\HeaderA{adjBaseOlig.error.step2}{Evaluates LPE variance function of M for quantiles of A within and 
experimental condition. It is based on the adaptive number of intervals.}{adjBaseOlig.error.step2}
\keyword{methods}{adjBaseOlig.error.step2}
\begin{Description}\relax
Similar to adjBaseOlig.error.step1 function, except that now the number of bins
are chosen adaptively instead of fixed 100.
\end{Description}
\begin{Usage}
\begin{verbatim}
  adjBaseOlig.error.step2(y, baseOlig.error.step1.res, df=10, stats=median, setMax=FALSE, min.genes.int=10, div.factor=1)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{y}] y is a preprocessed matrix or data frame of expression 
intensities in which columns are expression intensities for
a particular experimental condition and rows are genes.
\item[\code{baseOlig.error.step1.res}] It is the result obtained from 
adjBaseOlig.error.step1 function, in which number of bins are fixed=100
\item[\code{df}] df stands for degrees of freedom. It is used in 
smooth.spline function to interpolate the variances
of all genes. Default value is 10.
\item[\code{stats}] It determines whether mean or median is to be used
for the replicates
\item[\code{setMax}] If T then all variances below the max variance in the
ordered distribution of variances are set to the maximum
variance. If F then variances are left as is (recommended)
\item[\code{min.genes.int}] Determines the minimum number of genes in a subinterval for selecting the adaptive intervals.
\item[\code{div.factor}] Determines the factor by which sigma needs to be divided for
selecting adaptive intervals.
\end{ldescription}
\end{Arguments}
\begin{Value}
Returns object of class baseOlig comprising a data frame with 2 columns: A
and var M, and rows for each quantile specified. The A column contains
the median values of A for each quantile/bin and the M columns contains 
the pooled variance of the replicate chips for genes within each quantile/bin.
\end{Value}
\begin{Author}\relax
Carl Murie \email{carl.murie@mcgill.ca},
Nitin Jain \email{nitin.jain@pfizer.com}
\end{Author}
\begin{References}\relax
J.K. Lee and M.O.Connell(2003). \emph{An S-Plus library for the analysis of differential expression}. In The Analysis of Gene Expression Data: Methods and Software. Edited by G. Parmigiani, ES Garrett, RA Irizarry ad SL Zegar. Springer, NewYork.

Jain et. al. (2003) \emph{Local pooled error test for identifying
differentially expressed genes with a small number of replicated microarrays}, Bioinformatics, 1945-1951.

Jain et. al. (2005) \emph{Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data}, BMC Bioinformatics, Vol 6, 187.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{lpeAdj}{lpeAdj}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}

  # Loading the library and the data
  library(LPEadj)
  data(Ley)
  
  dim(Ley)
  # Gives 12488 by 7
  Ley[1:3,]
  # Returns 
  #       ID           c1   c2   c3    t1    t2    t3
  # 1  AFFX-MurIL2_at 4.06 3.82 4.28 11.47 11.54 11.34
  # 2 AFFX-MurIL10_at 4.56 2.79 4.83  4.25  3.72  2.94
  # 3  AFFX-MurIL4_at 5.14 4.10 4.59  4.67  4.71  4.67

  Ley[1:1000,2:7] <- preprocess(Ley[1:1000,2:7],data.type="MAS5")
  # Finding the baseline distribution of subset of the data
  # condition one (3 replicates)
  var.1 <- adjBaseOlig.error.step1(Ley[1:1000,2:4], q=0.01, df=10)
  dim(var.1)
  var.11 <- adjBaseOlig.error.step2(Ley[1:1000,2:4], var.1, df=10)
  # Returns a matrix of 1000 by 2 (A,M) format
\end{ExampleCode}
\end{Examples}

