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
library(sagenhaft)
testlib1 <- sagelibrary.simulate(taglength=10, lambda=10000,
                             mean.error=0.01)
testlib2 <- sagelibrary.simulate(taglength=10, lambda=20000,
                             mean.error=0.02, base.lib=testlib1)
testlib3 <- sagelibrary.simulate(taglength=10, lambda=10000,
                             mean.error=0.01, libseed=testlib1$seed)
\end{ExampleCode}
\end{Examples}

