%\VignetteIndexEntry{Using the BioSeqClass Package}
%\VignetteKeywords{Classification}
%\VignetteDepends{BioSeqClass}
%\VignettePackage{BioSeqClass}

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\author{Hong Li$^\ddagger$\footnote{sysptm@gmail.com}}
\begin{document}
\title{Using the BioSeqClass Package}
\maketitle
\begin{center}$^\ddagger$Key Lab of Systems Biology\\ 
Shanghai Institutes for Biological Sciences\\ 
Chinese Academy of Sciences, P. R. China
\end{center}

\tableofcontents

\section{Overview} 
There are now 863 completely sequenced genomes of cellular organisms in NCBI genome 
database. Nevertheless, functional annotation drops far behind sequencing because 
functional valida-tion experiments are time-consuming and costly. Taken model organism 
Homo sapiens, Mus musculus and Saccharomyces cere-visiae as examples, only 16%, 2% 
and 18% genes have experimen-tally determined functions (Traceable Author Source (TAS) 
anno-tations in Gene Ontology), respectively. Thus 
computational methods for predicting function is still a fun-damental complement. 
The most common com-putation approach is biological sequence 
based classification, since sequence information is still the most abundant and 
reliable. Se-quence based classification has been used in: discovering new microRNA 
candidates, predicting transcription factor binding sites 
, detecting protein post-translational modification sites 
, and so on.

Features and models are two basic factors for classification. Features generally are 
numerical values that can be used to distinguish different classes. Therefore it is 
preferable to select features that can achieve better and faster classification. 
Classification models are built from features by various algorithms, and it is 
necessary to evaluate its prediction ability by cross validation or jackknife test. 
For biological sequences, there are additional steps: one is to reduce homolog 
sequences which might result in overestimation of prediction accuracy, and then 
another most important step is to convert sequences into numerical features. 
Thus, the general workflow for sequence-based classifications includes 
(Figure \ref{fig:workflow3}): reduce homolog sequences; extract features from 
sequences and code them to numerical values; evaluate and select features; build 
classification model and evaluate its performance. 

Here we present an R package (BioSeqClass) to carry out the general workflow for 
biological sequence based classification. It contains diverse fearure coding schemas 
for RNA, DNA and proteins, supports feature seletion, and integrates multiple 
classification methods.

\begin{figure}[htbp]
\begin{center}
\includegraphics{workflow.pdf}
\caption{\label{fig:workflow3}Workflow for Biological Sequence based Classification.}
\end{center}
\end{figure}

\section{Installation}

\subsection{Requirements}
BioSeqClass employs some external programs to extract biological 
properties and use other R packages to build classification model:

\begin{enumerate}
\item BioSeqClass imported R packages are listed in table~\ref{table:package}.
These packages will be automatically installed when Biocalss is firstly loaded.
\item External programs are used to assist the performance of BioSeqClass (see 
table~\ref{table:program}). Some programs are 
invoked via their web service, and some ones are needed to be installed at 
the local computer.

Note: 
You do not need to install programs listed in table~\ref{table:program}, unless
you will use the related function in BioSeqClass.
\end{enumerate}

\subsection{Installation}
The biocLite script is used to install PAnnBuilder from within R:
<<install, eval=FALSE>>=
source("http://bioconductor.org/biocLite.R")
biocLite("BioSeqClass")
@
Users also can use the installation script "BioSeqClass.R" to 
download and install BioSeqClass package.
<<install2, eval=FALSE>>=
source("http://www.biosino.org/download/BioSeqClass/BioSeqClass.R")
BioSeqClass()
@
Load package:
<<load>>=
library(BioSeqClass)
@
Note: Web Connection is needed to install BioSeqClass and its required packages.
All the codes in this vignette were tested in R 2.8.0 and 2.9.0, thus the latest R 
version is recommended.

\section{Function description}

\subsection{Homolog Reduction}
Homologous sequences in training/testing data may lead to overestimation of 
prediction accuracy. Therefore, the first step for sequence based classification 
and prediction is homolog reduction based on sequence similarity. Taking 
computation complexity and similarity restriction into consideration, homolog 
reductions for full-length sequences 
and fragment sequences are different. We have designed different functions to deal with 
them, respectively (see table~\ref{table:hr}).

\begin{itemize}
\item \Rfunction{hr} - It employs \Rfunction{cdhitHR} and \Rfunction{aligndisHR} 
  to filter homolog sequences by sequence similarity. \Rfunction{cdhitHR} is 
  designed to filter full-length protein or gene sequences.
  \Rfunction{aligndisHR} is designed for aligned sequences with equal length.
\item \Rfunction{cdhitHR} - It uses cd-hit program to do homolog reduction 
  ("formatdb" and "blastall" are required for running cd-hit program). 
  CD-HIT is a program for clustering large protein database at high sequence 
  identity threshold \citep{2}.  
\item \Rfunction{aligndisHR} - It uses the number of different residues to do 
  homolog reduction \citep{3}. The algorithm proceeds in a stepwise manner by first 
  eliminating sequences that were different from another in exactly 1 position. 
  Elimination proceeds one peptide at a time; Re-evaluate after each peptide 
  is removed. Once no further homologs of distance 1 remain, homologs of distance 
  2 are eliminated, and so forth until identity between all peptides are less 
  than given cutoff.
\end{itemize}

\subsection{Feature Extraction and Numerical Coding}
\subsubsection{Biological sequences}
RNA, DNA and protein are three kinds of basic biological sequences. RNA and DNA
are composed of bases, while proteins are composed of amino acids. Furthermore,
amino acids have different physical-chemical properties, which were used to 
divide amino acids into different groups. These elements and groups are basic
objects for feature extraction (see table~\ref{table:ele}).

\subsubsection{Feature Coding}
Feature coding means to extract features from sequences and convert them into numerical 
values. The frequently used features are the basic elements of sequence (bases, amino acids), 
physical-chemical properties, secondary structures, and so on. There are many 
methods to convert features to numerical values. The simplest is the 
composition of element. But more sophisticated conversions are preferrable 
for achieving better distinguishing power. Previous studies have shown that 
feature coding is the key point for the accuracy of classification and 
prediction. Here we have summarized various feature coding methods
used in published papers and carried out them in BioSeqClass (see table~\ref{table:feature}).
These functions will allow more diverse choices of coding strategies and accelerate the feature 
coding process. We also provide a function \Rfunction{featureEvaluate} to test the 
performance of models with different feature coding schemes and different 
classification algorithms. 
                                  
\subsection{Feature Selection}      
Features are important for the accuracy of prediction model. However, it does not mean that 
the more the better. Computation time is usually increased with the increase of number of 
features. Conflictive features would even reduce the accuracy. Therefore, suitable 
feature selection is needed for better prediction performance and less computation cost. 
We provided two functions for feature selection (see table~\ref{table:fs}).
                                  
\subsection{Model Building and Performance Evaluation}    
Besides features, classification method is another factor that influences 
classification. Different cases may have different perference over classification methods.
Multiple classification methods are integrated and available in BioSeqClass (see 
table~\ref{table:model}). To evaluate and compare classification models, performance 
assessment is done for each model, including precision, sensitivity, specificity, 
accuracy, and matthews correlation coefficient.
      
\begin{table}
\caption{\label{table:package}Imported R packages.}
\begin{center}
\begin{tabular}{|l|l|}
\hline
\bf{Existing R Package} & \bf{Functions used by BioSeqClass}\\ \hline
\Rpackage{Biostrings} & readAAStringSet, writeXStringSet \\ \hline
\Rpackage{e1071} & svm \\ \hline
\Rpackage{ipred} & bagging \\ \hline
\Rpackage{klaR} & svmlight, NaiveBayes \\ \hline
\Rpackage{randomForest} & randomForest \\ \hline
\Rpackage{class} & knn \\ \hline
\Rpackage{tree} & tree \\ \hline
\Rpackage{nnet} & nnet \\ \hline
\Rpackage{rpart} & rpart \\ \hline
\Rpackage{party} & ctree \\ \hline
\Rpackage{foreign} & write.arff \\ \hline
\Rpackage{Biobase} & addVigs2WinMenu \\ \hline
\end{tabular}
\end{center}
\end{table}

\newpage 
\begin{table}
\footnotesize
\caption{\label{table:program}Invoked External Programs.}
\begin{center}
\begin{tabular}{|p{0.1\textwidth}|p{0.3\textwidth}|p{0.23\textwidth}|p{0.1\textwidth}|p{0.3\textwidth}|}
\hline
\bf{External Program} & \bf{Description} & \bf{Related BioSeqClass Function} & \bf{Need Installed?} & \bf{Ref} \\ \hline
cd-hit & a program for clustering large protein database at high sequence identity threshold & \Rfunction{cdhitHR} & Yes  & \citep{2}\\ \hline
blastpgp & PSI-BLAST (Position-Specific Iterated BLAST) for capturing the conservation pattern & \Rfunction{featurePSSM} & Yes  & \citep{21}\\ \hline
SVMlight & support vector machine & \Rfunction{classifyModelSVMLIGHT} & No  & \citep{22}\\ \hline
DSSP & a database of secondary structure assignments for protein entries in the Protein Data Bank (PDB) & \Rfunction{getDSSP} & No & \citep{24}\\ \hline
Proteus2 & predict secondary structure & \Rfunction{predictPROTEUS} & No  & \citep{23}\\ \hline
HMMER & predict domains with hmmpfam using models of Pfam database & \Rfunction{predictPFAM} & No & \citep{27}\\ \hline
\end{tabular}
\end{center}
\end{table}

\begin{table}
\caption{\label{table:hr}Summary Table for Homolog Reduction Functions.}
\begin{center}
\begin{tabular}{|l|p{0.65\textwidth}|l|}
\hline
\bf{Function} & \bf{Description} & \bf{Ref}\\ \hline
\Rfunction{hr} & employ \Rfunction{cdhitHR} and \Rfunction{aligndisHR} to do homolog reduction & \\ \hline
\Rfunction{cdhitHR} & invoke cd-hit to cluster sequences & \citep{2} \\ \hline
\Rfunction{aligndisHR} & calculated identity of aligned sequences & \citep{3} \\ \hline
\end{tabular}
\end{center}
\end{table}

\begin{table}
\caption{\label{table:ele}Summary Table for Base and Amino Acid Groups.}
\begin{center}
\begin{tabular}{|l|p{0.7\textwidth}|l|}
\hline
\bf{Function} & \bf{Description} & \bf{Ref}\\ \hline
\Rfunction{elements} & basic elements of biological sequence & \\ \hline
\Rfunction{aaClass} & amino acids groups depend on their physical-chemical
properties: hydrophobicity, normalized Van der Waals volume, polarizability, 
polarity, and so on & \citep{4} \\ \hline
\end{tabular}
\end{center}
\end{table}

\newpage 
\begin{table}
\footnotesize
\caption{\label{table:feature}Summary table for Feature Coding Functions.}
\begin{center}
\begin{tabular}{|l|l|p{0.45\textwidth}|p{0.2\textwidth}|}
\hline
\bf{Type} & \bf{Function} & \bf{Feature Coding Scheme} & \bf{Ref}\\ \hline
\multirow{5}{0.1\textwidth}{DNA, RNA, or protein}  
 & \Rfunction{featureBinary} & use 0-1 vector to code each element & \citep{39,40} \\
 & \Rfunction{featureCTD} & numeric vector for the composition, transition and distribution of 
properties & \citep{9} \\
 & \Rfunction{featureFragmentComposition} & numeric vector for the frequency of k-mer sequence 
fragment & \citep{5,6} \\
 & \Rfunction{featureGapPairComposition} & numeric vector for the frequency of g-spaced element 
pair & \citep{4}  \\
 & \Rfunction{featureCKSAAP} & integer vector for the number of k-spaced element pair (k cycled 
from 0 to g) & \citep{8} \\ \hline               
\multirow{8}{0.1\textwidth}{protein}
 & \Rfunction{featureHydro} & hydrophobic effect & \citep{10,11} \\ 
 & \Rfunction{featureACH} & average cumulative hydrophobicity over a sliding window & \citep{31,32} \\ 
 & \Rfunction{featureAAindex} & numeric vector measuring the physicochemical and biochemical properties based on AAindex database & \citep{12,13} \\
 & \Rfunction{featureACI} & numeric vector measuring the average cumulative properties in AAindex & \\
 & \Rfunction{featureACF} & numeric vector measuring the Auto Correlation Function (ACF) of properties in AAindex & \citep{14,15} \\            
 & \Rfunction{featurePseudoAAComp} & numeric vector for the pseudo amino acid composition proposed by Chou,K.C. & \citep{16}  \\
 & \Rfunction{featurePSSM} & numeric vector for the normalized position-specific score of PSSM generated by PSI-BLAST & \citep{17,18,19,31}  \\
 & \Rfunction{featureDOMAIN} & vector for the number of domain. Domains can be obtained by 'predictPFAM' function. & \citep{28}  \\ 
 & \Rfunction{featureSSC} & coding for secondary structure of protein. Secondary structure can be got by 'predictPROTEUS' or 'getDSSP'.& \citep{20} \\ \hline
\multirow{3}{0.1\textwidth}{DNA or RNA}  
 & \Rfunction{featureBDNAVIDEO} & Conformational and physicochemical DNA features from B-DNA-VIDEO database & \citep{33} \\ 
 & \Rfunction{featureDIPRODB} & conformational and thermodynamic dinucleotide properties from DiProDB database (http://diprodb.fli-leibniz.de)& \citep{34} \\ \hline
\end{tabular}                     
\end{center}                  
\end{table}

\newpage 
\begin{table}
\caption{\label{table:fs}Summary Table for Feature Selection Functions.}
\begin{center}
\begin{tabular}{|l|p{0.6\textwidth}|l|}
\hline
\bf{Function} & \bf{Description} & \bf{Ref} \\ \hline
\Rfunction{selectWeka} & feature selction by methods in WEKA & \citep{36} \\ \hline
\Rfunction{selectFFS} & feature forword selction based on the performance of classification model & \citep{13} \\ \hline
\Rfunction{classify} & build and test model with cross validation, also support feature selection by envoking WEKA & \\ \hline
\end{tabular}                     
\end{center}                  
\end{table}       
             
\begin{table}
\caption{\label{table:model}Summary Table for Classification Methods.}
\begin{center}
\begin{tabular}{|l|p{0.49\textwidth}|l|}
\hline
\bf{Function} & \bf{Description} & \bf{Depended R package}\\ \hline
\Rfunction{classifyModelLIBSVM} & support vector machine by LIBSVM & \Rpackage{e1071}\\ \hline
\Rfunction{classifyModelSVMLIGHT} & support vector machine by SVM-light & \Rpackage{klaR}\\ \hline
\Rfunction{classifyModelNB} & naive bayes & \Rpackage{klaR}\\ \hline
\Rfunction{classifyModelRF} & random forest & \Rpackage{randomForest}\\ \hline
\Rfunction{classifyModelKNN} & k-nearest neighbor & \Rpackage{class}\\ \hline
\Rfunction{classifyModelTree} & tree model & \Rpackage{tree}\\ \hline
\Rfunction{classifyModelNNET} & neural net algorithm & \Rpackage{VR}\\ \hline
\Rfunction{classifyModelRPART} & recursive partitioning trees & \Rpackage{rpart}\\ \hline
\Rfunction{classifyModelCTREE} & conditional inference trees & \Rpackage{party}\\ \hline
\Rfunction{classifyModelCTREELIBSVM} & combine conditional inference trees and support vector machine& \Rpackage{party}, \Rpackage{e1071}\\ \hline
\Rfunction{classifyModelBAG} & bagging method & \Rpackage{ipred}\\ \hline
\end{tabular}                     
\end{center}                  
\end{table}     
                                 
\newpage
\section{Examples}                 
To illustrate the use of BioSeqClass, lysine acetylation site prediction is taken as 
an example.
\begin{enumerate}
\item Suppose the original data are protein FASTA sequences and lysine acetylation sites. 
You can use \Rfunction{getTrain} to extract the flanking peptides of acetylation sites 
as positive dataset, and filter these peptides based on sequence identity. Lysine 
without acetylation annotation are regarded as negative dataset, and are filtered
like the positive dataset. Considering the computational time, only 20 positive data 
are used as examples in the following codes.
<<homologReduction>>=
  library(BioSeqClass)
  # Example data in BioSeqClass.
  file=file.path(path.package("BioSeqClass"),"example","acetylation_K.fasta")
  posfile = file.path(path.package("BioSeqClass"), 
       "example", "acetylation_K.site")
  # Only a part of lysine acetylation sites are used for demo.
  posfile1=tempfile()
  write.table(read.table(posfile,sep='\t',header=F)[1:20,], posfile1, 
    sep='\t', quote=F, row.names=F, col.names=F)
  seqList = getTrain(file, posfile1, aa="K", w=7, identity=0.4)
@

\item If the original data are non-redundant positive/negative peptides. We directly 
read the data into R and assign class labels for them.
<<readData>>=
  tmpDir=file.path(path.package('BioSeqClass'), 'example')
  tmpFile1=file.path(tmpDir, 'acetylation_K.pos40.pep')
  tmpFile2=file.path(tmpDir, 'acetylation_K.neg40.pep')
  posSeq=as.matrix(read.csv(tmpFile1,header=F,sep='\t',row.names=1))[,1]
  negSeq=as.matrix(read.csv(tmpFile2,header=F,sep='\t',row.names=1))[,1]
  seq=c(posSeq,negSeq)  
  classLable=c(rep("+1",length(posSeq)),rep("-1",length(negSeq)) ) 
  length(seq)
@  

\item Once you have positive/negative datasets, you can code them to numeric vectors 
by functions listed in table~\ref{table:feature}. Function \Rfunction{featureBinary} 
and \Rfunction{featureGapPairComposition} are taken as examples of different coding 
methods, which use binary 0-1 coding and the composition of gapped amino acid pair, respectively.
Other functions can be used in the same way.
<<featureSelect>>=
  # Use 0-1 binary coding.
  feature1 = featureBinary(seq,elements("aminoacid"))
  dim(feature1)
  # Use the compostion of paired amino acids.
  feature2 = featureGapPairComposition(seq,0,elements("aminoacid"))
  dim(feature2)
@

\item \Rfunction{classify} is used to build classification model under cross 
validation. It also supports feature selection by invoking WEKA. Models built with 
selected features usually can obtain higher accuracy. In the following codes, two 
models are built by \Rfunction{classify}. The 1st
classification model 'LIBSVM\_CV5' is built by support vector machine with linear kernel 
and get an accuracy of 0.56 under 5-fold cross validation. The 2nd classification model 
'FS\_LIBSVM\_CV5' is also built by support vector machine with linear kernel, but a 
feature selection method called "CfsSubsetEval" is used before building model. Thus the 2nd
model using feature selection achieves an higher accuracy of 0.62 than the 1st model using 
all features.
<<classModel>>=  
  data = data.frame(feature1,classLable)
  # Use support vector machine and 5 fold cross validation to do classification.
  LIBSVM_CV5 = classify(data, classifyMethod='libsvm',
               cv=5, svm.kernel='linear',svm.scale=F)
  LIBSVM_CV5[["totalPerformance"]]
  # Features selection is done by envoking "CfsSubsetEval" method in WEKA.
  FS_LIBSVM_CV5 = classify(data, classifyMethod='libsvm',
                  cv=5, evaluator='CfsSubsetEval', search='BestFirst',
                  svm.kernel='linear', svm.scale=F)
  
  FS_LIBSVM_CV5[["totalPerformance"]] ## Accuracy is increased by feature selection.
  # Selected features:
  colnames(data)[FS_LIBSVM_CV5$features[[1]]]
@

\item Different feature coding methods usually might result in different prediction 
performance. \Rfunction{featureEvaluate} can be used to test multiple feature 
coding methods. Figure \ref{fig:FeatureSets16} shows the 3D plot of prediction 
accuracy varied with feature coding functions and parameters. It can be generated
by employing \Rfunction{featureEvaluate} as follows (Note: It may be time consuming!):
<<featureTest, eval=FALSE>>=
  fileName = tempfile()
  # Note: It may be time consuming.
  testFeatureSet = featureEvaluate(seq, classLable, fileName, cv=5, 
    ele.type='aminoacid', featureMethod=c('Binary','GapPairComposition'),
    classifyMethod='libsvm',
    group=c('aaH', 'aaV', 'aaZ', 'aaP', 'aaF', 'aaS', 'aaE'), g=0,
    hydro.methods=c('kpm', 'SARAH1'), hydro.indexs=c('hydroE', 'hydroF', 'hydroC') )
  summary = read.csv(fileName,sep="\t",header=T)      
  # Plot the result of 'featureEvaluate'
  require("scatterplot3d")     
  tmp1 = summary[,"Feature_Function"]
  tmp2 = as.factor(sapply(as.vector(summary[,'Feature_Parameter']),
         function(x){unlist(strsplit(x,split='; '))[1]}))
  testResult = data.frame(as.integer(tmp2), as.integer(tmp1), summary[,"acc"])
  s3d=scatterplot3d(testResult,
      color=c('red','blue')[testResult[,2]], pch=19, xlab='Parameter',
      ylab='Feature Coding',
      zlab='Accuracy', lab=c(9,3,7),       
      x.ticklabs=gsub('class: ','',sort(unique(tmp2))), 
      type='h',ylim=c(0,3),y.margin.add=2.5,
      y.ticklabs=c('',gsub('feature','',sort(unique(tmp1))),'') )
@

\item Features from multiple functions can be combined and re-selected to increase the 
prediction accuracy. In the following code chunk, the first three feature sets 
from 'testFeatureSet' are combined together ('testFeatureSet' is generated in the 
aforementioned codes by \Rfunction{featureEvaluate}). Then feature selection functions
(\Rfunction{classify} and \Rfunction{selectFFS}) can be employed to 
selecte features. (\Rfunction{classify} has been illustrated in the aforementioned 
code chunk. Thus \Rfunction{selectFFS} is used here to do feature forward selection 
to select a subset with maximum prediction accuracy (Note: It may be time consuming!). 
The process of feature selection and the increasing 
accuracy are shown in Figure \ref{fig:cvFFSClassify0005}.
<<featureComSelect, eval=FALSE>>=
  feature.index = 1:3
  tmp <- testFeatureSet[[1]]$data
  tmp1 <- testFeatureSet[[feature.index[1]]]$model
  colnames(tmp) <- paste(
    tmp1["Feature_Function"],
    tmp1["Feature_Parameter"],
    colnames(tmp),sep=" ; ")      
  data <- tmp[,-ncol(tmp)]  
  for(i in 2:length(feature.index) ){
    tmp <- testFeatureSet[[feature.index[i]]]$data
    tmp1 <- testFeatureSet[[feature.index[i]]]$model
    colnames(tmp) <- paste(
      tmp1["Feature_Function"],
      tmp1["Feature_Parameter"],
      colnames(tmp),sep=" ; ")    
    data <- data.frame(data, tmp[,-ncol(tmp)] )
  }
  name <- colnames(data)
  data <- data.frame(data, tmp[,ncol(tmp)] )  ## Combined features
  # Use 'selectFFS' to do feature forward selection.
  # Note: It may be time consuming.
  combineFeatureResult = selectFFS(data,accCutoff=0.005,
    classifyMethod="knn",cv=5)  ## It is time consuming.
  tmp = sapply(combineFeatureResult,function(x){
    c(length(x$features),x$performance["acc"])})
  plot(tmp[1,],tmp[2,],xlab="Feature Number",ylab="Accuracy",
    , pch=19)
  lines(tmp[1,],tmp[2,])
@
\end{enumerate}

\newpage
\begin{figure}[htbp]
\begin{center}
\includegraphics{FeatureSets16.pdf}
\caption{\label{fig:FeatureSets16}Result of \Rfunction{featureEvaluate}.}
\end{center}
\end{figure}

\begin{figure}[htbp]
\begin{center}
\includegraphics{cvFFSClassify0005.pdf}
\caption{\label{fig:cvFFSClassify0005}Result of \Rfunction{selectFFS}.}
\end{center}
\end{figure}

\section{Session Information}

This vignette was generated using the following package versions:
<<echo=FALSE>>=
sessionInfo()
@

\bibliographystyle{plainnat}
\bibliography{BioSeqClass}

\end{document}
