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\begin{document}
<<setup1, include=FALSE>>=
require(knitr)
@
<<setup2, include=FALSE>>=
options(width=80)  # make the printing fit on the page
set.seed(1121)     # make the results repeatable
stdt<-date()
@

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% title page
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
    \title{\pkgname}
    \subtitle{Fast Sequence Alignment for High-throughput shRNA and CRISPR
Screens}
    \author{Jiaxing Lin \and Jeremy Gresham \and Tongrong Wang \and So Young
Kim \and James Alvarez \and Jeffrey S. Damrauer \and Scott Floyd \and Joshua
Granek \and Andrew Allen \and Cliburn Chan \and Jichun Xie \and Kouros Owzar}
    \date{2019-04-25}
    \titlepage
\end{frame}

\begin{frame}{Outline}
    \tableofcontents[]
\end{frame}
\small

\section{Introduction}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Introduction page 2
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Introduction}
This document provides comprehensive instructions and examples for using the
\pkgname{} package to perform alignment of CRISPR or shRNA reads to a library
of sequences. The alignment is based on a 'Trie' data structure, a tree like
data structure for fast searching.  The algorithm is implemented in C++,
and ported to \R{} by \Rcpp{}. \\
Features of this package include:
\begin{enumerate}
    \item $O(N\bar{m})$ computational complexity (where $N$ is the number of
    sequence reads in the sample, and $\bar{m}$ is the average read length.)
    \item Short sequences alignment that can be applied
    to barcode matching and similar problems.
    \item Allows errors during the alignment. Supports both hamming and
    edit distance matching with constraints.
    \item Alignment qualities are evaluated and ambiguous alignments are
    resolved using Bayes' classifier.
    \item Support for user-defined matching probability models in evaluating
    the alignment quality.
\end{enumerate}
Note: using the 'Trie' data structure only unique barcode sequences in the
library are kept.
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% bcseq_hamming arguments specfication page 3
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{\pkgname{}\_hamming}
<<bcSeq, eval=FALSE>>=
bcSeq_hamming(sampleFile, libFile, outFile, misMatch = 2, tMat = NULL,
    numThread = 4, count_only = TRUE, detail_info = FALSE)
@
\pkgname{}\_hamming can be used for performing alignment using
hamming distance.
\vspace{3mm}
Function arguments:\\
\begin{description}
    \item[\texttt{sampleFile}:] (string or DNAStringSet) If a string it is the
    sample filename and needs to be a fastq file.
    \item[\texttt{libFile}:] (string or DNAStringSet) If a string it is the 
    library filename, needs to be a fasta
    or fastq file.  libFile and sampleFile must have the same type.
    \item[\texttt{outFile}:] (string) output filename.
    \item[\texttt{misMatch}:] (integer) the number of maximum mismatches
    or indels allowed in the alignment.
    \item[\texttt{tMat}:] (two column dataframe) prior probability of a
    mismatch given a sequence. The first column is the prior sequence,
    the second column is the prior error probability. The default values of
    prior error probability for all different sequences are 1/3.
    \item[\texttt{numThread}:] (integer) the number of threads for parallel
    computing. The default is 4.
\end{description}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% bcseq_hamming arguments specfication page 4
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{\pkgname{}\_hamming}
Function arguments continued:\\
\begin{description}
\item[\texttt{count\_only}:]
(bool) option for function returns (default is \texttt{TRUE}). If set to
\texttt{FALSE}, returns a list contains a vector of read IDs, and a vector of
barcode IDs, and an alignment probability matrix between
all the reads and barcodes.
The vectors of read/barcode IDs serve as the row and column names for the
alignment probability matrix respectively. Examples of the probability matrix
are provided in the example section. Note that the probability matrix is a
sparse matrix.
\item[\texttt{detail\_info}:]
(bool) option for controlling function returns, default to
be \texttt{FALSE}. If set to \texttt{TRUE}, a file contain read indexes 
and library indexes reads aligned will be created with filename 
\texttt{\$(outFile).txt}.
\end{description}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% bcseq_hamming return specfication page 5
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{\pkgname{}\_hamming Return Values}
Function returns:
\begin{description}
\item[\texttt{default}:]
A csv count table is created and written to user specified file.
The .csv file contains two columns, the first column is the sequences of the
barcodes, and the second columns is the number
of reads that aligned to the barcodes. A list containing a vector of read IDs
and barcode IDs is returned to R.
\end{description}
Extra optional returns:\\
\begin{description}
\item[\texttt{count\_only = FALSE}:]
If set to \texttt{FALSE}, \pkgname{} will return a sparse matrix in the list
containing the vectors of read IDs and barcode IDs. The rows of the matrix
correspond to the vector of read IDs, and the columns correspond to the
vector of barcode IDs.
\end{description}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%bcseq_edit argument specfication page 6
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{\pkgname{}\_edit}
<<bcSeq_edit, eval=FALSE>>=
bcSeq_edit(sampleFile, libFile, outFile, misMatch = 2, tMat = NULL,
    numThread = 4, count_only = TRUE, userProb = NULL,
    gap_left = 2, ext_left = 1, gap_right = 2,
    ext_right = 1, pen_max = 5, detail_info = FALSE)
@
\pkgname{}\_edit can be used for performing alignment using edit distance.
\vspace{3mm}
Function arguments for \pkgname{}\_edit:\\
\begin{description}
    \item[\texttt{sampleFile}:] (string or DNAStringSet) If a string it is the
    sample filename and needs to be a fastq file.
    \item[\texttt{libFile}:] (string or DNAStringSet) If a string it is the 
    library filename, needs to be a fasta
    or fastq file.  libFile and sampleFile must have the same type.
    \item[\texttt{outFile}:] (string) output filename.
    \item[\texttt{misMatch}:] (integer) the number of maximum mismatches
    allowed in the alignment.
    \item[\texttt{tMat}:]
    (two column dataframe) prior probability of a mismatch given a sequence.
    The first column is the prior sequence, the second column is the
    error rate. The default value for all prior sequences is 1/3.
    \item[\texttt{numThread}:] (integer) the number of threads for
    parallel computing, default to be 4.
\end{description}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%bcseq_edit argument specfication page 7
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{\pkgname{}\_edit}
Function arguments continued:\\
\begin{description}
\item[\texttt{count\_only}:]
(bool) option for function returns (default is \texttt{TRUE}). If set to
\texttt{FALSE}, returns a list contains a vector of read IDs, and a vector of
barcode IDs, and an alignment probability matrix between
all the reads and barcodes.
The vectors of read/barcode IDs serve as the row and column names for the
alignment probability matrix respectively. Examples of the probability matrix
are provided in the example section. Note that the probability matrix is a
sparse matrix.
\item[\texttt{userProb}:]
(function) an vectorized R function taking three arguments:
\texttt{userProb(max\_pen, prob, pen\_val)}, \texttt{max\_pen} is the maximum
penalty allowed, \texttt{prob} is a vector containing the combined
match/misMatch probabilities for each unique alignment between a given read
and barcode, \texttt{pen\_val} is a vector containing total penalties for the
reads and barcodes in the same order. \texttt{userProb} is a way for the user
to control which alignments are considered. The default value is \texttt{NULL},
indicating the alignment probabilities are to be computed following the model
presented in the Alignment Probability Model section and a comprehensive
example is given there.
\item[\texttt{detail\_info}:]
(bool) option for controlling function returns, default to
be \texttt{FALSE}. If set to \texttt{TRUE}, a file contain read indexes 
and library indexes reads aligned will be created with filename 
\texttt{\$(outFile).txt}. Not avaliable for user-defined probablity model case.
\end{description}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%bcseq_edit argument specfication page 8
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{\pkgname{}\_edit function}
Extra function arguments for tuning alignment based on edit distance:\\
\begin{description}
    \item[\texttt{gap\_left}:] (double) Penalty score for deleting a base
    for the reads.
    \item[\texttt{ext\_left}:] (double) Penalty score for extending a deletion
    of base for the reads.
    \item[\texttt{gap\_right}:] (double) Penalty score for inserting a base
    \item[\texttt{ext\_right}:] (double) Penalty score for extending an
    insertion
    \item[\texttt{pen\_max}:] (double) Maximum penalty allowed for any
    alignment
\end{description}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%bcseq_edit return specfication page 9
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{\pkgname{}\_edit Return Values}
Function returns:
\begin{description}
\item[\texttt{default}:]
A csv count table is created and written to user specified file.
The .csv file contains two columns, the first column is the sequences of the
barcodes, and the second columns is the number
of reads that aligned to the barcodes. A list containing a vector of read IDs
and barcode IDs is returned to R.
\end{description}
Extra optional returns:\\
\begin{description}
\item[\texttt{count\_only = FALSE}:]
If set to \texttt{FALSE}, \pkgname{} will return a sparse matrix in the list
containing the vectors of read IDs and barcode IDs. The rows of the matrix
correspond to the vector of read IDs, and the columns correspond to the
vector of barcode IDs.
\end{description}
\end{frame}

\section{Examples}
\subsection{Example Data}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% example dummy data page 10
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{Example Library and Read simulation}
\scriptsize
Generating a library fasta file:
<<genlib, eval=TRUE>>=
lFName    <- "./libFile.fasta"
bases     <- c(rep('A', 4), rep('C',4), rep('G',4), rep('T',4))
numOfBars <- 7
Barcodes  <- rep(NA, numOfBars*2)
for (i in 1:numOfBars){
    Barcodes[2*i-1] <- paste0(">barcode_ID: ", i)
    Barcodes[2*i]   <- paste(sample(bases, length(bases)), collapse = '')
}
write(Barcodes, lFName)
@
Generating a read fastq file:
\scriptsize
<<genRead, eval=TRUE>>=
rFName     <- "./readFile.fastq"
numOfReads <- 8
Reads      <- rep(NA, numOfReads*4)
for (i in 1:numOfReads){
    Reads[4*i-3] <- paste0("@read_ID_",i)
    Reads[4*i-2] <- Barcodes[2*sample(1:numOfBars,1,
        replace=TRUE, prob=seq(1:numOfBars))]
    Reads[4*i-1] <- "+"
    Reads[4*i]   <- paste(rawToChar(as.raw(
        33+sample(20:30, length(bases),replace=TRUE))),
        collapse='')
}
write(Reads, rFName)
@
\end{frame}

\subsection{Example of Using \pkgname{}\_hamming}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% example of using bcSeq_hamming page 11
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{Alignment by \pkgname{}\_hamming}
\scriptsize
Alignment using default mapping probability and output
<<defaultAlign, eval=TRUE>>=
library(Matrix)
library(bcSeq)
ReadFile <- "./readFile.fastq"
BarFile  <- "./libFile.fasta"
outFile  <- "./count.csv"
@
<<defaultAlign_test, eval=FALSE>>=
res <- bcSeq_hamming(ReadFile, BarFile, outFile, misMatch = 2,
    tMat = NULL, numThread = 4, count_only = TRUE )
res <- read.csv(outFile, header=FALSE)
@
The function writes the read counts to a .csv file, the file name
can be set through argument {\tt outFile}.
\scriptsize
There are two columns in the output .csv file, the first column is the
barcode sequence, and the second column is the corresponding number of
reads aligned to the barcode.
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% example of using bcSeq_hamming with options page 12
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{Alignment using \pkgname{}\_hamming with Optional
Return}
\scriptsize
The package also provides an option for the user to output more detailed
information for the alignments. The current version can return the
alignment probability between the reads and the barcodes by setting argument
{\tt count\_only} to "FALSE".
<<custAlign,eval=FALSE>>=
outFile  <- "./count2.csv"
bcSeq_hamming(ReadFile, BarFile, outFile, misMatch = 2, tMat = NULL,
    numThread = 4,count_only=FALSE )
@
%<<clear,echo=FALSE>>=
%system2("rm", "*csv")
%@
\end{frame}

\subsection{Example of Using \pkgname{}\_edit}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% example of using bcSeq_edit page 13
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{Alignment by \pkgname{}\_edit}
\scriptsize
Alignment using default mapping probability and output format
<<defaultAlign2, eval=FALSE>>=
res <- bcSeq_edit(ReadFile, BarFile, outFile, misMatch = 2,
    tMat = NULL, numThread = 4, count_only = TRUE,
    gap_left = 2, ext_left = 1, gap_right = 2, ext_right = 1,
    pen_max = 7)
res <- read.csv(outFile, header=FALSE)
res[1:3,]
@
The function writes the read counts to a .csv file, the file name
can be set through argument {\tt outFile}.
\scriptsize
There are two columns in the output .csv file, the first column is the
barcode sequence, and the second column is the corresponding number of
reads aligned to the barcode.
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% example of using bcSeq_edit with options page 14
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{Alignment using \pkgname{}\_edit with
Optional Return}
\scriptsize
The package also provides an option for the user to output more detailed
information for the alignments. The current version can return the
alignment probability between the reads and the barcodes by setting argument
{\tt count\_only} to "FALSE".
%<<echo=FALSE>>=
%system2("rm","*csv")
%@
<<custAlign2>>=
outFile  <- "./count2.csv"
@
<<custAlign2_ex, eval=FALSE>>=
bcSeq_edit(ReadFile, BarFile, outFile, misMatch = 2, tMat = NULL,
    numThread = 4, count_only = FALSE, gap_left = 2, ext_left = 1,
    gap_right = 2, ext_right = 1, pen_max = 5)
@
%<<clear2,echo=FALSE>>=
%system2("rm","*fastq *fasta *csv")
%@
\end{frame}

\section{General Alignment Probability Model}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% alignment probability model page 15
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{General Alignment Probability Model in \pkgname{}}
Let $\Pr{\rtvec^k|\rvec}:=\Pr{\Rt=\rtvec^k|\R=\rvec}$ denote the probability
that the read originated from barcode $k$, with the corresponding sequence
$\rtvec^k$, given that the sequence of the observed read is
$\rvec=[\r_1,\ldots,\r_L]$. \pkg{} models the joint probability distribution of
the originating reads conditional on the corresponding observed reads,
$\Pr{\rtvec^k|\rvec}$, under the assumption of conditional independence, as
\begin{align*}
\Pr{\rtvec^k|\rvec} & = \Pr{\Rt=\rtvec^k|\R=\rvec} \\
    & = \Pr{\Rt=[\jtoL{\rt}{L}]|\R=[\jtoL{\r}{L}]} \\
    & = \prod_{i=1}^L \Pr{\Rt_j=\rt_j|\R=[\jtoL{\r}{L}]} \\
    & = \prod_{i=1}^L \Pr{\Rt_j=\rt_j|\R_j=\r_j}. \\
\end{align*}
The marginal conditional probability is modeled as
\begin{equation*}
    \Pr{\Rt_j=\rt_j|\R_j=\r_j} =
    \begin{cases}
        1-\epsilon_j,&\rt_j=\r_j\\
        \frac{\epsilon_j}{3}&\rt_j\ne\r_j,
    \end{cases}
\end{equation*}
where $\epsilon_j= 10^{-q_j/10}$ is the base-calling error probability
corresponding to the observed Phred score $q_j$.
\end{frame}

\section{User-defined Alignment Probability Model}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% user defined probability model page 16
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{User defined Alignment Probability}
\pkgname{} provides two approaches for the user to defined their own
alignment probability
\begin{itemize}
    \item User-defined Marginal Conditional Probability
    \item User-defined $\Pr{\Rt_j=\rt_j|\R_j=\r_j}$
\end{itemize}
\end{frame}
\subsection{User-defined Prior Probability}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% user defined probability model page 17
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{User-defined Marginal Conditional Probability}
Redefine marginal conditional probability:
\begin{align*}
    &\Pr{\Rt_j=\rt_j|\R_j=\r_j} \\
    \equiv&\Pr{\Rt_j=\rt_j|\R_j=[\r_{j-n},\ldots,\r_j]} \\
    =&
    \begin{cases}
        1-\epsilon_j,&\rt_j=\r_j\\
        f(\epsilon_j)_{\r_{j-n},\ldots,\r_j} &\rt_j\ne\r_j,
    \end{cases}
\end{align*}
This definition assumes that sequences before position $j$ has influence on the
error rate. The option can be activated by providing a dataframe to the
argument \texttt{tMat}. The first column of this dataframe is a vector of
sequences $[\r_{j-n},\ldots,\r_j]$ and the second column is the corresponding
value of $f(\epsilon_j)_{[\r_{j-n},\ldots,\r_j]}$.  If a sequence is not in the
dataframe the default values $\frac{\epsilon_j}{3}$ are used.
\end{frame}

\subsection{User-defined $\Pr{\Rt_j=\rt_j|\R_j=\r_j}$} \label{userP}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% user defined probability model page 18
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{User-defined $\Pr{\Rt_j=\rt_j|\R_j=\r_j}$}
\small
Customized $\Pr{\Rt_j=\rt_j|\R_j=\r_j}$ are also supported by \pkgname{} for
bcSeq\_edit. The user can define a function \texttt{userProb} to compute the
alignment score. \texttt{userProb} is a vectorized R function taking three
arguments: \texttt{userProb(max\_pen, prob, pen\_val)}, \texttt{max\_pen} is
the maximum penalty allowed, \texttt{prob} is a vector containing the combined
match/misMatch probabilities for each unique alignment between a given read
and barcode, \texttt{pen\_val} is a vector containing total penalties for the
reads and barcodes in the same order. \texttt{userProb} is a way for the user
to control which alignments are considered.
\scriptsize
<<comtomizePF>>=
customizeP <- function(max_pen, prob, pen_val)
{
    prob * (1 - log(2) + log(1 + max_pen / (max_pen + pen_val) ) )
}
@
<<comtomizeP,eval=FALSE>>=
bcSeq_edit(sampleFile, libFile, outFile, misMatch = 2, tMat = NULL,
    numThread = 4, count_only = TRUE, userProb = comstomizeP,
    gap_left = 2, ext_left = 1, gap_right = 2,
    ext_right = 1, pen_max = 5)
@
\small
Note: the using of user-defined $\Pr{\Rt_j=\rt_j|\R_j=\r_j}$ can add extra
computation time. As defined here, \texttt{customizeP} is equivalent to
the default model.
\end{frame}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% user defined probability model page 18
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]
\small
The customized \texttt{userProb} function can also be defined using sounceCpp
in a non-vectorized fashion.  The function signature is
Rcpp::NumericVector(double, Rcpp::NumericVector, Rcpp::NumericVector).
<<comtomizeP2F>>=
library(Rcpp)
sourceCpp(code='
#include<Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
NumericVector cpp_fun(double m, NumericVector prob, NumericVector pen){
    NumericVector ret;
    for(int i = 0; i < prob.size(); ++i){
        ret.push_back(prob[i] * (1 - log(2) + log(1 + pen[i]/(m + pen[i]))));
    }
    return ret;
}')
@
<<comtomizeP2,eval=FALSE>>=
bcSeq_edit(sampleFile, libFile, outFile, misMatch = 2, tMat = NULL,
    numThread = 4, count_only = TRUE, userProb = cpp_fun,
    gap_left = 2, ext_left = 1, gap_right = 2,
    ext_right = 1, pen_max = 5)
@
\end{frame}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% run MAGeCK data page 20
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Practical Example}
\begin{frame}[fragile]{Practical Alignment Example}
The sequencing library used in the benchmarking analysis can be
downloaded from the MAGeCK website:\\
\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR376/ERR376998/ERR376998.fastq.gz}\\
The reference barcode library can also be downloaded from Kosuke Yusa
laboratory:\\
\url{https://www.nature.com/nbt/journal/v32/n3/extref/nbt.2800-S7.xlsx}\\
\begin{verbatim}
get read and library file
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR376/ERR376998/ERR376998.fastq.gz
-P ./src/
wget https://www.nature.com/nbt/journal/v32/n3/extref/nbt.2800-S7.xlsx
-P ./src
\end{verbatim}
Process library data to fasta format
<<eval=FALSE>>=
library(gdata)
x <- read.xls("./nbt.2800-S7.xlsx")
fName <- "./libgRNA.fasta"
size <- nrow(x)
for(i in 1:size){
    cat(">seq ID","\n",file=fName, append=TRUE)
    cat(as.character(x$gRNA.sequence[i]),"\n", file=fName, append=TRUE)}
fName <- "./libgRNA.csv"
size <- nrow(x)
for(i in 1:size){
    cat(as.character(x$gRNA.sequence[i]),"\n", file=fName, append=TRUE)}
@
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% run MAGeCK data page 21
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[fragile]{Practical Alignment Example}
Unzip the reads and trim the adaptor
\begin{verbatim}
gunzip ERR376998.fastq.gz
\end{verbatim}
Ad hoc C++ function for trimming adaptor (\texttt{trimMAGeCK.cpp})
\scriptsize
\begin{lstlisting}
#include <iostream>
#include <fstream>
using namespace std;
int main(int argc, char *argv[]){
    int length = 0;
    string line;
    ifstream myfile (argv[1]);
    ofstream outfile (argv[2]);
    if (myfile.is_open()){
        while ( getline (myfile,line) ){
            outfile << line <<endl;
            getline (myfile,line);
            length = line.length() - 31;
            outfile << line.substr(23, length) <<endl;
            getline (myfile,line);
            outfile << line <<endl;
            getline (myfile,line);
            outfile << line.substr(23, length) <<endl;}
        myfile.close();}
    return 0;
}
\end{lstlisting}
\end{frame}

\begin{frame}[fragile]{Practical Alignment Example}
Ad hoc C++ function to remove duplicated barcodes (\texttt{uniqueBar.cpp})
\scriptsize
\begin{lstlisting}
#include <iostream>
#include <fstream>
#include <unordered_set>
using namespace std;
int main(int argc, char *argv[]){
    int length = 0; string line;
    std::unordered_set<std::string> myset;
    ifstream myfile (argv[1]); ofstream outfile (argv[2]);
    if (myfile.is_open()){
        while ( getline (myfile,line) )
            getline (myfile,line); myset.insert(line);
        myfile.close();}
    for(const std::string& x: myset)
        outfile << ">fake ID "<<x<< endl<<x<<endl;
    return 0;
}
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]{Practical Alignment Example}
Compile trim code and trim adoptor
\begin{verbatim}
g++ -std=c++11 trimMAGeCK.cpp -o trim.exe
./trim.exe ERR376998.fastq ERR376998_trimed.fastq
\end{verbatim}
Remove duplicated barcodes
\begin{verbatim}
g++ -std=c++11 uniqueBar.cpp -o uniBar.exe
./uniBar.exe ./libgRNA.fasta ./libgRNAUni.fasta
mv libgRNAUni.fasta libgRNA.fasta
\end{verbatim}
Perform alignment
<<eval=FALSE>>=
library(bcSeq)
readFileName  <- "ERR376998_trimed.fastq"
libFileName   <- "libgRNA.fasta"
alignedFile   <- "SampleAligned.txt"
bcSeq_hamming(readFileName, libFileName, alignedFile, misMatch = 2,
    tMat = NULL, numThread = 4, count_only = TRUE)
@
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% section information page 21
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Session Information}
\begin{frame}[fragile]{Session Information}
<<sessinfo, echo=FALSE, include=TRUE, results='asis'>>=
toLatex(sessionInfo(), locale=FALSE)
@
<<times, echo=FALSE, include=TRUE>>=
print(paste("Start Time",stdt))
print(paste("End Time  ",date()))
@
\end{frame}
\end{document}
