@@ -1,7 +1,7 @@

 Package: crlmm

 Type: Package

 Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.

-Version: 1.16.4

+Version: 1.16.5

 Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry

 Maintainer: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>

 Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms

@@ -29,14 +29,13 @@

   This vignette provides an overview of the \Rclass{CNSet} class and a

   brief discussion of the underlying infrastructure for large data

-  support with the \Rpackage{ff} package.  This package instantiates

+  support with the \Rpackage{ff} package.  This vignette instantiates

   an object of class \Rclass{CNSet} using a trivial dataset with 3

   files.  As this sample size is too small for estimating copy number

   with the \crlmm{} package, the final section of this vignette loads

   an object created by the analysis of 180 HapMap CEL files

-  (Affymetrix 6.0 platform).  In particular, this object was

-  instantiated by running (1) the \verb+AffymetrixPreprocessCN+

-  vignette and (2) the \verb+copynumber+ vignette.

+  (Affymetrix 6.0 platform).  This object was instantiated by running

+  the \verb+AffyGW+ vignette.

 \end{abstract}

@@ -76,14 +75,14 @@ processing subsets of the markers and/or samples. The functions

 markers and samples to read at once.  In general, specifying smaller

 values should reduce the RAM required for a particular job.  In

 general, smaller values will increase the run-time. In the following

-code-chunk, we declare that \crlmm{} should process 150,000 markers at

-a time (when possible) and 500 samples at a time.  If our dataset

-contained fewer than 500 samples, the \Rfunction{ocSamples} option

+code-chunk, we declare that \crlmm{} should process 100,000 markers at

+a time (when possible) and 200 samples at a time.  If our dataset

+contained fewer than 200 samples, the \Rfunction{ocSamples} option

 would not have any effect.  One can view the current settings for

 these commands, by typing the functions without an argument.

 <<ram>>=

-ocProbesets(50e3)

+ocProbesets(100e3)

 ocSamples(200)

 @

@@ -96,11 +95,12 @@ methods:

 \begin{itemize}

-\item[Approach 1:] during the preprocessing of the raw intensities for Illumina and

-  Affymetrix arrays by the the functions \Rfunction{constructInf} and

-  \Rfunction{genotype}, respectively. (The \Rfunction{genotype} calls

-  the non-exported function \Rfunction{constructAffy} to initialize a

-  \Rclass{CNSet} object for Affymetrix platforms.)

+\item[Approach 1:] during the preprocessing of the raw intensities for

+  Illumina and Affymetrix arrays by the the functions

+  \Rfunction{constructInf} and \Rfunction{genotype},

+  respectively. (The \Rfunction{genotype} calls the function

+  \Rfunction{constructAffy} to initialize a \Rclass{CNSet} object for

+  Affymetrix platforms.)

 \item[Approach 2:] by subsetting an existing \Robject{CNSet} object.

   As per usual, the `[' method can be used to extract a subset of

@@ -110,17 +110,18 @@ methods:

 \end{itemize}

 There are important differences in the underlying data representation

-depending on how the object was instantiated.  In particular, objects

-generated by the functions \Rfunction{constructInf} and

-\Rfunction{genotype} store high-dimensional data on disk rather than

-in memory through protocols defined in the \R{} package \ff{}.  For

-instance, the normalized intensities and genotype calls in a

-\Rclass{CNSet}-instance from approach (1) are \ff{}-derived objects.

-By contrast, when such an objected generated by approach (1) is subset

-by the `[' method, an object of the same class is returned but the

-\Rpackage{ff}-derived objects are coerced to ordinary matrices. Note,

-therefore, that both approaches (1) and (2) may involve substantial

-I/O.

+depending on how the \Rclass{CNSet} object was instantiated.  In

+particular, objects generated by the functions

+\Rfunction{constructInf} and \Rfunction{genotype} store

+high-dimensional data on disk rather than in memory through protocols

+defined in the \R{} package \ff{}.  For instance, the normalized

+intensities and genotype calls in a \Rclass{CNSet}-instance from

+approach (1) are \ff{}-derived objects.  By contrast, when such an

+objected generated by approach (1) is subset by the `[' method, an

+object of the same class is returned but the \Rpackage{ff}-derived

+objects are coerced to ordinary matrices. Note, therefore, that both

+approaches (1) and (2) may involve substantial I/O and that (2) should

+be performed judiciously.

 \subsubsection{Approach 1}

@@ -149,7 +150,7 @@ celfiles <- list.celfiles(path, full.names=TRUE)

 Typically, an object of class \Rclass{CNSet} is instantiated as part

 of the preprocessing and genotyping by calling the function

 \Rfunction{genotype}, as illustrated in the

-\verb+AffymtrixPreprocessCN+ vignette.

+\verb+AffyGW+ vignette.

 <<instantiateToyExample,cache=TRUE>>=

 exampleSet <- genotype(celfiles, batch=rep("1", 3), cdfName="genomewidesnp6")

@@ -163,11 +164,11 @@ ldPath()

 @

 One could also instantiate an object of class \Rclass{CNSet} without

-preprocessing/genotyping by calling the non-exported function

+preprocessing/genotyping by calling the exported function

 \Rfunction{constructAffy} directly using the \Rfunction{:::} operator.

 <<constructAffy,eval=FALSE>>=

-tmp <- crlmm:::constructAffy(celfiles, batch=rep("1", 3), cdfName="genomewidesnp6")

+tmp <- constructAffy(celfiles, batch=rep("1", 3), cdfName="genomewidesnp6")

 @

 The \Rfunction{show} method provides a concise summary of the

@@ -300,15 +301,6 @@ platform.  A consequence of keeping the rows of the assay data

 elements the same for all of the statistical summaries is that the

 matrix used to store genotype calls is larger than necessary.

-% only true for affy.  for illumina, we have intensities stored in 'B'

-%Note that NA's are stored in the slot for normalized 'B' allele

-%intensities:

-

-%<<B.NAs>>=

-%np.index <- which(!is.snp)

-%stopifnot(all(is.na(B(cnSet)[np.index, ])))

-%@

-

 \subsubsection{\texttt{batch} and \texttt{batchStatistics}}

 As defined in Leek \textit{et al.} 2010, \textit{Batch effects are

@@ -402,73 +394,10 @@ varLabels(protocolData(exampleSet))

 protocolData(exampleSet)$ScanDate

 @

-

-%\section{Suggested visualizations}

-%

-%\paragraph{SNR.}

-%

-%A histogram of the signal to noise ratio for the HapMap samples:

-%

-%<<plotSnr, fig=TRUE, include=FALSE>>=

-%open(cnSet$SNR)

-%hist(cnSet$SNR[, ], xlab="SNR", main="", breaks=25, col="lightblue", xlim=c(3, max(cnSet$SNR[])))

-%abline(v=5, lty=2, col="grey")

-%text(3,5, label="SNR range for low \n quality arrays", adj=0, col="grey40")

-%@

-%\begin{figure}

-%  \centering

-%  \includegraphics[width=0.6\textwidth]{copynumber-plotSnr}

-%  \caption{Signal to noise ratios for the HapMap samples. SNRs below 5

-%   for the Affymetrix platform are often samples of lower quality.

-%   Such samples will tend to have much more variable estimates of copy

-%   number.}

-%\end{figure}

-

-

-%\paragraph{One sample at a time: locus-level estimates}

-%

-%Figure \ref{fig:oneSample} plots physical position (horizontal axis)

-%versus copy number (vertical axis) for the first sample.  There is

-%less information to estimate copy number at nonpolymorphic loci;

-%improvements to the univariate prediction regions at nonpolymorphic

-%loci are a future area of research. If the \Rpackage{SNPchip} is

-%available, an idiogram can be added to the existing plotting

-%coordinates as indicated in the following example.

-%

-%<<oneSample, fig=TRUE, width=8, height=4, include=FALSE>>=

-%GT.CONF.THR <- 0.8

-%marker.index <- which(chromosome(cnSet) == 1)

-%cn <- totalCopynumber(cnSet, i=marker.index, j=1)

-%x <- position(cnSet)[marker.index]

-%par(las=1, mar=c(4, 5, 4, 2))

-%plot(x, cn, pch=".",

-%     cex=2, xaxt="n", col="grey60", ylim=c(0,6),

-%     ylab="copy number", xlab="physical position (Mb)",

-%     main=paste(sampleNames(cnSet)[1], ", CHR: 1"))

-%axis(1, at=pretty(x), labels=pretty(x)/1e6)

-%require(SNPchip)

-%invisible(plotCytoband(1, new=FALSE, cytoband.ycoords=c(5.5, 6), label.cytoband=FALSE))

-%@

-

-%\begin{figure}

-%  \includegraphics[width=0.9\textwidth]{copynumber-oneSample}

-%  \caption{\label{fig:oneSample} Total copy number (y-axis) for

-%    chromosome 1 plotted against physical position (x-axis) for one

-%    sample.  Estimates at nonpolymorphic loci are plotted in light

-%    blue.}

-%\end{figure}

-%

-%\clearpage

-%\paragraph{One SNP at a time}

-%

-%Scatterplots of the A and B allele intensities (log-scale) can be

-%useful for assessing the biallelic genotype calls.  This section of

-%the vignette is currently under development.

-

 \section{Trouble shooting with a HapMap example}

 This section uses an object of class \Rclass{CNSet} instantiated by

-the \verb+AffymetrixPreprocessCN+ vignette and saved to a local path

+the \verb+AffyGW+ vignette and saved to a local path

 on our computing cluster indicated by the object \Robject{outdir}

 below.  The \verb+copynumber+ vignette was used to fill out the

 \verb+batchStatistics+ slot of the \Robject{cnSet} object.

@@ -490,17 +419,6 @@ invisible(open(cnSet))

 \subsection{Missing values}

-% There are several reasons for estimates of the allele-specific copy

-% number to have missing values (\texttt{NA}'s). This section briefly

-% elaborates on the source of missing values in the HapMap analysis

-% and discusses possible alternatives to reduce the number of missing

-% values.  Note that allele-specific copy number, 'CA' and 'CB', is

-% not saved in the \Robject{cnSet} object.  Rather, the respective

-% accessors calculate 'CA' and 'CB' on the fly from the normalized

-% intensities and from the marker-specific parameter estimates in the

-% linear model.  In general, a missing value arises when the

-% background or slope parameter was not estimated in the linear

-% model.

 Most often, missing values occur when the genotype confidence scores

 for a SNP were below the threshold used by the

 \Robject{crlmmCopynumber} function. For the HapMap analysis, we used a

@@ -44,40 +44,17 @@ appropriate.

 \hline

  Vignette                &  Platform            &  Annotation package                        &  Scope                                               \\

 \hline

-Infrastructure          & Affy/Illumina                    &

-&  The CNSet container / large data support using the \Rpackage{ff} package            \\

- AffymetrixPreprocessCN  &  Affy 5.0, 6.0       &  genomewidesnp5Crlmm, genomewidesnp6Crlmm  &  Preprocessing and genotyping                        \\

- IlluminaPreprocessCN    &  Illumina  &  several$^\dagger$                                   &  Preprocessing and genotyping                        \\

- copynumber              &  Affy/Illumina       &  N/A                                       &  raw copy number estimates                           \\

-% SmoothingRawCN          &  Affy/Illumina       &  N/A                                       &  smoothing via segmentation or hidden Markov models  \\

+Infrastructure          & Affy/Illumina                    & &  The CNSet container / large data support using the \Rpackage{ff} package            \\

+ AffyGW  &  Affy 5.0, 6.0       &  genomewidesnp5Crlmm, genomewidesnp6Crlmm  &  Preprocessing, genotyping, CN estimation\\

+ IlluminaPreprocessCN    &  Illumina  &  several$^\dagger$ &  Preprocessing, genotyping, CN estimation

 \hline

 \end{tabular}

 \end{center}

 \caption{\label{overview} Vignettes for copy number

-  estimation. $^\dagger$ Annotation packages available for the

-  Illumina platform include  \Rpackage{human370v1cCrlmm}, \Rpackage{human370quadv3cCrlmm},

-  \Rpackage{human550v3bCrlmm}, \Rpackage{human650v3aCrlmm}, \Rpackage{human610quadv1bCrlmm},

-  \Rpackage{human660quadv1aCrlmm}, \Rpackage{human1mduov3bCrlmm}, and \Rpackage{humanomni1quadv1bCrlmm}}

+  estimation. $^\dagger$ See \texttt{annotationPackages()} for a

+  complete listing of supported Illumina/Affy platforms}

 \end{table}

-%We make use of the \R{} package \Rpackage{cacheSweave} for cacheing

-%code chunks that are computationally intensive.  In addition, we

-%indicate that the cached files should be stored in the directory

-%\verb+outdir+.

-

-In general, the workflow is

-\begin{enumerate}

-\item preprocess and genotype the arrays

-  (\verb+AffymetrixPreprocessCN+ for Affymetrix and

-  \verb+IlluminaPreprocessCN+ vignettes for Illumina)

-\item copy number estimation (\verb+copynumber+ vignette)

-%\item inferring regions of copy number gain and loss

-%  (\verb+SmoothingRawCN+ vignette)

-\end{enumerate}

-%The \verb+SmoothingRawCN+ vignette illustrates one approach for

-%interfacing with packages such as \Rpackage{DNAcopy} and

-%\Rpackage{VanillaICE} for identifying regions of copy number gain or

-%loss.

 The \verb+Infrastructure+ vignette provides additional details on the

 \Rclass{CNSet} container used to organize the processed data as well

 as a brief discussion regarding large data support through the \ff{}